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Indian Behind-the-Meter (BTM) stationary storage market is projected to grow from 32 GWh of annual demand in 2025 to over 39 GWh by 2033, according to a latest report from the India Energy Storage Alliance and Customised Energy Solutions (CES).
The leading industry body, IESA, highlights these findings as a sign that India is entering a new era of energy resilience and technological leadership. BTM systems, which include onsite energy generation and storage located on the customer’s side of the utility meter, enable the direct use of generated or stored energy without passing through the grid. These systems include rooftop solar panels, battery storage, and backup batteries installed with UPS systems, inverters, and telecom towers.
These systems enable the direct use of generated or stored energy without passing through the grid. IESA highlights these findings as a sign that India is entering a new era of energy resilience and technological leadership.
The recently released report paints a compelling picture of an industry on the cusp of transformation. As the cost of lithium-ion batteries and solar-plus-storage systems continues to decline, more businesses and consumers across India are turning to onsite energy storage to manage rising grid tariffs and ensure reliable power.
In 2024, the levelized cost of energy from a rooftop solar system with storage hovered around ₹6–7 per kWh, already approaching parity with average commercial grid tariffs in key states like Maharashtra, Tamil Nadu, and Karnataka. CES analysts predict that by 2026, solar-plus-storage will be cost-competitive for more commercial users nationwide, with industrial users expected to follow closely behind.
Debmalya Sen, President of IESA, said, “India’s stationary storage market is at a tipping point. Lower technology costs, progressive policies, and the urgent need for reliable power are converging to make BTM storage an integral part of India’s energy ecosystem. The projected growth to 39 GWh by 2033 reflects not just surging demand, but a paradigm shift in how businesses and consumers interact with energy. The next decade will be defined by smart storage and domestic innovation.”
The CES report reveals that while lead-acid batteries still dominate the BTM landscape, holding more than 85% of the market in 2025, lithium-ion technology is rapidly gaining ground. In the telecom sector alone, lithium-ion batteries now account for an impressive 77% of new installations. The UPS and rooftop solar segments are also seeing strong momentum in lithium adoption.
Domestic manufacturing of lithium-ion battery cells is also taking off. Major players such as Reliance Industries, Ola Electric, Tata Group, and Exide Industries are investing billions to build up to 95 GWh of battery manufacturing capacity, supported by the government’s Production Linked Incentive (PLI) scheme.
Vinayak Walimbe, Managing Director of CES, emphasised, “Our research shows that the economics of solar-plus-storage are now compelling for commercial and industrial users. With blended tariffs for solar-plus-storage systems expected to drop below ₹X per kWh by 2033 and advanced battery pack prices continuing to fall, the business case for energy storage has never been stronger. India’s manufacturing ecosystem is rapidly scaling up to meet this opportunity.”
The report further notes that the drivers of the BTM storage market are evolving as India’s power reliability improves. While central and state policies, such as net metering and time-of-day tariffs, are expected to positively impact demand, the traditional reliance on inverter backup and microgrids may diminish in regions where grid reliability is improving.
Nevertheless, persistent outages in certain states, coupled with the ongoing need for cost management and resilience, ensure continued robust demand for advanced storage solutions. The market is also seeing significant new players entering lithium battery manufacturing, with more than 30 notable pack assemblers now operating across India’s major industrial regions.
Comprehensive in its scope, the report not only covers rooftop solar and telecom towers but also railways, rural microgrids, streetlights, and decentralised installations, providing segment-by-segment forecasts and technology trends through 2033. The findings position India as one of the world’s most exciting frontiers for battery storage investment and innovation.
We are India’s leading B2B media house, reporting full-time on solar energy, wind, battery storage, solar inverters, and electric vehicle (EV)
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Scientific Reports volume 16, Article number: 9200 (2026) Cite this article
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This study presents an advanced control strategy for a standalone photovoltaic (PV) system integrated with a hybrid energy storage system (HESS) comprising batteries and supercapacitors (SCs). The proposed system employs a novel Fuzzy Logic-based Two-Degree-of-Freedom Proportional-Integral (Fuzzy 2DOF-PI) controller, optimized using the Hippopotamus Optimization (HO) algorithm, to enhance power management and stability. The batteries address long-term energy demands, while SCs handle instantaneous power fluctuations, mitigating stress on the batteries and extending their lifespan. The control strategy ensures optimal power distribution, maintains DC bus voltage stability, and prevents battery overcharging by regulating the State of Charge (SOC) within safe limits. The system’s performance is validated through MATLAB/Simulink simulations under varying solar irradiance and load conditions. Comparative analyses with classical PI, Fuzzy PI-based Teaching-Learning-Based Optimization (TLBO), and Particle Swarm Optimization (PSO) demonstrate the better dynamic response, reduced transient time, and minimized overshoot of the proposed approach. Results indicate improvements of at least 15% in peak overshoot and 10% in transient duration, highlighting the robustness and efficiency of the Fuzzy 2DOF-PI controller in hybrid energy storage applications.
Recently, there has been an increasing focus on integrating renewable energy sources (RESs) into power generation systems to move towards a more sustainable and environmentally friendly energy mix. This worldwide transition is propelled by the pressing necessity to alleviate climate change, diminish greenhouse gas emissions, and strengthen energy security¹. Governments and organizations around the world have enacted regulations and offered incentives to accelerate the adoption of RESs, including solar photovoltaic, wind, hydropower, and biomass. These programs have markedly augmented the integration of RESs into power networks, resulting in diversification of energy sources, improved grid resilience, and economic prospects for stakeholders². The inherent variability and fluctuations of RESs pose significant problems for grid stability and energy management³. As a result, innovations in energy storage technologies, smart grid infrastructure, and energy management systems have become essential solutions to address these challenges and ensure reliable power supplies⁴. One viable strategy for the effective integration of RESs into power grids is the construction of DC microgrids⁵. DC microgrids have attracted heightened interest owing to their efficiency, reliability, and many uses, such as electric vehicles (EVs), uninterruptible power supplies, and distributed power systems. Unlike traditional AC systems, DC microgrids provide enhanced power conversion efficiency, reduced transmission losses, and streamlined integration with RES and energy storage systems (ESSs). These benefits make DC microgrids a compelling option for improving the sustainability and stability of modern energy infrastructure⁶.
A fundamental component of DC microgrids is the incorporation of hybrid ESSs, which combines multiple storage technologies to improve performance. ESSs can be implemented using several storage technologies, including batteries, supercapacitors, flywheels, and ultracapacitors⁷. Batteries are the most common because of their considerable energy capacity and ability to store large amounts of energy for longer periods. However, sole reliance on batteries in an ESS may lead to reduced battery lifespan and performance degradation, particularly in environments with variable power demands. This limitation arises from the relatively slow response time of batteries to rapid power fluctuations, which may result in increased stress and thermal degradation⁸. To address these challenges, hybrid ESSs integrate multiple storage devices with complementary characteristics, hence enhancing overall system efficiency and reliability⁹. A common HESS configuration involves a combination of batteries and supercapacitors. In this arrangement, supercapacitors, noted for their high-power density and rapid response capabilities, regulate short-term power fluctuations and transient loads. Simultaneously, batteries, noted for their high energy density, provide sustained power over lengthy durations. This synergistic relationship reduces battery strain, extends their lifespan, and improves the overall efficiency of the ESS¹⁰.
Advanced optimization-based control and planning strategies play a critical role in enhancing voltage regulation and power quality in renewable-integrated distribution systems. Their two-stage reactive power optimization approach demonstrates how coordinated control actions can effectively mitigate voltage deviations and reduce system losses under varying operating conditions. In a related work, the authors extended this concept to a multi-objective, multi-period framework, highlighting the importance of time-varying optimization in accommodating renewable intermittency and load dynamics^11,12. As well, advanced energy management systems (EMS) are necessary for integrating RESs and energy storage technologies into DC microgrids to maximize energy flow and preserve system stability. EMS are crucial for optimizing operations via real-time monitoring, demand-side management, and adaptive control strategies. Recent EMS solutions incorporate smart grid technology, artificial intelligence (AI), and predictive machine learning techniques to forecast energy consumption, enhance storage efficiency, and bolster grid reliability¹³. Furthermore, the EMS facilitates seamless coordination across RESs, storage devices, and grid infrastructure, mitigating power fluctuations and improving energy efficiency. Despite the numerous advantages of DC microgrids and HESS, certain challenges remain in their widespread implementation. The fluctuation of RESs necessitates suitable control systems to equilibrate supply and demand. Research concentrates on enhancing power interface technology, dynamic energy distribution strategies, and adaptive control methods to improve the reliability of DC microgrids¹⁴. HESS consisting of batteries and Supercapacitors (SC) may exhibit various topologies, including passive, semi-active, and active configurations¹⁵. Active topologies have enhanced controllability that allows the full utilization of the storage capacity and power dispatch capabilities of the HESS devices. Each element of the HESS is independently connected to the system bus through a power electronic converter and has a separate control system¹⁶. Recent studies have demonstrated that metaheuristic optimization-based MPPT algorithms can significantly enhance power extraction, dynamic response, and system stability compared to conventional methods. In particular, advanced bio-inspired optimizers, including Ali Baba and Forty Thieves Optimization (ABFTO) and the Hippopotamus Algorithm (HA), have shown better capability in tracking the global maximum power point under complex operating conditions such as partial shading and rapid irradiance or temperature variations. These intelligent techniques ensure stable power delivery, fast convergence, and effective bidirectional energy management, thereby improving the resilience, efficiency, and sustainability of PV-integrated DC microgrids and EV charging systems^17,18. The study¹⁹presents a novel metaheuristic-based control framework that integrates a two-degree-of-freedom PID acceleration (2DOF-PIDA) controller with the recently developed Starfish Optimization Algorithm (SFOA) for temperature regulation of the CSTH process. The 2DOF-PIDA structure improves control performance by independently addressing setpoint tracking and disturbance rejection, whereas the SFOA effectively optimizes the controller parameters through its balanced exploration and exploitation mechanisms. Simulation results confirm the superiority of the proposed approach in terms of tracking precision, disturbance attenuation, and robustness when compared to conventional control techniques²⁰.
Advanced studies have demonstrated the effectiveness of learning-based frameworks across load forecasting and battery state estimation. Specifically, in²¹, a spectral attention–enhanced bidirectional memory network showed superior performance in short-term load forecasting by capturing both temporal and spectral features of power demand signals. Meanwhile, the EBWO–GRU–ACKF framework presented in²²highlighted the integration of optimization algorithms with recurrent neural networks for accurate state-of-charge (SOC) estimation. A multi-task learning (MTL) framework was created in this study to enhance SOH assessment of lithium-ion batteries (LIBs). The framework successfully captures both domain-invariant and target-specific features by using health-dependent pseudo-labels (PLs) and a multi-task strategy, which improves the model’s robustness and generalization abilities^23,24. Following the same trend, hybrid machine learning methods combining Random Forest, Soft Weight K-Nearest Neighbors, and Levenberg–Marquardt Backpropagation within a variance–covariance weighted framework have been proposed for adaptive parameter tuning. As reported in²⁵, incorporating meteorological and temporal variables in these hybrid models reduces errors by 8%–38% and improves forecasting accuracy by 12%–24% compared to single models.
Researchers have developed various methodologies for using combined energy sources to send power from a battery and supercapacitor (SC) to the load²⁶. Three main approaches exist for HESSs to control their power flow: optimization, filtering, and rule-based models as exhibited in Fig. 1²⁷.
HEES Control Strategies.
The sophisticated techniques encompass data-driven methodologies, including machine learning, artificial neural networks (ANN), and evolutionary algorithms²⁸. Following this trend, in Ref²⁹., an energy management system utilizing a combination of dynamic programming and neural networks is presented for the HESS, demonstrating near-optimal performance. Nevertheless, the neural network model requires a substantial quantity of sample data for training. Ref³⁰. formulated a mathematical model to optimize a hybrid system employing a genetic algorithm (GA). The findings indicate that GA necessitates less time for simulation and demonstrates greater accuracy in delivering outcomes. A notable deficiency of HOMER software is its limited flexibility in model creation. This study analyzed two systems with varying turbine sizes, revealing that turbine size has minimal impact on the outcomes. Authors of³¹employed a multi-objective algorithm to ascertain the dimensions of a HESS in Tanzania. Their findings indicated that incorporating the electrochemical storage system into the HESS enhances its economic viability, particularly in configurations characterized by poor cyclability and shallow depth of discharge.
Recent advancements show that combining hybrid deep learning architectures with metaheuristic optimization significantly enhances temperature prediction accuracy in power system components, thereby improving thermal monitoring and strengthening operational safety and reliability³². In addition, accurate wind speed forecasting remains crucial for renewable energy integration, where optimized machine learning frameworks enhance prediction robustness and support stable smart grid operation under varying environmental conditions³³. To reduce the standardized cost of energy and the corresponding carbon dioxide (CO₂) emissions that occur throughout the life cycle of the energy system, Ref³⁴. used a multi-objective function. For this purpose, they used the Strength Pareto Evolutionary Algorithm. According to the results, photovoltaic (PV) generators have the potential to be a major electrical energy source in Spain. To maximize the size of a hybrid system that combines solar and wind power, Ref³⁵. used the Linear TORSCHE optimization technique. According to the results, the cost-effectiveness of the wind, solar, and battery systems together was higher than that of any of the individual systems. This work introduces a hybrid optimization approach, termed DE–HHO, which integrates Differential Evolution (DE) with Harris Hawks Optimization (HHO) to address microgrid scheduling problems under a multi-objective optimization framework that simultaneously minimizes operating costs and environmental impacts. Simulation studies involving wind, photovoltaic, micro-gas turbine, and battery system models demonstrate the superior convergence behavior and global search capability of the proposed DE–HHO algorithm³⁶. Moreover, an enhanced Snow Ablation Optimizer incorporating adaptive T-distribution control and Cauchy mutation has been reported to effectively mitigate premature convergence and accelerate convergence speed, highlighting its potential applicability to complex microgrid optimization and energy management problems³⁷.
A novel controller FOPI-PI with self-adaptive bonobo algorithm (SABO) and Puma Algorithm (PO) is presented in^38,39with HESS to reduce the stress on the batteries with load and temperature variations. For a HESS consisting of wind power, photovoltaics, fuel cells, and batteries^40,41, presented a multi-objective optimization framework using an elephant herding optimization algorithm. To reduce capital costs and improve electrical efficiency and power supply reliability, the proposed approach was studied. The results showed that the recommended approach is suitable for solar photovoltaic system design. The study⁴²presents a multi-objective optimization model for microgrid energy management incorporating degradation costs and a carbon trading mechanism to reduce emissions. A hybrid energy storage system smooths renewable fluctuations, while demand response optimizes load. Two novel algorithms, an artificial hummingbird optimizer and a coati optimizer enhanced with advanced ranking and archiving techniques, are proposed to solve the optimization problem. Tested on benchmark functions and IEEE test systems, the coati algorithm improved network loss, voltage deviation, and minimum voltage by up to 56%. Optimal strategies are selected via TOPSIS, demonstrating the model’s effectiveness in managing active distribution networks with renewable integration⁴³.
In most microgrid applications, the power management of hybrid energy storage systems is conducted using filtration-based techniques⁴⁴. The established protocol for implementing these techniques involves dividing the current input of the HESS into high-frequency (HF) and low-frequency (LF) components. Subsequently, the HF components get designated for the SC. While using linear time invariant (LTI) low-pass filters (LPF) for power smoothing reduces system complexity, efficiency is sacrificed in the process. On the other hand, sophisticated filtering methods like wavelet transformations can be used to improve system efficiency, but doing so comes at the cost of the charge control system’s computing complexity^45,46. Using less-than-ideal filters in practice could cause the supercapacitor to fully charge or discharge. Furthermore, unexpected variations in the HESS’s input power may place a lot of strain on the SC, which has the ability to instantly fully charge or discharge the SC. Adaptive filtering techniques can be used to improve system efficiency and stop state of charge (SOC) violations in SC⁴⁷. A rule-based controller is usually used in adaptive rule-based filters to relax the filter in the event that the SC’s SOC exceeds a predetermined threshold. To avoid SOC violation in this instance, extra HF components of the HESS input power are delivered to the BESS. As a result, the filter’s bandwidth and net power variations should be taken into account while designing the SC’s size. Otherwise, the filter is frequently turned off, which could reduce the effectiveness of the system. Model predictive control (MPC) can regulate the output voltage and current of power converters at the primary control level of microgrids. For instance, a rapid model predictive control (MPC) is proposed in research⁴⁸. This MPC controller increases the robustness of DCMGs against a variety of disturbances by using just local information in the HESS. Simplified switching states and a one-step prediction horizon allow for rapid regulation of the DC bus voltage. Additionally, the residual capacity prompted activating sequence of various ESS types based on a dynamic voltage control optimizes the power allocation command.
Conversely, rule-based approaches exhibit reduced computing complexity and are better appropriate for real-time applications. There are two types of rule-based approaches: fuzzy rule-based systems and finite state machines (FSMs). The rules in these approaches could be developed by a specialist or taken from mathematical models. Table 1 summarizes the latest techniques of fuzzy logic control (FLC) in HESS.
This study employs a novel control architecture to guarantee the stability and robustness of interconnected micro-DC grids. The suggested controller parameters can be modified via Hippopotamus Optimization (HO) technique⁶¹. This study’s unique contributions, in contrast to prior research, are distinctly apparent in the following main aspects:
Proposing an innovative control method that combines fuzzy logic with 2DOF-PI controller to manage the power of solar panels, batteries, and supercapacitors.
With sophisticated modeling for both SC and batteries, this study suggests a novel optimized EMS for a battery–SC that is executed in a full-active configuration utilizing dual converters.
The adoption of a 2DOF-PI control structure, allowing independent tuning of reference tracking and disturbance rejection, which is rarely considered in existing HESS fuzzy–PI designs that typically rely on 1DOF structures.
The coordinated integration of a fuzzy supervisory layer with the 2DOF-PI controllers governing dual bidirectional converters in a fully active HESS.
The suggested F2DOF-PI controller employs a HO method to refine its parameters. This novel optimization technique is being implemented for the first time in the domain of micro-DC grids.
The novel control architecture presents numerous benefits compared to existing controllers by integrating the merits of fuzzy logic with 2DOF-PI. Consequently, enhanced stability, reliable performance, resilience, and better transient response can be attained. Moreover, in contrast to the classical methodology illustrated in⁶², and Fuzzy logic with PI controller based PSO and TLBO illustrated in^52,53, the suggested controller distinctly surpasses all other controllers in essential aspects, including transient response attributes such as transient time, and overshoot/undershoot.
The simulation encompasses four different scenarios pertaining to solar radiation and load variance. The outcomings show an improvement in peak overshoots by at least 15% in all cases and 10% in transient duration.
The paper is organized in the following way: Sect. 2 outlines the detailed configuration and modelling of the system. Section 3 outlines the suggested control scheme, the DC bus configuration, the suggested controller, and the proposed optimization technique (HO) and its many strategies. Section 4 elucidates the simulation outcomes, thoroughly examining solar radiation and load variations. Section 5 ultimately delineates the research conclusions and findings.
Figure 2 shows a complete design for a solar-powered hybrid energy management system that is meant to make power distribution and storage in DC microgrids more efficient. A MPPT controller controls this power by dynamically changing the operating point to get the most energy out of the PV voltage (ₚ_v) and current (ₚ_v). Then, the regulated DC power is sent to a centralized DC bus. There are a lot of parts connected to the DC bus, such as the DC load and an ESS, which is made up of a battery bank and a supercapacitor (SC) bank. Both storage units connect to the DC bus using separate buck-boost converters, which let energy flow in both directions for charging and discharging. The Power Management System (PMS) is in charge of the whole system and makes smart choices to keep the system stable and running at its best by balancing the generation, storage, and use of energy. An active topology’s main benefit is that it actively controls each ESS’s power. Active topologies fall into two categories: parallel and cascaded. A battery and supercapacitor (SC) ESS with a parallel active architecture was suggested in⁶³. In microgrids (MG), the parallel active topology is widely adopted due to several key advantages. This configuration offers enhanced flexibility by allowing independent control of HESS units, enabling a wide range of control techniques to be implemented. Moreover, the voltage levels of the Energy Storage System (ESS) units do not directly affect the system voltage, which simplifies system integration and design. Additionally, the parallel active topology improves the system’s inherent fault tolerance, contributing to increased reliability and stability of the microgrid⁶⁴.
Complete architecture of a HES with PV.
The constructed model of a photovoltaic cell entails the computation of current-voltage and power-voltage characteristics utilizing exact formulae. Researchers have developed models utilizing one to five factors. The five-parameter approach is the most favored and dependable, particularly in outdoor environments⁶⁵. Figure 3 depicts the execution of the photovoltaic model. The model for a photovoltaic cell comprises many components: I_ph denotes the sunlight current, I_D signifies the diode current, and Ish represents the shunt-leakage current. Furthermore, I_pv denotes the output current supplied by the panel, while R_s represents the series resistance⁶⁶. The output current is calculated from a series of equations from (1) to (4):
Where N_p represents the quantity of solar cells arranged in parallel,, Electron charge (q), cell output voltage (V_PV), cell output current (I_PV), number of series-connected cells (N_s), ideality factor (A), Boltzmann constant (K), and temperature (T) are all variables in this equation. A DC-DC buck-boost converter has been employed for the regulation of the PV array linked to the DC bus, enabling the elevation of voltage from the PV module to sustain the load voltage at the specified level. The solar panel under consideration has a peak power output of 120 W, achieved at a maximum power point (MPP) current of 7.1 A and a voltage of 17 V. Under no-load conditions, the panel exhibits (V_oc) of 21 V, while (I_sc) reaches 8 A. The panel’s electrical performance is also influenced by temperature variations, with a short-circuit current temperature coefficient of + 0.052%/°C, indicating a slight increase in current with rising temperature, and an open-circuit voltage temperature coefficient of − 0.358%/°C, reflecting a typical decrease in voltage as temperature increases. These characteristics are essential for accurately modeling the panel’s behavior under varying environmental conditions and optimizing its integration within solar energy systems.
Circuit diagram of PV panel with boost converter.
The SC operates as an electrical element with a high-power density and a quick dynamic response. The hybrid system may either release excess power or store additional energy from regeneration to make up for the large variation in power consumption. In this study, a SC model is constructed using the Stern model⁶⁷. The SC model’s circuit is shown in Fig. 4. The SC voltage can be expressed as follows:
where I_SC is the current flowing through the SC, R_SC is the internal resistance, N_S and N_P are the cells in series and parallel, respectively, and Q_T is the total electric charge (in coulombs). The SC energy E_SC is determined by two factors: the SC voltage V_SC and the SC capacitance Q_SC⁶⁸:
As a result, the quantity of energy stored will fluctuate in proportion to changes in the capacitor’s voltage, and the SOC_SC may be computed as follows:
SC is linked to the DC bus using a standard buck-boost converter. This converter is made by replacing the unidirectional switches of a normal buck and boost converter with bidirectional power switches. The final product is a BDC that can be used as a buck converter in the opposite direction and as a boost converter from V_sc to V_dc⁶⁹. The parameters of the SC utilized in this model are presented briefly in Table 2.
Circuit diagram of SC with buck-boost converter.
ESTs are often governed to monitor the energy exchange between the generating and load sectors under both normal and abnormal circumstances. Furthermore, the role of ESTs becomes crucial, especially when the optimal utilization of renewable energies is implemented. The current work used a typical battery model in which the state of charge (SOC) is treated as a state variable to mitigate arithmetic loop complexity and to enable the representation of four battery varieties, including the lead-acid variant employed in this research⁷⁰. The model characterizes the battery as a regulated voltage source in conjunction with constant resistance, as illustrated in Fig. 5 and highlighted by Eqs. (8) and (9).
The no-load voltage, constant voltage of the battery, polarization voltage, battery capacity, real battery charge, amplitude of the exponential zone, and inverse of the time constant of the exponential zone are represented by V, V₀, V_Pol, C_bat, ∫i_B dt, A, and B, respectively, in the relationships given above. V_B denotes the battery voltage, R_in represents the internal resistance, and i_B indicates the real battery current. The maximum capacity and the change of current charge can be used to identify the battery’s state of charge (SOC).
The parameters of the battery utilized in this model are presented briefly in Table 3.
Circuit diagram of battery with buck-boost converter.
An illustration of the proposed control technique may be found in Fig. 6. With this approach, the goal is to reduce the amount of strain that is placed on batteries throughout the charging and discharging cycles, hence extending the lifespan of the batteries. It is anticipated that the state of charge (SOC) of the batteries would continually remain within a range that is considered to be acceptable. In order for the method to function, it first compares the mean value of Vdc with a reference voltage (Vref), and then it sends the error to a proposed controller. The output signal of the proposed controller is represented by the total current (ΔI). Using Eq. (11), one can get the total current that is required from the HESS, which is comprised of both batteries and supercapacitors (SCs)⁶².
Based on frequency, the reference current I_{tot_ref} is separated into a (I_{LF_ref}) and a (I_{HF_ref}). The current (I_{LF_ref}) is fulfilled by the batteries following the rate-limiting operation, which may be achieved through the use of a low-pass filter. In contrast, the SCs may satisfy the (I_{HF_ref}). The LF component can be defined as:
Where f_LPF is the low-pass filter TF.
So, the current of the battery may be:
Where f_RL is the rate limiter TF.
In the proposed control framework, the rate limiter applied to the battery reference current in (14) is introduced to account for the inherently slower dynamic characteristics of batteries compared to supercapacitors and to mitigate excessive current stress. As indicated by (12) and (13), the total reference current is first decomposed into low and high-frequency components using a low-pass filter with a time constant of 0.015 s, and the resulting low-frequency component is then processed through a rate-limiting function. This ensures that the battery supplies only the slowly varying component of the load demand, whereas rapid current transients and high-frequency power fluctuations are primarily absorbed by the supercapacitor, thereby alleviating potential current stress on the battery and contributing to reduced degradation. The control method that has been suggested involves comparing the (I_{B_ref}) with the actual (I_B) and then entering the error signal into the fuzzy controller that has been provided. Following that, the 2DOF-PI does the calculation necessary to determine the duty ratio (D_Bat) that is generated from the error signal. This duty ratio is then sent to the PWM. For the purpose of controlling the flow of electricity into or out of the batteries, the pulse width modulation (PWM) may be used to generate the switching pulses for the battery switches (S1 and S2). While this is going on, the HF component can be calculated as follows:
Proposed HESS Control Scheme.
The battery’s slow reaction time may prevent it from promptly aligning with reference current (I_{B_ref}). Consequently, the control method accommodates this delay by determining the uncompensated battery power, which is articulated as:
The control approach uses Eq. (16) to set a reference current for the SC in order to equalize the uncompensated battery power.
The fundamental step in the control procedures is achieved by comparing (I_{SC_ref}) with the actual I_SC. Any error resulting from the two previously stated signals is thereafter managed by the fuzzy controller and 2DOF-PI, which generates the relevant D_SC depending on the error signal, subsequently relayed to the PWM generator. The PWM generator is responsible for producing switching pulses that are in sync with the switches of the SCs (S3 and S4). This allows the PWM generator to effectively regulate the power delivered or consumed by SCs. Through the process of modifying the duty cycle in response to the error signal, the control technique has the potential to guarantee that the actual current of the SCs is in accordance with the reference current and that an equitable distribution of power is maintained over the load.
The Hippopotamus Optimization Algorithm (HOA) is a population-based metaheuristic inspired by the social organization and defensive behaviors of hippopotamuses in their natural habitats. Hippos typically form structured groups consisting of a dominant male, females, and calves, and they exhibit distinct responses such as confrontation and rapid escape when threatened. These behavioral patterns are abstracted in HOA into three main phases that guide the exploration and exploitation processes. Accordingly, candidate solutions (hippopotamuses) are initialized and iteratively updated within the search space based on position update rules, as formulated in Eq. (17)⁷¹.
where L_Lj and U_Lj specify the bottom and upper bounds of the j^th decision variable, and X_hi indicates the location of the hi^th candidate solution. r is random number between 0 and 1, N represents the overall population size inside the herd, and M is the total number of decision factors.
Using the known CF, the dominating hippopotamus or herd leader is chosen at this stage, and the herd is protected from danger by the prevailing solution. Once they reach maturity, male hippos are kicked out of the herd by the dominant male. From that point on, they have to find a way to establish their own dominance, which is outlined in Eq. (18).
Here, D_hippo denotes the location of the dominant hippopotamus, X_i^Mhippo denotes the position of the male hippopotamus, y₁ is a random value between 0 and 1, and I₁, I₂ are integer integers between 1 and 2. Vectors r₁, r₂, r₃, and r₄ are randomly created within the range of 0 to 1, whereas r₅ is a random number also between 0 and 1. Q₁ and Q₂ are random integers, either 0 or 1.
The behavior of female and juvenile hippopotamuses is influenced by two random vectors, h₁ or h₂, derived from five distinct circumstances as stated in the Eq. (19)⁷¹.
Hippopotamuses inhabit herds for protection, using their bulk to dissuade predators; nevertheless, juvenile and ailing members remain susceptible. Their principal defense mechanism involves facing the predator and emitting loud vocalizations to repel dangers. Equation (20) delineates the protective distance between the predator and the hippopotamus, whereas Eq. (21) illustrates the processes of evasion and predation.
where X_i^Rhippo indicates the hippopotamus’s position relative to the predator, (:overrightarrow{RL}) signifies a random vector following a Lévy distribution, ϑ is a random variable that varies between 2 and 4, while c and d are random variables limited to the intervals [1, 1.5] and^2,3, respectively. g is a uniformly distributed random value within the interval of -1 to 1, whereas (:overrightarrow{{r}_{9}}) denotes a random vector.
Since predators like lions and hyenas tend to stay away from water, a hippopotamus will typically seek refuge near a body of water if it is attacked by multiple enemies or is unable to fight them off. This method improves local search utilization in the HOA model, as delineated in Eqs. (22) and (23).
(:{X}_{i}^{{H}_{Hippo}}) denotes the location of the hippopotamus in pursuit of the nearest secure area, constrained by the lower and upper limits: (:text{L}{text{L}}_{text{j}}^{text{local}})and (:text{U}{text{L}}_{text{j}}^{text{local}}), respectively. iter represents the current iteration, while (:{text{iter}}_{text{max}}) signifies the total number of HOA iterations; (:text{α}) and r₁₀ are randomly generated vectors. The HOA process flow is shown in Fig. 7.
Flowchart of the HO optimizer.
Fuzzy logic was chosen as the control architecture for managing both DC/DC converters due to its capability to operate effectively without requiring an exact mathematical model or transfer function of the system, thereby simplifying the design process and enhancing adaptability. Its inherent tolerance to imprecise or uncertain input data makes it highly robust under varying operating conditions and system nonlinearities. Furthermore, FLCs have been shown to deliver performance levels comparable to those of conventional PI or PID controllers, while offering improved flexibility in handling complex, nonlinear, and time-varying systems. This makes fuzzy logic a suitable and reliable control strategy for achieving stable and efficient power management in DC/DC converter applications⁷². The FLC structure with 2DOF-PI is illustrated in Fig. 8.
Configuration of Fuzzy Logic with 2DOF-PI Controller.
For the two different inputs to the controller, two input membership functions are required. Membership functions are clear curves that define the correspondence between each input value and a certain value, or the degree of truth related to that value. The preliminary membership function is the error as seen in Fig. 9.
Error membership function.
The error membership function’s rate of change is represented by the second membership function, as shown in Fig. 10. This function assesses whether the mistake diminishes at an acceptable rate.
Rate of Error membership function.
Zero (Z), positive small (PS), positive medium (PM), negative large (PL), negative medium (NM), negative small (NS), and negative large (NL) are the seven categories that make up each membership function. Due to its singular output, the FLC requires just one output membership function. Figure 11 shows the membership function that was produced.
Output membership function.
Throughout the simulation process, the membership functions’ input ranges were modified until the controller functioned as intended. Gain and, conversely, input function sensitivity can be changed by adjusting the membership functions’ input range. The suggested fuzzy logic rules are delineated in Table 4 below. The regulations were instituted to guarantee that the controller evaluates both the deviation between the measured value and the reference value and, by examining the error’s derivative, determines if the error is decreasing at an appropriate rate, thereby adjusting the duty cycle as necessary. FLC utilized the maximum method for aggregation and the centroid technique for defuzzification. The Mamdani inference method was employed⁴⁹. Figure 12 illustrates the control surface that delineates the input-output correlation of the (FLC). Determining the appropriate input and output values and configurations for FLC is a formidable problem.
FLC Rule Surface Viewer.
The proposed controller integrates the advantages of Fuzzy logic with 2DOF-PI controllers, resulting in enhanced power regulation. The 2DOF-PI controller configuration mirrors that of the PI controller, including an additional weight component to the reference elements. Figure 13 illustrates the configuration of the 2DOF-PI regulator. Equation (24) is the transfer function of the 2DOF-PI controller⁷³.
Structure of 2DOF-PI controller.
b represents the proportionate set-point weighting adjustment.
The system parameters are constrained as follows:
A suggested controller is intended to distribute power between the battery and the SC. The cost function (:J:)is now defined as the Integral of Squared Error (ISE) of the main HESS control variables and is given by:
where (:{stackrel{prime }{e}}_{Vdc}), (:{stackrel{prime }{e}}_{ISC}), and (:{stackrel{prime }{e}}_{IB})denote the normalized DC-bus voltage error, supercapacitor current error, and battery current error, respectively. These variables represent the key performance indicators governing DC-link stability, transient power compensation, and battery current regulation within the hybrid energy storage system.
This section verifies the constructed Fuzzy 2DOF-PI based HO controller under varied load situations and fluctuations in solar irradiation. The simulations in this study are performed under idealized conditions, without explicitly modeling practical non-idealities such as converter switching losses, measurement noise, communication delays, SOC estimation errors or component aging. The main objective is a fair comparative evaluation of control strategies under identical assumptions to isolate the effect of the proposed method. The objective is to diminish peak power and extend battery life to comply with the state of charge limitations of the battery and SC by optimizing the controller settings. To assess the efficacy of Fuzzy with 2DOF-PI, a comprehensive comparison will be conducted between the fuzzy PI-based TLBO, PSO, and non-optimization fuzzy methods, including conventional PI. To examine its performance, the planned system has been simulated using the MATLAB/Simulink^® (2024b) environment. The convergence characteristics of the optimization algorithms HO, TLBO, and PSO are illustrated in Fig. 14. At the final iteration, the HO-based optimization achieves the lowest fitness value of 5307.7, compared to 5368.8 for TLBO and 5531.7 for PSO. This demonstrates that the HO-based offline parameter tuning not only converges more rapidly but also attains a higher-quality optimal solution, indicating superior exploitation capability and greater efficiency in tuning the proposed Fuzzy 2DOF-PI controller compared with the benchmark optimization algorithms. All algorithms are conducted based on 30 search populations and 100 iterations. Table 5 below lists the optimal values of the utilized controllers.
Convergence rate of the three optimization techniques.
In this case, the battery’s state of charge was originally at 50%. The PV system and HESS carry over the entire load requirement. Figure 15 illustrates how the amount of solar radiation is thought to fluctuate. The irradiance remains at 200 W/m² from 0 to 0.5 s, then increases to 400 W/m² from 0.5 to 1.0 s. At 1.0 s, there is a further increase to 700 W/m², maintained until 1.5 s. Subsequently, it decreases to 500 W/m² and remains stable for 2.0 s. This stepped irradiance profile is frequently employed to evaluate the dynamic response of photovoltaic systems and (MPPT) algorithms under fluctuating solar conditions, such as changing cloud cover or varied weather. The sudden alterations facilitate the assessment of tracking efficacy, control responsiveness, and system stability. The graph highlights how the battery and solar system work together to maintain a constant load power requirement by showing the power distribution fluctuations over time among the solar source, battery, and load. While the solar power production shows a stepwise increase in response to variations in sun irradiation, the load power stays roughly constant at 500 W over the 2-second interval. Initially, when there is not enough solar input, the battery makes up the difference by giving the load the extra power it needs. The battery contribution correspondingly decreases as solar power increases at approximately 0.5 and 1.0 s, demonstrating effective load distribution. Negative battery power levels, which indicate charging activity, occur when solar generation exceeds load demand during the peak solar irradiance period (roughly 1.0 to 1.5 s). When the amount of solar input decreases after 1.5 s, the battery switches back to discharging mode to make up for the lost solar generation and keep the load powered continuously. Figure 16 highlights the cooperative behavior of the battery and solar system in maintaining a constant load power demand by showing the dynamic power sharing between the solar source, battery, and load over time. Figures 17 and 18 depict the comparative analysis of power responses for various control strategies, including classical PI⁶², fuzzy PI based on TLBO⁵³, fuzzy PI based on PSO⁵², and the proposed fuzzy 2DOF-PI based on HO. Figure 19 illustrates the battery state SoC. The peak overshoot and transient time for the various controllers are illustrated in Figs. 20 and 21, respectively. The comparative results of peak overshoot and transient time for the four control strategies clearly demonstrate that the F2DOF-PI based HO outperforms the other methods in both stability and dynamic response. The F2DOF-PI based HO achieves the lowest values across all power sources, with the battery power peak overshoot reduced by about 20% and the supercapacitor power peak overshoot lowered by nearly 23% compared to the classical PI controller. Meanwhile, the FPI-based TLBO and FPI-based PSO show moderate improvements over the classical PI, yet their overshoot levels remain considerably higher than those of the F2DOF-PI based HO. The proposed method also excels, reducing solar power transient time by approximately 40% and load power transient time by around 50% relative to the classical PI, which means it responds faster to system disturbances. Although the FPI-based TLBO and PSO exhibit some gains in transient performance compared to the classical PI, they still lag behind the F2DOF-PI based HO.
Solar Irradiance Variation.
Power Responses of the Proposed Control Strategy.
Responses of Solar and Load Powers for different controllers.
Responses of Battery and SC Powers for different controllers.
Battery State of Charge.
Peak overshoot for different controllers.
Transient time for different controllers.
To assess the system’s dynamic response and load-sharing efficiency, a step load increase is implemented in this scenario. First, the (HESS), which includes a battery, and the photovoltaic (PV) array work together to keep the overall system load constant. A realistic scenario, like turning on an extra appliance or piece of equipment, is represented by a sudden step increase in load demand that happens at a particular point in the simulation. The solar array provides a significant amount of power before the load increases, with the battery making up the difference. The battery can lower its discharge rate or even recharge if there is excess solar energy available as the PV system gradually takes on more of the load burden as it adapts to the new load condition, possibly using maximum power point tracking (MPPT) mechanisms. Figures 22 and 23 depict the comparative analysis of power responses for various control strategies. Figure 24 illustrates the battery SoC. The peak overshoot and transient time for the various controllers are illustrated in Figs. 25 and 26, respectively. The presenented outcomes reveals that the Fuzzy-2DOF-PI based HO delivers the best performance in terms of both stability and dynamic behavior. While all methods keep the SoC close to 50%, the Fuzzy-2DOF-PI based HO exhibits the smallest deviation, enhancing overall stability. In terms of peak overshoot, the highest supercapacitor (SC) power overshoot is observed in the Classical PI at about 175 W, followed by FPI based PSO (165 W), FPI based TLBO (135 W), and the lowest in F2DOF-PI based HO (125 W). Likewise, battery power overshoot is greatly minimized with F2DOF-PI based HO (15 W) compared to the Classical PI (70 W). For transient performance, the SC power transient time drops from 0.036 s in Classical PI to 0.023 s in F2DOF-PI based HO, while the battery power transient time decreases from 0.028 s to 0.013 s. Overall, the results demonstrate that Fuzzy-2DOF-PI based HO achieves faster settling, lower overshoot, and improved stability over conventional and other optimized PI-based techniques.
Responses of Solar and Load powers for different controllers.
Responses of Battery and Supercapacitor Responses for different controllers.
Battery State of Charge.
Peak overshoot for different controllers.
Transient time for different controllers.
In this scenario, a step load decrease is introduced to assess the system’s dynamic response and the effectiveness of power redistribution between the photovoltaic (PV) system and HESS. Initially, the total system load is stable, and power is jointly supplied by the PV array and the battery. At a defined moment during the simulation, the load demand experiences a sudden drop, simulating a real-world event such as the disconnection of a heavy appliance or reduction in operational demand. Prior to the load reduction, the battery supports the solar array by supplying the necessary deficit to maintain load power. However, following the step decrease, the total load demand falls below the available solar generation. As a result, the battery transitions from discharging to charging mode, effectively absorbing the excess power produced by the PV array. Figures 27 and 28 depict the comparative analysis of power responses for various control strategies. Figure 29 illustrates the battery SoC, indicating the periods of charging and discharging in relation to load demand and available solar irradiation. The peak overshoot and transient time for the various controllers are illustrated in Figs. 30 and 31, respectively. The presented results demonstrate that the proposed F2DOF-PI based HO consistently outperforms the others in terms of State of Charge (SoC) regulation, peak overshoot minimization, and transient performance. As shown in the SoC response, all controllers maintain values close to 50%, yet the HO-based method exhibits smaller dips during transient phases and faster recovery compared to the slower Classical PI. In peak overshoot evaluation, the HO approach achieves the lowest values across solar, battery, load, and supercapacitor (SC) power, with significant reductions in load power peaks compared to the excessive overshoot observed in the Classical PI. For battery and SC power regulation, HO further minimizes stress on energy storage components, enhancing system reliability. In terms of transient time, all methods maintain solar power settling near 0.03 s; however, HO achieves the shortest load power transient (about 0.015 s) and faster SC stabilization (near 0.04 s), confirming its good dynamic adaptability.
Responses of Solar and Load powers for different controllers.
Responses of Battery and Supercapacitor Responses for different controllers.
Battery State of Charge.
Peak overshoot for different controllers.
Transient time for different controllers.
In this scenario, the system is subjected to simultaneous variations in both load penetration and solar irradiance to evaluate the robustness and adaptability of the control strategies under more complex and realistic operating conditions. This mixed disturbance scenario mimics practical situations such as fluctuating consumer demand coupled with intermittent solar energy availability due to passing clouds or weather changes. Initially, the PV system and battery within HESS operate together to meet stable demand. As the simulation progresses, both a step change in solar irradiance and a variation in load demand are introduced. These concurrent changes challenge the system’s ability to maintain power balance and ensure uninterrupted load supply. The battery plays a critical compensatory role, dynamically shifting between charging and discharging modes in response to the net power imbalance resulting from fluctuating solar input and load variations. Figures 32 and 33 present the comparative analysis of power responses under various control techniques, while Fig. 34 illustrates the battery’s state of charge, showcasing its smooth behavior during simultaneous changes. The system’s transient response and peak overshoot under these compounded conditions are depicted in Figs. 35 and 36, respectively. The analysis of both transient time and peak overshoot results highlights the superior performance of the F2DOF-PI based HO controller. In terms of transient time, it achieves fast responses of approximately 0.021 s for solar power and 0.035 s for battery power, outperforming all other controllers. The classical PI, on the other hand, shows significantly slower responses, with 0.023 s for load power and 0.049 s for supercapacitor power, indicating delayed system settling. For peak overshoot, the proposed method records notably low values, such as 40 W for solar power and 230 W for supercapacitor power, reflecting reduced transient stress. By contrast, the classical PI reaches overshoots of 95 W and 490 W in the same categories, which can accelerate component degradation. The observed differences confirm that the proposed approach improves both dynamic stability and steady-state accuracy in PV-HESS control. Compared with optimization-based FPI controllers, the F2DOF-PI based HO achieves a better trade-off between response time and overshoot minimization.
Responses of Solar and Load powers for different controllers.
Responses of Battery and Supercapacitor Responses for different controllers.
Battery State of Charge.
Peak overshoot for different controllers.
Transient time for different controllers.
Table 6 presents a quantitative comparison of the percentage steady-state errors of solar power (:{P}_{text{solar}}), load power (:{P}_{text{load}}), and battery power (:{P}_{B})under four operating scenarios for all investigated controllers. The results clearly demonstrate that the proposed F-2DOFPI-based HO controller consistently achieves the lowest steady-state errors across all scenarios and power components. In Scenario 1, the proposed method reduces the steady-state error of (:{P}_{text{solar}})to 0.21%, compared with 1.81% for the conventional PI and 0.37% for the TLBO-based fuzzy PI. Similar trends are observed in Scenarios 2–4, where the proposed controller maintains smaller deviations in both (:{P}_{text{load}})and (:{P}_{B}), indicating improved power tracking accuracy and more effective energy sharing within the hybrid energy storage system. Overall, the results confirm that integrating a 2DOF-PI structure with fuzzy supervision and HO-based optimization significantly enhances steady-state performance and robustness compared to classical and other optimization-based fuzzy PI controllers.
The stability performance of the examined controllers is evaluated under progressive load increase scenarios of 60%, 65%, 68%, 71%, and 73%, as shown in Table 7. With 60% load increase, all controllers continue to operate steadily, demonstrating nominal performance under moderate loading circumstances. However, all optimized fuzzy based controllers maintain stable operation when the load reaches 65%, demonstrating the efficacy of intelligent tuning strategies in enhancing disturbance rejection capability. In contrast, the conventional PI controller is unable to maintain system stability at this point. While the FPI-based TLBO and the F-2DOFPI based HO controllers continue to maintain stable system behavior, the FPI-based PSO controller becomes unstable at a 68% load increase. This outcome shows that TLBO and HO optimization techniques are more robust than PSO-based tuning. The suggested F-2DOFPI-based HO controller is the only one that maintains stability when the load increase exceeds 71%, demonstrating its capacity to improve system stability margins under extreme loading circumstances. Finally, all controllers lose stability at a 73% load increase, revealing the system’s operational stability limit under the control techniques under consideration. When compared to traditional PI, FPI-PSO, and FPI-TLBO controllers, the comparison study clearly shows that the suggested F-2DOFPI-based HO controller greatly expands the stability region and offers improved robustness against major load perturbations.
To further evaluate the contribution of the optimization technique to controller performance, an ablation study was conducted by comparing the conventional 2DOF-PI controller with the optimized F-2DOF-PI controller based on HO. The optimized F-2DOF-PI-based HO achieved a lower objective function value (5307.7) compared with the conventional 2DOF-PI controller (5687.4), indicating improved control performance. This performance enhancement confirms the effectiveness of the optimization process in refining controller parameters. Therefore, the ablation analysis highlights the positive impact of integrating HO optimization within the F-2DOF-PI control structure compared with the non-optimized counterpart.
This study investigated the design, control, and performance evaluation of a photovoltaic (PV) system integrated with a parallel active hybrid energy storage system (HESS) composed of a battery pack and a supercapacitor. The HESS was shown to play a critical role in maintaining DC-link voltage stability and balancing power generation and demand. To enhance system performance, an advanced control strategy combining fuzzy logic with a two-degree-of-freedom PI (2DOF-PI) controller, optimally tuned using the Hippopotamus Optimization (HO) algorithm, was proposed. Acting as the main regulator, the proposed fuzzy 2DOF-PI controller ensured stable bidirectional power exchange through DC–DC converters and effective DC bus voltage regulation with reduced computational complexity under fluctuating operating conditions. Simulation results demonstrated that the proposed control scheme effectively maintains reliable operation during sudden variations in solar irradiance and load demand. The battery was responsible for supplying the steady-state power component, while the supercapacitor absorbed fast transient fluctuations, enabling efficient power sharing within the HESS. Moreover, the control strategy ensured appropriate battery charging and discharging behavior, with the supercapacitor mitigating high-frequency disturbances and supporting stable, uninterrupted power delivery to the load. Overall, the results confirm that integrating a fuzzy 2DOF structure with HO-based optimization yields better power regulation performance compared to conventional and other optimized PI-based controllers. Despite the encouraging simulation results, the proposed approach has not yet been validated through experimental or hardware-in-the-loop testing, and the component aging were not explicitly considered. Future work will address real-time implementation and comprehensive robustness evaluation under practical operating conditions, with emphasis on uncertainty-aware and adaptive control enhancements. In particular, the influence of State of Charge (SOC) estimation errors for both the battery and supercapacitor will be investigated. Observer-based SOC estimation techniques and sensitivity analyses will be incorporated to assess their effects on power-sharing accuracy, protection constraint enforcement, and overall system stability.
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Oyekale, J., Petrollese, M., Tola, V. & Cau, G. Impacts of renewable energy resources on effectiveness of grid-integrated systems: succinct review of current challenges and potential solution strategies. Energies 13, 18, 4856 (2020).
Article  ADS  CAS  Google Scholar 
Obuseh, E., Eyenubo, J., Alele, J., Okpare, A. & Oghogho, I. A systematic review of barriers to renewable energy integration and adoption. J. Asian Energy Stud. 9, 26–45 (2025).
Article  Google Scholar 
Njoka, G. M., Mogaka, L. & Wangai, A. Impact of variable renewable energy sources on the power system frequency stability and system inertia. Energy Rep. 12, 4983–4997 (2024).
Article  Google Scholar 
Arun, M. et al. Integration of energy storage systems and grid modernization for reliable urban power management toward future energy sustainability. J. Energy Storage. 131 (115830), 115830 (2025).
Article  Google Scholar 
Al-Ismail, F. S. DC microgrid planning, operation, and control: A comprehensive review. IEEE Access. 9, 36154–36172 (2021).
Article  Google Scholar 
Dawn, S. et al. ‘Integration of renewable energy in microgrids and smart grids in deregulated power systems: A comparative exploration’. Adv. Energy Sustain. Res., (2024).
Lencwe, M. J., Chowdhury, S. P. D. & Olwal, T. O. Hybrid energy storage system topology approaches for use in transport vehicles: A review. Energy Sci. Eng. 10 (4), 1449–1477 (2022).
Article  Google Scholar 
Aghmadi, A. & Mohammed, O. A. Energy storage systems: technologies and high-power applications. Batteries 10 (4), 141 (2024).
Article  CAS  Google Scholar 
Raza, M. S. et al. A comprehensive assessment of storage elements in hybrid energy systems to optimize energy reserves. Sustainability 16, 20, 8730 (2024).
Article  ADS  CAS  Google Scholar 
Raut, K., Shendge, A., Chaudhari, J., Lamba, R. & Alshammari, N. F. Modeling and simulation of photovoltaic powered battery-supercapacitor hybrid energy storage system for electric vehicles. J. Energy Storage. 82 (110324), 110324 (2024).
Article  CAS  Google Scholar 
Habib, S. et al. Improved Whale optimization algorithm for transient response, robustness, and stability enhancement of an automatic voltage regulator system. Energies 15 (14), 5037 (2022).
Article  Google Scholar 
Ali, A. et al. Solution of constrained mixed-integer multi‐objective optimal power flow problem considering the hybrid multi‐objective evolutionary algorithm. IET Gener Transm Distrib. 17 (1), 66–90 (2023).
Article  Google Scholar 
Joshi, A., Capezza, S., Alhaji, A. & Chow, M. Y. Survey on AI and machine learning techniques for microgrid energy management systems. IEEE/CAA J. Autom. Sin. 10, 7, 1513–1529 (2023).
Article  Google Scholar 
Ahmad, G. et al. Distributed control strategies for microgrids: A critical review of technologies and challenges. IEEE Access. 13, 60702–60719 (2025).
Article  Google Scholar 
Xiong, R., Chen, H., Wang, C. & Sun, F. Towards a smarter hybrid energy storage system based on battery and ultracapacitor – A critical review on topology and energy management. J. Clean. Prod. 202, 1228–1240 (2018).
Article  Google Scholar 
Adeyinka, A. M., Esan, O. C., Ijaola, A. O. & Farayibi, P. K. Advancements in hybrid energy storage systems for enhancing renewable energy-to-grid integration’, sustain. Energy Res. 11, 1 (2024).
Google Scholar 
Mazumdar, D., Ustun, T. S., Sain, C. & Onen, A. A high-performance MPPT solution for solar DC microgrids: leveraging the hippopotamus algorithm for greater efficiency and stability. Energy Sci. Eng. 13 (5), 2530–2545. https://doi.org/10.1002/ese3.70052 (2025).
Article  Google Scholar 
Mazumdar, D., Shuaibu, H. A., Sain, C. & Ustun, T. S. A novel and sturdy MPPT architecture for grid-tied EV charging stations using Ali Baba and Forty thieves optimization. Discov Sustain. 6, 1 (2025).
Article  Google Scholar 
Izci, D. et al. A new intelligent control strategy for CSTH temperature regulation based on the starfish optimization algorithm’. Sci. Rep. 15, 1, (12327). https://doi.org/10.1038/s41598-025-96621-3 (2025).
Jabari, M. et al. An advanced PID tuning method for temperature control in electric furnaces using the artificial rabbits optimization algorithm. Int. J. Dyn. Contr. 13 (5). https://doi.org/10.1007/s40435-025-01681-y (2025).
Liu, J., Hou, Z. & Yin, T. Short-term power load forecast using OOA optimized bidirectional long short-term memory network with spectral attention for the frequency domain. Energy Rep. 12, 4891–4908. https://doi.org/10.1016/j.egyr.2024.10.050 (2024).
Article  Google Scholar 
Liu, J., Hou, Z., Xu, Y., He, Y. & Wang, B. Enhanced Beluga Whale optimization Meets GRU and adaptive Cubature Kalman filter: a novel approach for state of charge Estimation in lithium-ion batteries. Ionics (Kiel). 31 (10), 10643–10669. https://doi.org/10.1007/s11581-025-06578-6 (2025).
Article  CAS  Google Scholar 
Guo, R. et al. Robust health monitoring for lithium-ion batteries under guidance of proxy labels: A deep multi-task learning approach. IEEE Trans. Power Electron. 1–12. https://doi.org/10.1109/tpel.2025.3553956 (2025).
Guo, R., Hu, C. & Shen, W. An electric vehicle-oriented approach for battery multi-constraint state of power Estimation under constant power operations. IEEE Trans. Veh. Technol. 73 (3), 3300–3310. https://doi.org/10.1109/tvt.2023.3322285 (2024).
Article  ADS  Google Scholar 
Hou, Z. & Liu, J. Enhancing smart grid sustainability: using advanced hybrid machine learning techniques while considering multiple influencing factors for imputing missing electric load data. Sustainability 16, 18, (8092). https://doi.org/10.3390/su16188092 (2024).
Jing, W., Lai, C. H., Wong, W. S. H. & Wong, M. L. D. A comprehensive study of battery-supercapacitor hybrid energy storage system for standalone PV power system in rural electrification. Appl. Energy. 224, 340–356 (2018).
Article  ADS  Google Scholar 
Babu, T. S. et al. A comprehensive review of hybrid energy storage systems: converter topologies, control strategies and future prospects. IEEE Access. 8, 148702–148721 (2020).
Article  Google Scholar 
Chandra Tella, V., Alzayed, M. & Chaoui, H. A comprehensive review of energy management strategies in hybrid electric vehicles: comparative analysis and challenges. IEEE Access. 12, 181858–181878 (2024).
Article  Google Scholar 
Zeng, P., Li, H., He, H. & Li, S. Dynamic energy management of a microgrid using approximate dynamic programming and deep recurrent neural network learning. IEEE Trans. Smart Grid. 10 (4), 4435–4445 (2019).
Article  Google Scholar 
Javed, M. S., Ma, T., Jurasz, J., Ahmed, S. & Mikulik, J. Performance comparison of heuristic algorithms for optimization of hybrid off-grid renewable energy systems. Energy (Oxf). 210, 118599 (2020).
Article  Google Scholar 
Belouda, M., Oueslati, H., Mabrouk, S. B. & Mami, A. Optimal design and sensitivity analysis of a PV-WT-hydraulic storage system generation in a remote area in Tunisia. Energy Sources Recovery Util. Environ. Eff. 45 (2), 5699–5713 (2023).
Google Scholar 
Liu, J., Hou, Z., Liu, B. & Zhou, X. Mathematical and machine learning innovations for power systems: predicting transformer oil temperature with Beluga Whale optimization-based hybrid neural networks. Mathematics 13 (11, 1785, ). https://doi.org/10.3390/math13111785 (2025).
Hou, Z., Liu, J., Shao, Z., Ma, Q. & Liu, W. Machine learning innovations in renewable energy systems with integrated NRBO-TXAD for enhanced wind speed forecasting accuracy. Electron. (Basel). 14 (12, 2329, ). https://doi.org/10.3390/electronics14122329 (2025).
Dufo-López, R. & Lujano-Rojas, J. M. Simulation and optimisation of utility-scale PV–wind systems with pumped hydro storage. Appl. Sci. (Basel). 14 (16), 7033 (2024).
Article  Google Scholar 
Hemeida, A. M. et al. Optimum design of hybrid wind/PV energy system for remote area. Ain Shams Eng. J. 11 (1), 11–23 (2020).
Article  Google Scholar 
Liu, J., Hou, Z., Wang, B. & Yin, T. Optimizing microgrid energy management via DE-HHO hybrid metaheuristics. Comput. Mater. Contin. 84, 3, 4729–4754. https://doi.org/10.32604/cmc.2025.066138 (2025).
Article  Google Scholar 
Xie, J. et al. ‘An enhanced snow ablation optimizer for UAV swarm path planning and engineering design problems’, Heliyon, 10(18), e37819, (2024). https://doi.org/10.1016/j.heliyon.2024.e37819
Khairalla, A. G. et al. ‘Enhanced control strategy and energy management for a photovoltaic system with hybrid energy storage based on self-adaptive Bonobo optimization’, front. Energy Res., 11, (2023).
Khairalla, A., Elrefaie, H. B., Kotb, H. & AboRas, K. ‘Leveraging an innovative puma algorithm for the optimisation of energy management in a microgrid, incorporating RES and energy storage devices’, in 2025 15th International Conference on Electrical Engineering (ICEENG), 1–6. (2025).
Durairasan, M., Ramprakash, S. & Balasubramanian, D. System modeling of micro-grid with hybrid energy sources for optimal energy management—A hybrid elephant herding optimization algorithm‐adaptive neuro fuzzy inference system approach. Int. J. Numer. Model. 34, 6 (2021).
Article  Google Scholar 
AboRas, K. M., Fouad, A., Kotb, H., ElRefaie, H. B. & Yakout, A. H. Enhancing frequency stability in power systems through DO-optimized FOPI-PIDA-controlled STATCOM for wind energy integration. Alex Eng. J. 129, 192–204 (2025).
Article  Google Scholar 
Li, L. L. et al. Microgrid energy management system with degradation cost and carbon trading mechanism: A multi-objective artificial hummingbird algorithm. Appl. Energy. 378 (124853, 124853, ). https://doi.org/10.1016/j.apenergy.2024.124853 (2025).
Ji, B. X. et al. Phased optimization of active distribution networks incorporating distributed photovoltaic storage system: A multi-objective Coati optimization algorithm. J. Energy Storage. 91 (112093, 112093, ). https://doi.org/10.1016/j.est.2024.112093 (2024).
Behera, P. K. & Pattnaik, M. Performance enhancement of a modified filtration based control scheme for hybrid energy storage system in low voltage direct current microgrid. J. Power Sources. 610, 234698 (2024).
Article  CAS  Google Scholar 
Bocklisch, T. Hybrid energy storage systems for renewable energy applications. Energy Procedia. 73, 103–111 (2015).
Article  Google Scholar 
Guo, T., Liu, Y., Zhao, J., Zhu, Y. & Liu, J. A dynamic wavelet-based robust wind power smoothing approach using hybrid energy storage system. Int. J. Electr. Power Energy Syst. 116, 105579, 105579 (2020).
Article  Google Scholar 
Zhang, X. Z., Lu, Z. Y., Tan, C. Z. & Wang, Z. Y. Fuzzy adaptive filtering-based energy management for hybrid energy storage system. Comput. Syst. Sci. Eng. 36 (1), 117–130 (2021).
Article  CAS  Google Scholar 
Ni, F. et al. Jun., Enhancing resilience of DC microgrids with model predictive control based hybrid energy storage system, International Journal of Electrical Power & Energy Systems, 128, 106738, (2021).
van Jaarsveld, M. J. & Gouws, R. An active hybrid energy storage system utilising a fuzzy logic rule-based control strategy. World Electr. Veh. J. 11, 2, 34 (2020).
Article  Google Scholar 
Mahmoudi, S. M., Maleki, A. & Rezaei Ochbelagh, D. Optimization of a hybrid energy system with/without considering back-up system by a new technique based on fuzzy logic controller. Energy Convers. Manag. 229, 113723, 113723 (2021).
Article  Google Scholar 
SadolaluBoregowda, V. K. et al. Enhancing DC microgrid performance with fuzzy logic control for hybrid energy storage system. Signal. Image Video Process. 18 (5), 4505–4514 (2024).
Article  Google Scholar 
Ibrahim, O. et al. Fuzzy logic-based particle swarm optimization for integrated energy management system considering battery storage degradation. Results Eng. 24 (102816), 102816 (2024).
Article  Google Scholar 
Rostami, S. M. R. & Al-Shibaany, Z. Intelligent energy management for full-active hybrid energy storage systems in electric vehicles using teaching–learning-based optimization in fuzzy logic algorithms. IEEE Access. 12, 67665–67680 (2024).
Article  Google Scholar 
Rezk, H. & Fathy, A. Combining proportional integral and fuzzy logic control strategies to improve performance of energy management of fuel cell electric vehicles. Int. J. Thermofluids. 26, 101076 (2025).
Article  CAS  Google Scholar 
Benzaouia, S., Rabhi, A., Benzaouia, M., Oubbati, K. & Pierre, X. Design, assessment and experimental implementation of a simplified FLC for hybrid energy storage system. J. Energy Storage. 84, 110840 (2024).
Article  Google Scholar 
Cabrane, Z., Ouassaid, M. & Maaroufi, M. Battery and supercapacitor for photovoltaic energy storage: a fuzzy logic management’, IET renew. Power Gener. 11, 8, 1157–1165 (2017).
Article  Google Scholar 
Sruthi, S., Karthikumar, K. & Chandrasekhar, P. An efficient power management control strategy for grid-independent hybrid renewable energy systems with hybrid energy storage: hybrid approach. J. Energy Storage. 95 (112325), 112325 (2024).
Article  Google Scholar 
Albasheri, M. A., Bouchhida, O., Soufi, Y., Cherifi, A. & Mujammal, M. A. H. Supervisory energy management strategy based-fuzzy logic for a DC microgrid. Int. J. Dyn. Contr. 13, 1 (2025).
Google Scholar 
Wang, C., Liu, R. & Tang, A. Energy management strategy of hybrid energy storage system for electric vehicles based on genetic algorithm optimization and temperature effect. J. Energy Storage. 51, 104314, 104314 (2022).
Article  Google Scholar 
Zehra, S. S., Ur Rahman, A. & Ahmad, I. Fuzzy-barrier sliding mode control of electric-hydrogen hybrid energy storage system in DC microgrid: Modelling, management and experimental investigation. Energy (Oxf). 239 (122260), 122260 (2022).
Article  Google Scholar 
Amiri, M. H., Mehrabi Hashjin, N., Montazeri, M., Mirjalili, S. & Khodadadi, N. Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm. Sci. Rep. 14 (1), 5032 (2024).
Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 
Guentri, H., Allaoui, T., Mekki, M. & Denai, M. POWER management and control of A PHOTOVOLTAIC system with hybrid battery-supercapacitor energy storage BASED ON HEURISTICS METHODS. J. Energy Storage. 39 (102578), 102578 (2021).
Article  Google Scholar 
Kollimalla, S. K., Mishra, M. K. & Narasamma, N. L. Design and analysis of novel control strategy for battery and supercapacitor storage system. IEEE Trans. Sustain. Energy. 5 (4), 1137–1144 (2014).
Article  ADS  Google Scholar 
Han, Y., Li, H., Shen, P., Coelho, E. A. A. & Guerrero, J. M. Review of active and reactive power sharing strategies in hierarchical controlled microgrids. IEEE Trans. Power Electron. 32 (3), 2427–2451 (2017).
Article  ADS  Google Scholar 
Elkholy, A. & Abou El-Ela, A. A. ‘Optimal parameters estimation and modelling of photovoltaic modules using analytical method’, Heliyon, 5, 7, e02137, (2019).
Zaouche, F., Rekioua, D., Gaubert, J. P. & Mokrani, Z. Supervision and control strategy for photovoltaic generators with battery storage. Int. J. Hydrogen Energy. 42, 30, 19536–19555 (2017).
Article  ADS  CAS  Google Scholar 
Ramu, S. K., Vairavasundaram, I., Palaniyappan, B., Bragadeshwaran, A. & Aljafari, B. Enhanced energy management of DC microgrid: artificial neural networks-driven hybrid energy storage system with integration of bidirectional DC-DC converter. J. Energy Storage. 88 (111562), 111562 (2024).
Article  Google Scholar 
Albasheri, M. A., Bouchhida, O., Soufi, Y. & Cherifi, A. ‘Enhanced vector control of induction motor by fuzzy logic controller’, in 2nd International Conference on Electrical Engineering and Automatic Control (ICEEAC), 2024. (2024).
Gorji, S. A., Sahebi, H. G., Ektesabi, M. & Rad, A. B. Topologies and control schemes of bidirectional DC–DC power converters: an overview. IEEE Access. 7, 117997–118019 (2019).
Article  Google Scholar 
Xu, X., Casale, E., Bishop, M. & Oikarinen, D. G. ‘Application of new generic models for PV and battery storage in system planning studies’, in IEEE Power & Energy Society General Meeting, 2017, 1–5. (2017).
Ashraf, H., Draz, A., Elmoaty, A. M. & El-Fergany, A. A. Precise modelling of commercial photovoltaic cells/modules of different technologies using hippopotamus optimizer. Energy Convers. Manag. 325, 119382 (2025).
Article  CAS  Google Scholar 
Olaleye, T. A., Olatomiwa, L., Longe, O. M. & Jack, K. E. An energy management scheme for hybrid energy system using fuzzy logic controller. Niger J. Technol. Dev. 20 (1), 66–78 (2023).
Article  Google Scholar 
Mishra, S., Prusty, R. C. & Panda, S. Design and analysis of 2dof-PID controller for frequency regulation of multi-microgrid using hybrid dragonfly and pattern search algorithm. J. Control Autom. Electr. Syst. 31 (3), 813–827 (2020).
Article  Google Scholar 
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Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria, 21544, Egypt
Hossam Kotb, Ahmed G. Khairalla, Hesham B. ElRefaie & Kareem M. AboRas
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Hossam Kotb: Conceptualization, Methodology, Supervision, Writing – Review & Editing. Ahmed G. Khairalla: Software, Validation, Formal Analysis, Writing – Original Draft. Hesham B. ElRefaie: Investigation, Data Curation, Visualization. Kareem M. AboRas: Resources, Conceptualization, Methodology, Supervision, Writing – Review & Editing. All authors contributed to the discussion of results and approved the final manuscript.
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Kotb, H., Khairalla, A.G., ElRefaie, H.B. et al. Enhanced power management in PV-Integrated hybrid energy storage systems using fuzzy 2DOF-PI control optimized by hippopotamus algorithm. Sci Rep 16, 9200 (2026). https://doi.org/10.1038/s41598-026-40106-4
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National Solar Energy Federation of India (NSEFI), an India-based solar industry body, has said India is set to become the world’s second-largest solar market in 2026 by annual installations. According to the statement, India has installed 50 GW of additional solar capacity in just 14 months, raising total installed solar capacity to 150 GW. The statement has also added that the first 50 GW had taken 11 years to materialize, while the rise from 100 GW to 150 GW had taken nearly three years. NSEFI said that the solar capacity is expected to reach to 280-300 GW to help India attain its 500 GW non-fossil capacity target by 2030, with yearly additions approaching 50 GW. It added that PM Surya Ghar, the upcoming PM KUSUM 2.0, floating solar policies, and demand associated with the National Green Hydrogen Mission are supporting this growth. The industry body further said DRE and C&I solar are likely to lead expansion during the next three years.

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German research organisation Fraunhofer ISE has launched a new consultancy spin-off—NEXUS GreenTech—to support companies active in the solar PV industry.
NEXUS GreenTech was founded at the end of March, and is headquartered in Freiburg, Germany. The new company is led by Dr Jochen Rentsch, Dr Sebastian Nold and Dr Nico Wöhrle, who were previously working with the PV Technology Transfer unit at Fraunhofer ISE, and nave more than 60 years of cumulative experience in PV research, development and technology.
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Rentsch said that the spin-off aims to address “a great need for consulting” in an increasingly complex global PV industry.
“During our collaboration with PV companies in the field of technology transfer, we repeatedly noticed that many of the inquiries were not about a research question in the strict sense,” said Rentsch. “At the same time, there is a great need for consulting: Which cell technology should I choose, which suppliers are available, which factory layout makes sense—to name just a few issues.”
Fraunhofer ISE said that the company would focus on several key areas, including technical and commercial due diligence, feasibility studies, layout planning for factories and technology consulting. NEXUS GreenTech will use scientific methods from Fraunhofer ISE, secured through cooperation and licensing agreements.
The spin-off will start work with US solar cell manufacturer Talon PV, and support “the establishment and operation” of a new production line of tunnel oxide passivated contact (TOPCon) cells. Last year, Talon PV CEO Adam Tesanovich spoke to PV Tech Premium about some of the legal barriers that have impeded domestic TOPCon production in the US, and how the company aims to overcome them. In the months since, the company signed a wafer supply agreement with German solar wafer manufacturer NexWafe.
This is also not the first collaboration between Talon PV and Fraunhofer ISE. Last year, the latter announced plans to build a pilot TOPCon cell production line in Germany to support the former’s development of its own manufacturing capacity in the US, and the launch of NEXUS GreenTech follows on from this cooperation.

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Scientific Reports volume 15, Article number: 40405 (2025) Cite this article
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The concept of Islanded Hybrid Power System (IHPS) has attracted considerable interest lately, especially for energizing remote or energy-poor locations. IHPS are more dependable and cost-effective alternatives to systems using only one energy source when properly constructed. IHPS configuration, including Diesel Engine Generator (DEG), Photovoltaic (PV) systems, and Battery Storage (BATT) elements, are desirable for islanded systems about price and dependability. IHPS mostly use Renewable Energy Sources (RES) for power production, which is variable. Consequently, these variations often make it difficult for traditional control systems to maximize efficiency across various operating environments. The current research discusses the requirement for more effective frequency control in IHPS by suggesting a Model Reference Adaptive Control-Fuzzy Proportional Integral based Whale Optimization Algorithm (MRAC-FPI-WOA) controller. The proposed controller can efficiently manage a range of disturbances by dynamically adjusting its control techniques. The current research conducts an evaluation study comparing the effectiveness of the suggested MRAC-FPI-WOA controller against FPI-WOA, PI-WOA, and PI-PSO controllers. The key evaluation criteria are the ability to maintain stability in frequency within the IHPS and the effectiveness of power production in the overall system. The results demonstrate the superior performance of the MRAC-FPI-WOA controller across diverse operational scenarios. Notably, during a three-phase fault at Bus2, the MRAC-FPI-WOA controller achieves significant performance enhancements over the PI-PSO controller, with reductions of 59.05% in maximum overshoot (%(:{text{M}}_{text{p}})), 72.83% in maximum undershoot (%(:{text{M}}_{text{u}text{s}})), 32.07% in settling time ((:{text{T}}_{text{s}})), and 34.81% in the integral of time-weighted absolute error (ITAE). A similar trend is observed during a three-phase fault at the tie-line, where the MRAC-FPI-WOA controller yields improvements of 57.47% in %(:{text{M}}_{text{p}}), 79.36% in %(:{text{M}}_{text{u}text{s}}), 40.9% in (:{text{T}}_{text{s}}), and 78.08% in ITAE. Furthermore, the controller exhibits exceptional dynamic responsiveness to ramp variations in solar radiation, substantially reducing %(:{text{M}}_{text{p}}:)by 96.72%, %(:{text{M}}_{text{u}text{s}}) by 95.24%, (:{text{T}}_{text{s}}:)by 22.79%, and ITAE by 89.69%. Additionally, it demonstrates robust adaptability to random solar radiation fluctuations, consistently optimizing transient response with reductions of 96.63% in %(:{text{M}}_{text{p}}), 99.58% in %(:{text{M}}_{text{u}text{s}}), 22.07% in (:{text{T}}_{text{s}}), and 95.23% in ITAE.
Sustainable energy solutions are being widely adopted in modern power systems to reduce environmental impact and enhance grid performance. While they improve efficiency, voltage stability, and ecological benefits, their excessive integration can challenge grid operation, protection, and control¹. A microgrid (MG) represents a localized power network that integrates renewable generation sources (e.g., photovoltaic arrays, wind turbines) with energy storage components (e.g., battery banks) to form a self-contained electrical system². Hybrid Power System (HPS) operation can switch between two key modes: independent (islanded) and grid-tied operation. IHPS are considered the most effective approach for supplying electricity to remote and rural areas due to their technical feasibility and cost-efficiency³. The intermittent and unpredictable nature of RES in HPS can cause voltage instability and oscillations, potentially affecting connected loads. To ensure system reliability and the quality of electrical supply, an effective control strategy must be developed, allowing the HPS to operate efficiently despite uncertainties in weather conditions and load variations during the system runs in real-time⁴. As a result, IHPS operations necessitate BATT to retain surplus energy generated by the HPS, ensuring power availability when production is insufficient to meet demand. This study examines the dynamic performance of IHPS under various operating conditions.
The efficient control and management of HPS require advanced strategies and algorithms to optimize the utilization of RES, manage BATT, and ensure a stable and reliable power supply⁵. One of the most critical aspects of HPS operation is frequency stability, which is essential for maintaining high-quality electricity for connected loads. Fluctuations in frequency arise from variations in power generation and consumption, highlighting the necessity for robust frequency regulation mechanisms to maintain HPS stability and performance⁶. Several approaches can be applied to frequency regulation in IHPS. One widely used method involves BATT to compensate for fluctuations in RES generation, ensuring a steady and secure system frequency. Other techniques include advanced control strategies and demand-side management approaches. Extensive research has explored various control methodologies for regulating the operation of standalone hybrid MG⁷. A control strategy proposed in⁸ focuses on biogas-based MG, allowing the system to increase or decrease power generation in response to disturbances caused by fluctuations in RES input or load demand. However, a key drawback of this approach is its inability to respond swiftly to sudden changes, potentially leading to transient instability. Additionally, the controller may lack robust fault detection and isolation capabilities, and its effectiveness could decline when scaling up or integrating with larger power grids. To enhance frequency regulation and stability, Ref⁹ suggests using an adaptive active power droop controller along with voltage setpoint adjustment in IHPS. These control mechanisms aim to improve the overall performance of HPS systems. Furthermore, Ref¹⁰ explores a control technique for BATT designed to mitigate frequency variations and enhance the dynamic response of IHPS. To achieve superior frequency stability during transient disturbances, they propose the use of a Piecewise Linear-Elliptic (PLE) droop characteristic in BATT control systems. This control characteristic enables a faster equilibrium between consumption and power generation, leading to improved frequency regulation in HPS. However, while the PLE controller can reduce frequency variations, it does not fully eliminate them. Additionally, it may be less effective when load demand is lower than power generation, potentially causing sudden fluctuations in BATT output power. In¹¹, a voltage regulation strategy for IHPS incorporating PV and BATT was examined. Ref¹² deals with the control of the Vehicle Cruise Control System (VCCS) based on a Model Predictive Controller (MPC) in parallel with the conventional PID controller. The study evaluates the technique’s effectiveness in improving HPS performance, but it does not fully address key challenges related to islanded mode regulations, frequency stability, protection settings, power management, and load diversity handling in HPS.
Optimization algorithms inspired by biological and natural phenomena are classified as metaheuristic approaches. Unlike traditional mathematical optimization techniques, which often struggle with complex search spaces, metaheuristic algorithms effectively explore potential solutions to high-dimensional, nonlinear, and multi-modal optimization problems. As a result, techniques like the WOA, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) have gained widespread attention across various fields. These techniques are commonly used to optimize system performance by fine-tuning control parameters in advanced control systems, including Proportional-Integral (PI), Proportional-Integral-Derivative (PID), Fuzzy Proportional-Integral (FPI), Fractional-Order PI (FOPI), and Fuzzy-Fractional Order PID (FFOPID) controllers.
Recent studies highlight innovative bio-inspired optimization techniques for power systems. In¹³ Bio-Dynamic Grasshopper Optimization Algorithm (BDGOA) is used to optimize Tilt-Derivative with N-filter plus PI controllers for frequency/tie-line oscillation damping. In¹⁴ Diligent Crow Search Algorithm (DCSA) is employed for solar cell parameter identification to maximize PV output. In¹⁵ Hybrid Adaptive Ant Lion Optimization (HAALO) with PI/FOPID controllers is developed to enhance Switched Reluctance Motor performance through adaptive mutation and torque ripple reduction. In¹⁶ BDGOA is applied for precise parameter estimation across five solar module technologies. In¹⁷ Crow-Search Algorithm (CSA) is employed to optimize Type-2 Fuzzy Cascade (T2F-CPIF) controllers for robust frequency/tie-line error mitigation in hybrid systems under contingency scenarios. In¹⁸ WOA is utilized to enhance Fuzzy Cascade PD-PI controllers, substantially improving microgrid transient response during operational disturbances. For secondary frequency regulation. In¹⁹ Improved Salp Swarm Optimization (I-SSO) tuned Type-II Fuzzy PID controller is implemented to maintain nominal frequency and tie-line power despite uncertainties. Complementing these approaches. In²⁰ advanced Sine Cosine Algorithm (a-SCA) is implemented to optimize the Fractional-Order Fuzzy for precise generation-demand balancing in fluctuating conditions. In²¹ Coati Optimization Algorithm (COA) was implemented to optimize the parameters of Fuzzy-PI (FPI) and conventional PI controllers, significantly improving the frequency regulation performance in a two-area power system. In²² modified Sea-horse Optimization (SHO) method is developed for tuning Proportional-Integral-Derivative-Tilt (PID-T) controllers in renewable-integrated multi-area systems. In²³ SHO is enhanced to optimize Model Predictive Control (MPC), PID, Fractional order proportional integral derivative (FOPID), and Tilted Integral Derivative (TID) controllers for complex power networks. For cyber-resilient operation. In²⁴ Chaos Quasi-Oppositional SHO (CQOSHO) proposes to tune a novel Cascaded tilted-FO derivative with filter ((:{text{C}text{P}text{D}}^{{upmu:}})F − TI) controller with deep learning capabilities. Complementing these advances. In²⁵ Opposition-based SHO (OSHO) is developed for hybrid systems, optimizing TID-MPC controllers to manage renewable penetration and virtual inertia challenges. In²⁶ Dragonfly Search Algorithm (DSA) is employed to optimize an Adaptive Fractional Order PI (AFOPI) controller for precise motor speed regulation. In²⁷ DSA is utilized for tuning a novel cascaded PI-(FOP + PD) structure to mitigate frequency fluctuations in power systems. Complementing these approaches. In²⁸ Tunicate Search Algorithm (TSA) is implemented to enhance transient stability in hybrid grids through optimized Tilt Fractional Order PID (TFOPID) control. These developments showcase the effectiveness of bio-inspired optimization in addressing diverse control challenges across electromechanical and power system applications.
The proposed WOA has demonstrated remarkable efficacy across diverse domains, especially in enhancing control system configurations²⁹. For instance, when applied to PID controllers, WOA-optimized systems achieve rapid transient responses, minimized steady-state deviations, and enhanced oscillation damping in contemporary power grids, outperforming GA and Artificial Bee Colony (ABC) approaches³⁰. In renewable energy applications, WOA-driven Fractional-Order Proportional-Integral Controllers (FOPIλ) excel within sensor-free speed control applications for solar-fed permanent-magnet brushless DC motors. These systems surpass Bat Algorithm (BA) and Grey Wolf Optimizer (GWO) implementations by reducing tracking errors and shortening convergence intervals³¹. Similarly, WOA-enhanced FFOPID controllers integrated into active vehicle suspension models significantly attenuate driver vibrations relative to Fractional-Order PID (FOPID) and PSO-tuned counterparts³². Furthermore, WOA-based Maximum Power Point Tracking (MPPT) techniques applied to Proton Exchange Membrane Fuel Cells (PEMFC) dynamically adapt to electrolyte hydration fluctuations, securing optimal power extraction with greater efficiency than Perturb-and-Observe (P&O), Fuzzy Logic Controller (FLC), and PSO methodologies³³. These advancements underscore WOA’s versatility in resolving nonlinear, multi-variable challenges across energy and mechanical systems.
The Research gap of this study includes:
Limitations of Traditional Controllers: Existing IHPS studies rely on PI, PID, FOPI, and FPI controllers, which face challenges in handling nonlinear system dynamics and severe grid disturbances. These controllers show slow transient recovery, increased frequency overshoot, and prolonged settling times, compromising system stability.
Inadequate Handling of Diverse Disturbances: Prior research does not sufficiently address the combined impact of gradual fluctuations (e.g., solar irradiance) and severe grid anomalies (e.g., three-phase faults, load shedding), causing instability in IHPS.
Lack of Adaptive Frequency Control: Many existing controllers do not adapt to varying renewable energy fluctuations and load changes, leading to poor frequency regulation and reduced system efficiency.
Deficiencies in Power Coordination and Scalability: Conventional methods do not effectively coordinate power generation, storage, and demand, limiting overall system reliability and scalability for real-world applications.
Underutilization of Intelligent Optimization in Control Tuning: Automated gain calibration for frequency controllers is underdeveloped, and no framework integrates nonlinear adaptive control with swarm-based optimization for dynamic tuning.
Need for an Advanced Control Strategy: A novel approach is crucial for optimizing frequency regulation, transient stability, and operational robustness in IHPS. The integration of MRAC-FPI-WOA gives a promising answer by enabling adaptive tuning in real time and intelligent power coordination in IHPS.
The contributions of this study include:
Methodological innovations:
Investigates the transient behavior and operational robustness of integrated PV-BATT-DEG power systems under both gradual environmental perturbations (e.g., incremental solar irradiance shifts) and severe grid anomalies (e.g., three-phase faults, abrupt load shedding).
Proposes a load frequency control to synchronize power generation, storage, and demand in IHPS. This strategy strengthens inter-component coordination, adapts to real-time grid dynamics, and ensures voltage/frequency stability during fluctuating renewable outputs and load transitions.
Develops a non-linear adaptive controller (MRAC-FPI-WOA). This innovation optimizes transient frequency recovery across diverse operating regimes, outperforming PI-PS0, PI-WOA and FPI-WOA controllers in damping oscillations and minimizing settling times.
Enhances the technical feasibility of large-scale renewable adoption by mitigating frequency volatility in IHPS. This advancement aligns with global sustainability agendas, reducing fossil dependency while improving energy distribution reliability in decentralized grids.
Algorithmic implementations:
Proposes Beta-based MPPT technique, which enhances the tracking accuracy and dynamic performance of the PV system by adaptively controlling power extraction based on a novel intermediary variable (β), rather than relying solely on conventional power change methods. The WOA is integrated with the beta-based MPPT controller to enhance the total efficiency of the PV system.
Leverages the PSO and WOA to automate gain calibration for proposed controllers. WOA effectively resolves nonlinearities and component interdependencies, ensuring the best dynamic response in variable operating conditions.
Simulation/experimental findings:
Demonstrates the MRAC-FPI-WOA’s superiority through rigorous metrics: lower maximum overshoot (%(:{text{M}}_{text{p}})), and trough undershoot (%(:{text{M}}_{text{u}text{s}})) at lower frequencies, faster settling time ((:{text{T}}_{text{s}})), and a decrease in the integral of time-weighted absolute error (ITAE) in contrast to benchmarks. These results validate its capability to sustain grid stability during both minor and catastrophic disturbances.
This paper’s remaining sections are arranged as follows: This paper systematically explores the design and control of IHPS components PV systems, DEG, and BATT in “Modeling of islanded hybrid power system“, proceeding to evaluate four frequency control strategies, including MRAC-FPI-WOA, FPI-WOA, PI-WOA, and PI-PSO controllers in “Frequency Control“. A detailed simulation-based analysis in “Results and discussion” compares controller performance under seven scenarios, including three-phase faults, step/ramp/random solar irradiance fluctuations, as well as abrupt load changes and composite disturbances. Cases 3 (step irradiance) and 6 (sudden load shift) are tested concurrently to assess robustness under hybrid stresses. The study concludes in “Conclusions” that the MRAC-FPI-WOA controller, enhanced by metaheuristic tuning, outperforms conventional methods in maintaining frequency stability and power quality across all disturbances, underscoring its potential to enhance HPS resilience in real-world applications characterized by renewable intermittency and operational uncertainties.
This research undertaking centers its analytical scope on the architectural design and functional dynamics of Alternating current (AC) IHPS, integrating multiple distributed energy resources, including DEG, PV, AC consumer loads, and advanced BATT solutions. Figure 1 delivers a refined schematic overview of the IHPS infrastructure, emphasizing the interconnection of the DEG to the primary AC distribution backbone through sophisticated power electronic interfaces. These components perform dual critical functions: harmonizing the phase and frequency characteristics of disparate AC power sources while enabling efficient conversion of Direct Current (DC) electricity harvested from solar panels into HPS-compatible alternating current waveforms. The BATT incorporates a bidirectional power conversion apparatus, engineered to transition seamlessly between AC-to-DC operational modes during energy accumulation cycles and DC-to-AC modes during discharge phases. This dual functionality not only stabilizes the HPS against voltage fluctuations and transient load imbalances but also enhances operational flexibility during system upkeep or component servicing. This comprehensive framework underscores HPS’s resilience in maintaining uninterrupted power delivery while accommodating diverse energy inputs and dynamic load profiles.
Block diagram of the proposed IHPS.
This section introduces a detailed and robust simulation framework designed to be a high-performance PV system. The system architecture encompasses several critical elements: a 100-kilowatt solar panel array, a step-up DC-DC converter, a power inversion unit, and a voltage adjustment transformer. A methodically structured schematic diagram and computational model, illustrated in Fig. 2, offer a comprehensive and logically organized visualization of the entire configuration. Sunlight is harvested by a solar array and converted into DC electricity. To enable compatibility with standard power distribution networks, this DC output must undergo conversion to AC. This critical transition is eased by the inverter module, which transforms the unidirectional electrical flow into a three-phase AC output synchronized with grid specifications. Subsequently, a voltage-elevating transformer amplifies the AC voltage to match the grid’s operational requirements, ensuring seamless energy transfer.
Each component operates synergistically: the Boost converter optimizes the DC voltage from the solar panels to maximize efficiency, the inverter ensures waveform compatibility with HPS standards, and the transformer bridges voltage disparities to enable stable power injection. This integrated approach highlights the system’s capability to efficiently harness, process, and deliver renewable energy while adhering to technical and operational benchmarks for grid integration^34,35.
Schematic of a Solar PV System.
Various mathematical representations describing the functionality and efficiency of solar panels have been extensively documented in previous studies. For real-time simulation, it is necessary to develop an equivalent circuit model of PV cells. Among the different approaches, the single-diode model is the most widely adopted by researchers. This circuit configuration comprises, at a minimum, four key elements: a photocurrent source ((:{I}_{ph})), a diode (D), a shunt resistance ((:{R}_{sh})), and a series of resistance ((:{R}_{ser})). Based on the equivalent single-diode model of a PV cell depicted in Fig. 3, the output current ((:{I}_{out})) can be expressed mathematically in the following way^36,37.
Where(::left({N}_{P}right)) is the number of PV cells arranged in parallel, ((:{I}_{rs})) is The PV cell’s reverse leakage current, (q) is the electric charge of an electron,(:{(V}_{out})) is the cell’s output voltage, (A) is the diode ideality factor, (K) is the Boltzmann constant, (T) is the temperature measured in Kelvin, (:{(N}_{S})) is the total PV cells wired in a series connection,(:{:(text{I}}_{text{s}text{c}})) is the short-circuit current, (:{(k}_{i})) is the short circuit current factor, (:left({T}_{r}right)) is the cell reference temperature and (E) is the solar irradiance.
Schematic representation of a basic diode-based model used for PV solar cells.
Figure 4(a) and Fig. 4(b) depict the I-V and P-V characteristics of the PV cell, derived from a MATLAB-based computational model. These findings provide critical insights into the operational dynamics of the solar module under fluctuating irradiance scenarios, revealing how variations in solar intensity influence electrical output characteristics such as Maximum Power Point (MPP), open-circuit voltage (:{(V}_{oc}), and (:{I}_{sc})). The simulations show the nonlinear relationship between irradiance levels and energy conversion efficiency, emphasizing the importance of adaptive control strategies for optimizing solar harvesting in real-world environmental conditions.
(a) I-V curve and (b) P-V characteristics of solar cells at varying irradiation levels.
A basic DC-DC boost converter is employed to deliver power from the PV to the DC link and the inverter once the matching condition between them is met. This matching is achieved by applying a suitable duty cycle (ranging between 0 and 1). The converter’s switching element, typically an IGBT, is regulated using a PWM signal. Figure 5 displays the Simulink model layout of the boost converter. The mathematical relationships governing the converter’s input and output parameters are expressed through the following Eqs^35,36.
Here, the input and output voltages, along with the duty cycle, are represented as(:{::(V}_{o:}), (:{V}_{in}), and D), respectively. The roles of the boost converter’s inductor (L) and capacitor (C) elements are specified as follows^35,36:
Where ((:f)) is the frequency, (:(varDelta:I) and (:varDelta:V)) are the current and voltage ripple.
Circuit diagram of a boost converter.
The β-MPPT method involves observing an intermediate variable called ((:beta:)), rather than directly tracking power variations, as outlined in Eqs. (7) and (8)^36,37.
Here, (:left({I}_{pv}right)) is the output current, (C) is the diode constant, and (N) is the total count of solar cells contained in the module.
This method uses a hybrid step-size strategy, applying a variable step during dynamic changes and a fixed step during stable operating conditions. As outlined in Fig. 6, the algorithm begins by continuously observing voltage and current values to compute the intermediary beta parameter. If the calculated beta lies within a designated threshold range ((:{beta:}_{min}) to (:{beta:}_{max})), the system is in a steady state, and a fixed step is applied. If beta falls outside this range, the algorithm identifies a transient phase and switches to a P&O approach. In this stage, the variable step size, denoted as ΔD, is adjusted based on a reference parameter called (:{(beta:}_{g})), which is defined mathematically in Eq. (9)^36,37.
Where (F) is the scaling factor.
Flow chart of β-MPPT.
The WOA discussed in Sect. 4 is integrated with the beta-based MPPT controller to enhance the total efficiency of the PV system. Within this hybrid framework, the scaling factor (F) is essential for adaptively regulating the step size (∆D) during the dynamic response phase of the Beta MPPT method. Selecting the perfect value for (F) is key to achieving:
Rapid tracking of the Maximum PowerPoint (MPP).
Minimized fluctuations during steady-state operation.
Enhanced performance across various levels of sunlight and temperature conditions.
Since the scaling factor (F) significantly affects MPPT efficiency but does not have an exact analytical expression, a metaheuristic optimization method can be applied to find its best value. The WOA offers a reliable control mechanism across various load scenarios and system parameters. This enhances both the flexibility and resilience of the control framework, ensuring that the Beta MPPT method consistently performs at its best under changing operational conditions. The objective function aims to find the ideal value of the scaling factor in a way that enhances power extraction efficiency (η) while simultaneously reducing both convergence time (CT) and Steady-State Oscillations (SSO). The goal is to minimize J(F) and obtain the best value of the scaling factor as outlined in Fig. 7.
Where:
MPPT Efficiency (η) is expressed as the ratio of the power obtained using the MPPT method ((:{P}_{MPPT})) to the ideal power ((:{P}_{ideal})).
(SSO) is the Root Mean Square (RMS) value of the power fluctuations in the steady state.
CT refers to the duration needed for the system to reach 98% of the (:{text{P}}_{text{i}text{d}text{e}text{a}text{l}}).
(W₁, W₂, and W₃ ) are the weighting coefficients assigned to balance the impact of each parameter in the optimization process.
Flow chart of the WOA to calculate the best value of the scaling factor (F).
As illustrated in Fig. 8, the control framework of the voltage source inverter (VSI) includes two inner loops for managing current and two outer loops for managing voltage. The d-axis current ((:{text{I}}_{text{d}})) controls active power, which directly influences the DC bus voltage. On the other hand, controlling the q-axis current ((:{text{I}}_{text{q}})) allows for the regulation of reactive power, thus stabilizing the AC load voltage. The PI controller is employed to evaluate and enhance the dynamic response of the external voltage regulation loops on the DC and AC sides^3,21. The mathematical expressions governing the VSI voltage are outlined in Eq. (11). To operate in the (dq) rotating reference frame (synchronous frame), the original three-phase (abc) signals are converted using transformation matrices, as described in Eq. (12).
Assume that (:({V}_{as}), (:{V}_{bs}), (:{V}_{cs})​) are the phase voltages produced by the VSI, and (:{(I}_{as}), (:{I}_{bs}), (:{I}_{cs})) ​ correspond to its output currents. The filter’s resistance and inductance are denoted by (:{(R}_{f}) and (:{L}_{f})) respectively. (:{(V}_{aL}), (:{V}_{bL}), (:{V}_{cL}))​ are the voltages across the connected load. In the synchronous dq reference frame, (:({V}_{dqs}), (:{V}_{dqL}), (:{I}_{dqs})) ​ are the inverter’s output voltages, the load-side voltages, and the inverter output currents, respectively. According to the described approach, the control of reactive power is managed through the q-axis current component, as detailed in Eq. (13), while the regulation of active power is managed through the d-axis current, as specified in Eq. (14)^3,21.
Here, (:{(text{Q}}_{s})​ and (:{text{P}}_{s}):)are the delivered reactive and active power, respectively. The responses generated by the current controllers aligned with the d-axis and q-axis are computed using the expressions provided in Eqs. (15) and (16)^3,21.
VSI control.
The integration of electrochemical storage units, such as lithium-ion battery banks, plays a pivotal role in HPS incorporating variable RES like PV arrays. These storage systems address imbalances between electricity production and consumption that arise from rapid fluctuations in solar insolation. During periods of diminished solar generation, when PV output falls short of the inverter’s target power level, the battery discharges to supplement load requirements^38,39. Conversely, when PV generation exceeds demand, surplus energy is stored within the battery for next use. Solar installations inherently cease operation during nocturnal intervals due to the absence of sunlight^40,41. Here, BATT synergizes with DEG to enhance system reliability and cost-effectiveness compared to standalone DEG configurations, reducing fuel consumption and operational expenses.
The operational framework of the BATT, illustrated in Fig. 9, is governed by critical performance metrics including terminal voltage, energy capacity, and charge retention level State of Charge (SOC). The battery is mathematically represented as a tunable voltage source paired with an internal impedance component. Where (:{(text{C}}_{text{R}}) )​ is the rated capacity and (:left({text{I}}_{text{B}text{A}text{T}text{T}}right)) ​is terminal current flow. Additional governing equations account for electrochemical reactions, gas evolution phenomena, thermal dynamics, and voltage-current relationships. Key variables include (:{text{V}}_{text{B}text{A}text{T}text{T}}) ​ (battery terminal potential), (:{text{I}}_{text{R}}) ​ (internal reaction current), (:{text{I}}_{text{G}}) ​ (parasitic gassing current), and (:{text{T}}_{text{B}text{A}text{T}text{T}})​ (operating temperature).
Battery Model.
The battery management strategy enforces specific operational constraints to ensure safe and efficient usage. Firstly, it restricts both the charging and discharging power levels, ensuring they do not exceed the maximum threshold specified by Eq. (17). Secondly, as outlined in Eq. (18), it regulates the battery’s SOC, keeping it within acceptable boundaries to avoid risks associated with overcharging or excessive depletion^38,39,40,41.
In the proposed system, batteries are utilized to mitigate the effects of the intermittent nature associated with PV sources. Due to their high energy density, batteries can deliver power at nearly constant voltage when their charging and discharging cycles are appropriately managed. The modeled battery is integrated into the DC link through a bi-directional DC-DC converter, as illustrated in Fig. 10. This converter facilitates the charging and discharging of the battery while maintaining the DC link voltage at 500 volts. When the battery supplies power to the microgrid, the converter operates in boost mode; conversely, when it absorbs power from the grid or PV panels, it operates in buck mode. The control loop regulates the DC link voltage by adjusting the duty cycle of the bi-directional DC-DC converter. It continuously measures the DC link voltage, compares it to a reference value, and processes the error through a voltage mode compensator to determine the necessary duty ratio. This control approach is agnostic to the direction of power flow and generates appropriate switching signals for the buck and boost operations. As shown in Fig. 11, an intelligent controller determines the operational mode and transmits the control pulses to a designated semiconductor switch. The decision to operate the converter in a buck or boost mode is based on the command signal received from the HPS. In the absence of a regulation signal, the battery’s SOC determines whether the converter should operate in buck mode to facilitate charging.
Bi-directional DC-DC converter.
Battery controller.
The DEG assumes a crucial role as a backup power solution, particularly in scenarios where RES such as PV is insufficient due to intermittent availability or environmental factors. Additionally, the system activates in island mode when the main grid experiences instability, such as voltage sags, frequency deviations, or unforeseen disconnections. In this isolated operational state, the DEG autonomously sustains power supply to critical loads, preventing blackouts and enabling seamless transitions until grid conditions stabilize or renewable generation resumes. This dual functionality underscores the DEG’s importance in hybrid energy systems, bridging gaps between renewable intermittency and grid reliability while ensuring uninterrupted electricity access during emergencies^42,43.
The DEG system illustrated in Fig. 12 is composed of multiple interconnected elements designed to ensure reliable power generation and grid stability. At its core, the system includes a governor mechanism for the diesel engine, an excitation system, and a synchronous machine integrated with the engine. The governor operates through a closed-loop feedback control strategy, which continuously monitors and adjusts the engine’s rotational speed. By dynamically aligning the engine’s output with a predefined reference speed, the governor guarantees the stabilization of the electrical grid’s frequency, even under fluctuating load demands. This precision in speed regulation is critical for maintaining synchronization between the generator and the grid, thereby preventing disruptions in power quality.
Diesel Engine Generator model.
The primary objective of stabilizing an islanded AC HPS lies in regulating the electrical supply to preserve system frequency at its predefined operational standard. This process hinges on frequency stability management, which entails dynamically modulating generator output levels to equilibrate power consumption needs while sustaining consistent grid oscillations. Within such systems, the cumulative energy contribution from distributed resources—comprising DEG, PV, and BATT—must collectively satisfy load requirements, as expressed by the relationship:
Given the inherent variability of PV generation due to weather-dependent intermittency, this analysis prioritizes DEG as the primary actuator for frequency correction. The control framework compensates for deviations caused by fluctuating loads and PV generation by adaptively scaling DEG output. Conventional PI regulators remain widely adopted for such stabilization tasks, while FPI systems introduce rule-based adaptability, enhancing responsiveness to dynamic operational shifts. To address limitations in existing hybrid energy systems, this work proposes an MRAC-FPI-WOA framework, which synergizes adaptive reference tracking with fuzzy logic to optimize disturbance rejection across diverse instability scenarios.
PI-PSO, PI-WOA, and FPI-WOA architectures have proved efficacy in grid frequency management, yet the MRAC-FPI-WOA hybrid appears as a superior solution, using real-time parameter adaptation to maintain precision under abrupt load transitions, resource volatility, and compound disruptions. This innovation underscores the critical need for advanced control paradigms in modernizing HPS resilience against the uncertainties of renewable integration.
The study specifically examines the PI controller’s effectiveness in maintaining system frequency stability and enhancing proposed IHPS operational performance, utilizing a control law expressed as (Eq. 20), with particular focus on its PI controller dynamic response characteristics and stabilization capabilities under varying load conditions⁴.
This equilibrium enables accelerated convergence and superior precision compared to conventional optimization frameworks. By defining frequency control as an optimization problem, the ITAE performance metric can be minimized^44,45.
Where (t) is time, while e(t) is the deviation between (:{F}_{m}:)and (:{F}_{ref}).
The system configuration depicted in Fig. 13 presents the closed-loop control structure employing the PI-PSO controller. PSO is popular for its simplicity and fitness-based approach, effective for diverse optimization problems. However, it risks premature convergence due to declining swarm diversity. The methodology incorporates three fundamental components⁴⁶:
Individual Best ((:{:P}_{pest:})): The optimal solution encountered by particle (i) during its search history.
Global Best ((:{:g}_{pest:})): The most favorable solution discovered by the entire particle collective.
Dynamical Update Rules: Governing equations directing particle movement through the solution space.
The particle’s velocity vector is modified following Eq. (22), while its positional coordinates are recomputed via Eq. (23) through vectorial addition of the updated velocity to its prior location⁴⁷.
The PSO algorithm updates each particle’s velocity and position through three key components: (1) an inertia term ((:{:wv}_{id})) that preserves momentum from previous movements, (2) a cognitive component ((:{:r}_{1}{C}_{1}left({:P}_{pest,id}left(tright)-:{:X}_{iid}left(tright):right)))) that attracts particles toward their personal best positions ((:{P}_{pest,id})), and (3) a social component ((:{:r}_{2}{C}_{2}left({:g}_{pest,id}left(tright)-:{:X}_{id}left(tright):right)))) that guides particles toward the swarm’s global best solution ((:{g}_{pest,id})), where (w) represents the inertia weight, C₁ and C₂ are cognitive and social learning rates, respectively, and (r₁,r₂) are random numbers that maintain stochastic exploration. This balanced combination of individual experience (cognitive) and collective knowledge (social) enables effective search-space exploration while progressively converging toward optimal solutions. Figure 14 illustrates the algorithm’s operational flowchart⁴⁸.
PI-PSO Controller-Based Control System Structure.
PSO flowchart.
PI-WOA controller illustrated in Fig. 15, for frequency stabilization. By framing the controller tuning process as an optimization problem, WOA dynamically minimizes frequency deviations through iterative adjustments to the gain values, ensuring robust adaptability to grid disturbances. This hybrid approach synergizes the simplicity of PI control with the intelligence of bio-inspired optimization, enabling enhanced precision in frequency regulation for modern power networks characterized by intermittent renewable integration and complex load dynamics. The methodology aims to elevate grid resilience, reduce oscillations, and maintain nominal frequency stability under heterogeneous operating conditions.
PI-WOA Controller-Based Control System Structure.
WOA technique is a robust nature-inspired computational method modeled after the foraging strategies of humpback whales. It shows exceptional ability in addressing intricate optimization problems by harmonizing the search for novel solutions (exploration) with the refinement of existing ones (exploitation)^49,50.
Figure 16 illustrates the flowchart of the WOA, which can be mathematically expressed using the following Eqs^49,50.
The symbols X(t), (:{X}_{p}left(tright)), and (:{X}_{r}left(tright)), correspond to the position vectors of the whale, prey, and random whale, respectively. (t) is the current iteration. (A and C) are the coefficient vectors. Over the number of rounds, (a) constantly decreases linearly from 2 to 0. The random integer (l) is between − 1 and 1, the random vector (r) is between 0 and 1, the (p) is the probability number ε [0, 1], and the constant that determines the spiral logarithmic form is represented by (b). Figure 17 demonstrates the convergence behavior of the objective function for both optimization methods, with Table 1 detailing the corresponding algorithmic parameters and optimized PI controller gains obtained through WOA and PSO implementations.
WOA flow chart.
Convergence of the objective function.
This research explores the FPI-WOA controller, illustrated in Fig. 18, as a hybrid control strategy that merges essential aspects of both FLC and PI-WOA control frameworks, aiming to enhance the capabilities of the PI controller by incorporating the advantages of FPI control. FLC outperforms classical methods in complex power systems due to its adaptability to nonlinearities and uncertainties without precise modeling. They maintain robust performance amid variable conditions like renewable generation fluctuations and load changes. Their rule-based heuristic approach enables intuitive tuning using operational expertise rather than complex math. Additionally, they are less sensitive to parameter variations than PID controllers, making them ideal for real-world applications with drifting system parameters^2,20,21. As outlined in^51,52 the fuzzy inference process consists of three main phases. The first step, fuzzification, transforms precise input values into fuzzy variables within their respective fuzzy sets. In this study, two input Errors (E), depicted in Fig. 19(a), and a Change in Error (CE), illustrated in Fig. 19(b), along with one output, shown in Fig. 19(c), are represented through triangular membership functions. Each input and output is characterized through a set of seven linguistic levels: NB (Negative Big), NM (Negative Medium), PB (Positive Big), PM (Positive Medium), PS (Positive Small), NS (Negative Small), and ZO (Zero). At the fuzzy logic rule inference stage, decisions are formulated through the integration of aggregation and implication techniques within the framework of fuzzy inference rules. The fuzzy rules, detailed in Table 2, can be linguistically described as follows: If both error (E) and (CE) are categorized as (PB), then the corresponding output is also classified as (PB). The parameters of PI-PSO, PI-WOA, and FPI-WOA controllers are displayed in Table 3.
FPI-WOA Controller-Based Control System Structure.
The MFs (a) E, (b) CE, (c) ΔD.
To enhance the accuracy of the FPI-WOA controller, it has been integrated with MRAC to enhance its efficiency and adaptability. Implementing MRAC in IHPS offers significant benefits, particularly in managing the unpredictable nature of RES. By dynamically adjusting to changes in generation and load variations, MRAC strengthens frequency stability and voltage regulation, optimizing system performance through continuous tuning of control variables adjusted during real-time operating conditions. This integration results in a more robust and dependable energy system, enabling seamless RES integration while improving the overall efficiency and reliability of IHPS.
Significant applications include maintaining stable output voltage in DC-DC converters used in IHPS⁵³, implementing a tailored MIT-rule-driven MRAC for boost-type DC-DC converters⁵⁴, and improving conventional droop-based regulation in marine power systems⁵⁵. Additionally, MRAC has been applied in HPS to regulate the unified interphase power controller (UIPC)⁵⁶, and develop a fractional-order MRAC control strategy to stabilize voltage and current in multi-source power configurations using DC-DC converters⁵⁷. As depicted in Fig. 20, the MRAC-FPI-WOA controller consists of three main components: the FPI controller, the reference model, and the adjustment mechanism.
MRAC-FPI-WOA Controller-Based Control System Structure.
In this study, simulations were conducted using MATLAB Simulink as shown in Fig. 21 to introduce an MRAC-FPI-WOA controller designed to ensure frequency stability within the system while facilitating fundamental control processes. A comparative analysis was performed between the MRAC-FPI-WOA, FPI-WOA, PI-WOA, and PI-PSO controllers across various scenarios to analyze the controllers’ effectiveness. These scenarios are essential for a thorough assessment of performance. For example, Case 1, which focuses on a three-phase fault at Bus 2, offers insights into the system’s robustness across different network configurations. Case 2 analyzes a three-phase fault occurring at the center of the tie-line, further evaluating the system’s capacity to manage faults that impact multiple components at once. Additionally, Cases 3, 4, and 5 address fluctuations in solar radiation, including step changes, ramp changes, and random variations, respectively. These scenarios are crucial for understanding how dynamic solar input influences overall system performance, given the inherent variability of solar energy due to environmental factors. Case 6 introduces a rapid load change, testing the system’s responsiveness to sudden alterations in energy demand, a frequent challenge in practical applications. Finally, Case 7 merges Cases 3 and 6, running them simultaneously to evaluate how the system performs under various conditions of concurrent changes in solar radiation and load demands. This integrated approach offers a comprehensive understanding of how various disturbances interact and impact frequency regulation, ultimately informing more efficient design and control strategies for the proposed IHPS. This section presents an in-depth analysis that includes a variety of responses and numerical results, demonstrating the findings and implications of our research. The nominal specifications for the PV, DEG, BATT, and loads are detailed in Table 4.
MATLAB/Simulink model.
In this situation, a fault involving a three-phase short circuit at BUS 2 occurs, lasting 0.1 s. This fault starts at the terminals of the AC load after a time interval of 1 s and is identified by a fault resistance of 0.001 ohms. Figure 22(a) visually illustrates the output power generated by both the PV and DEG sources. Additionally, Table 5; Fig. 22(b) assess the overall efficiency of the system’s frequency by analyzing various control strategies, including the MRAC-FPI-WOA, FPI-WOA, PI-WOA and PI-PSO controllers. This evaluation considers several Key performance indicators like ITAE, (:{text{T}}_{text{s}}), %(:{text{M}}_{text{p}}), and %(:{text{M}}_{text{u}text{s}}:)during instances of three-phase faults. The findings from this case prove that the MRAC-FPI-WOA controller surpasses the PI-WOA, PI-PSO and FPI-WOA controllers in every evaluated aspect. Furthermore, Fig. 22(c) shows the voltage values existing in the system.
System behavior in case 1. (a) Output power of the sources, (b) System Frequency, (c) System Voltage.
In this case, a three-phase short circuit starts at the central point of the tie, lasting for a total duration of 0.11 s. This fault event starts at the terminals of the AC load after 2 s and shows fault resistance as low as 0.001 ohms. Figure 23(a) visually depicts the output power generated by both the PV and DEG sources. Meanwhile, Table 5; Fig. 23(b) provide an assessment of the system’s efficiency by examining various control methodologies, including the MRAC-FPI-WOA, FPI-WOA, PI-WOA and PI-PSO controllers. The evaluation process considers several important performance metrics, such as ITAE, %(:{text{M}}_{text{u}text{s}}), %(:{text{M}}_{text{p}}), and (:{text{T}}_{text{s}}), specifically during instances of three-phase faults. The results from this analysis confirm the enhanced effectiveness of the MRAC-FPI-WOA controller over other controllers in all evaluated performance aspects. Additionally, Fig. 23(c) illustrates the voltage values existing in the system.
System behavior in case 2. (a) Output power of the sources, (b) System Frequency, (c) System Voltage.
Figure 24(a) illustrates the stepped variation in solar irradiance over time. One must note that changes in solar radiation levels can significantly affect the frequency within the system. The implementation of the MRAC-FPI-WOA controller plays a vital role in ensuring effective frequency regulation under these varying conditions. When compared to PI-PSO, PI-WOA and FPI-WOA controllers, the MRAC-FPI-WOA controller demonstrates a higher level of accuracy in responding to abrupt changes in solar radiation, notably when efficiency is high and weather conditions shift rapidly. The performance efficiency is assessed using the control strategies, taking into account several key parameters, including %(:{text{M}}_{text{u}text{s}}), ITAE, (:{text{T}}_{text{s}}), %(:{text{M}}_{text{p}}). Table 5; Fig. 24(b) present an in-depth analysis of the IHPS frequency’s behavior in response to a step change. Furthermore, Fig. 24(c) visually represents the output power produced by both the PV and DEG sources. In this scenario, when solar radiation diminishes from 1000 to 800 W/m² after two seconds, the PV power decreases from 94 kW to 73 kW. This reduction in PV power generation prompts an increase in the power output from the DEG, which grows from 56 kW to roughly 70 kW to meet the energy demand. Conversely, when solar radiation declines further to 600 W/m² after an additional four seconds, while maintaining a constant ambient temperature, the PV power output declines from 73 kW to about 54 kW. In response, the DEG power generation escalates from 70 kW to approximately 84 kW to satisfy the demand. Additionally, when solar radiation increases from 600 to 900 W/m² at the six-second mark, PV power rises from 54 kW to about 82 kW, causing the DEG output to decline from 84 kW to roughly 63 kW. The proposed controller successfully stabilizes the system frequency, even amidst fluctuations in solar radiation levels. Lastly, Fig. 24(d) provides further insight by presenting the distribution of voltage across the system.
System behavior in case 3. (a) Radiation is a step-changed profile, (b) Output power of the sources, (c) System Frequency, (d) System Voltage.
Figure 25(a) illustrates the ramp-shaped trend of solar irradiance over a specific period. The MRAC-FPI-WOA controller plays a crucial role in ensuring efficient frequency control. Compared to other controllers, the MRAC-FPI-WOA controller proves a significantly higher level of accuracy and responsiveness to ramp variations in solar irradiance levels. Figure 25(b) depicts the output power of the sources. As solar radiation decreases, there is a corresponding increase in the DEG power. Alternatively, as the solar radiation increases, the DEG power also rises to accommodate the changing power consumption needs. To evaluate the efficiency of the system, A comparative analysis is performed on the MRAC-FPI-WOA, PI-WOA, PI-PSO and FPI-WOA controllers. Table 5, along with Fig. 25(c), provides a comprehensive overview of the controlled response of the system frequency under ramp shifts in solar irradiance. The MRAC-FPI-WOA controller ensures stable control of system frequency, even in the face of ramp variations in solar radiation. This proves the controller’s ability to sustain stable performance across different conditions. Moreover, Fig. 25(d) presents additional further by illustrating the voltage levels across the system, further contributing to the general comprehension of the system’s performance.
System behavior in case 4. (a) Radiation is a ramp-changed profile, (b) Output power of the sources, (c) System Frequency, (d) System Voltage.
Figure 26(a) shows the erratic behavior of solar irradiance levels over a defined time. In these situations, the MRAC-FPI-WOA controller is crucial for ensuring effective frequency control. When contrasted with other controllers, the MRAC-FPI-WOA controller shows a notably greater degree of precision and responsiveness to unpredictable fluctuations in solar irradiance levels. Figure 26(b) illustrates the output power of the sources. As the intensity of solar irradiance declines, the DEG power grows proportionally. However, when solar radiation increases, the DEG power rises to match the fluctuating energy requirements. Table 5, along with Fig. 26(c), offers an in-depth examination of the system frequency behavior throughout instances of random fluctuations in solar irradiance. The MRAC-FPI-WOA controller successfully maintains the stability of the system frequency, even amidst unpredictable fluctuations in solar irradiance. This highlights the controller’s ability to offer consistent functionality across a range of conditions. Additionally, Fig. 26(d) offers additional insights by illustrating the system’s voltage levels, improving the overall comprehension of system’s operational performance.
System behavior in case 5. (a) Radiation is a random-changed profile, (b) Output power of the sources, (c) System Frequency, (d) System Voltage.
Figure 27(a) illustrates the power output from both the PV and DEG sources during instances of abrupt load changes. At the 2-second mark, there is a notable decrease of 18.3% in the demand for the AC load within the system, dropping from 120 kW to 98 kW. During this period, the power output from the PV systems remains unchanged at 94 kW, while the output from the DEG declines from 56 kW to 35 kW. At the 4-second mark, as depicted in Fig. 27(a), there is an increase of 11.2% in the AC load demand, rising from 98 kW to 109 kW. Throughout this time, the PV power continues to hold steady at 94 kW, while the DEG power rises from 35 kW to 44 kW to satisfy the additional energy requirements. Figure 27(b) and Table 5 present a detailed comparison of the performance of the MRAC-FPI-WOA, FPI-WOA, PI-WOA, and PI-PSO controllers in managing sudden changes in load, emphasizing the MRAC-FPI-WOA controller’s effectiveness in ensuring effective frequency regulation under these conditions. Furthermore, Fig. 27(c) provides a sequential representation of the system’s voltage measurements, offering additional insights into its operational dynamics.
System behavior in case 6. (a) Output power of the sources, (b) System Frequency, (c) System Voltage.
In this case, scenarios (3) and (6) are interconnected and run simultaneously. Figure 28(a) visually depicts the output power of the sources. When solar irradiance reduces from 1000 to 800 W/m² after two seconds, there is also a concurrent 18.3% reduction in the demand for AC load within the system, which drops from 120 kW to 98 kW. At precisely 2 s, the output from the PV systems reduces from 94 kW to 73 kW. This decline in PV power output, combined with the reduced load, leads to a decrease in power generation from the DEG, which falls from 56 kW to about 48 kW to meet the adjusted energy requirements. Subsequently, when solar radiation further decreases to 600 W/m² after an additional four seconds, there is an 11.2% increase in the AC load demand, rising from 98 kW to 109 kW. At this 4-second mark, the PV power drops from 73 kW to 54 kW. In response to this change, DEG’s power generation rises from 48 kW to 72 kW to fulfill the new demand. Additionally, when solar radiation increases from 600 to 900 W/m² at the six-second mark, the AC load demand within the system remains constant at 109 kW. The PV power rises from 54 kW to about 82 kW, resulting in decreased production from the DEG, which reduces from 72 kW to 62 kW. Table 5; Fig. 28(b) provide a comprehensive overview of the system frequency response. The system’s performance efficiency is assessed based on the MRAC-FPI-WOA, PI-WOA, PI-PSO and FPI-WOA controllers, considering several critical parameters, including (:{text{T}}_{text{s}}), ITAE, %(:{text{M}}_{text{p}}), and %(:{text{M}}_{text{u}text{s}}). Finally, Fig. 28(c) gives deeper insights into displaying the system’s voltage levels.
System behavior in case 7. (a) Output power of the sources, (b) System Frequency, (c) System Voltage.
This study proposes a robust technique for controlling the frequency of an IHPS, utilizing MRAC-FPI-WOA, FPI-WOA, PI-WOA, and PI-PSO controllers to maintain system stability amid disturbances. The findings highlight the substantial benefits of the MRAC-FPI-WOA controller compared to the FPI-WOA, PI-WOA, and PI-PSO controllers across multiple scenarios. For instance, in Case 1, during a three-phase fault for 100 ms at Bus2, the MRAC-FPI-WOA controller lowers %(:{text{M}}_{text{p}}) by 59.05%, %(:{text{M}}_{text{u}text{s}}) by 72.83%, (:{text{T}}_{text{s}}) by 32.07%, and ITAE by 34.81% compared to the PI-PSO controller. In Case 2, with a three-phase fault at the tie-line lasting 110 ms, similar improvements are observed, including lowering %(:{text{M}}_{text{p}}) by 57.47%, %(:{text{M}}_{text{u}text{s}}:)by 79.36%, (:{text{T}}_{text{s}}) by 40.9%, and ITAE by 78.08%, reinforcing the MRAC-FPI-WOA controller’s superior performance in dynamic situations when compared to the PI-PSO controller. In Case 3, MRAC-FPI-WOA showcases its superior adaptability under varying solar irradiance conditions. When irradiance drops from 1000 to 800 W/m², the controller significantly enhances performance by reducing overshoot by 100%, undershoot by 94.12%, settling time by 75.14%, and ITAE by 82.8%. A further decrease from 800 to 600 W/m² yields even better results, undershoot improved by 94.06%, overshoot cut by 100%, settling time improved by 78.05%, and ITAE reduced by 89.47%. Conversely, when solar radiation increases from 600 to 900 W/m², MRAC-FPI-WOA maintains strong performance, decreasing overshoot by 95.38%, undershoot by 100%, settling time by 83.96%, and ITAE by 92.24%. Furthermore, the MRAC-FPI-WOA controller proves improved dynamic responsiveness to ramp changes in solar radiation in Case 4, achieving reductions in %(:{text{M}}_{text{p}}), %(:{text{M}}_{text{u}text{s}}), (:{text{T}}_{text{s}}), and ITAE by 96.72%, 95.24%, 22.79%, and 89.69%, respectively. In addition, it also shows enhanced adaptability to random fluctuations in solar radiation in Case 5, consistently lowering %(:{text{M}}_{text{p}}), %(:{text{M}}_{text{u}text{s}}), (:{text{T}}_{text{s}}), and ITAE by 96.63%, 99.58%, 22.07%, and 95.23%, respectively. The MRAC-FPI-WOA controller also proves effective during load variations in Case 6, significantly improving dynamic performance when the load decreases by 18.3% from 120 kW to 98 kW, with reductions in %(:{text{M}}_{text{p}}) by 93.38%, %(:{text{M}}_{text{u}text{s}}) by 100%, (:{text{T}}_{text{s}}) by 55.19%, and ITAE by 83.08%. Likewise, with a load increase of 11.2% from 98 kW to 109 kW, the MRAC-FPI-WOA controller enhances performance by cutting %(:{text{M}}_{text{p}}) by 33.33%, %(:{text{M}}_{text{u}text{s}}) by 93.48%, (:{text{T}}_{text{s}}) by 77.24%, and ITAE by 86.79%. In Case 7, MRAC-FPI-WOA exhibits exceptional adaptability under varying operating conditions: when solar irradiance decreases from 1000 to 800 W/m² alongside an 18.3% load reduction (120 kW to 98 kW), it reduces overshoot by 92.45%, undershoot by 100%, settling time by 69.81%, and ITAE by 87.46%; during a further irradiance drop to 600 W/m² with an 11.2% load increase (98 kW to 109 kW), it achieves even better performance with 100% overshoot reduction, 93.94% undershoot reduction, 75.3% settling time improvement, and 88.22% ITAE reduction; and finally, when irradiance rebounds to 900 W/m² at a steady 109 kW load, it maintains superior control with 95.4% overshoot reduction, 100% undershoot suppression, 72.9% faster settling, and 90.4% lower ITAE, demonstrating consistent excellence across all test scenarios. The simulation results confirm that the MRAC-FPI-WOA controller effectively sustains system stability and quality by balancing generation and consumption across diverse operating conditions. While the current study demonstrates the controller’s effectiveness through comprehensive MATLAB/Simulink simulations, we acknowledge that real-time hardware validation, such as HIL (Hardware-in-the-Loop) and Processor-in-the-Loop (PIL) validation, would be necessary to fully verify its performance in practical implementations. Future work will focus on experimental validation using microgrid testbeds with actual power electronics interfaces, robustness testing under real-world communication delays and measurement noise, and comparative analysis with physical benchmark controllers. Additionally, we plan to integrate advanced control techniques, such as machine learning, to further improve adaptability and explore hybrid energy systems that incorporate additional renewable sources, with parallel development of hardware prototypes for field testing.
All data generated or analyzed during this study are included in this published article.
Artificial bee colony
Alternating current
Adaptive fractional order PI
advanced sine cosine algorithm
Battery storage
Bat algorithm
Bio-dynamic grasshopper optimization algorithm
Coati optimization algorithm
Cascaded tilted-FO derivative with filter
Chaos quasi-oppositional SHO
Crow-search algorithm
Direct current
Diligent crow search algorithm
Diesel engine generator
Dragonfly search algorithm
Fuzzy-fractional order PID
Logic Controller
Model reference adaptive control-fuzzy proportional integral based whale optimization algorithm
Proportional-integral-derivative-Tilt
Piecewise Linear-Elliptic
Particle swarm optimization
Perturb and observe
Photovoltaic
Renewable energy sources
Sea-horse optimization
State of Charge
Type-2 Fuzzy Cascade
Tunicate search algorithm
Tilt fractional order PID
Whale optimization algorithm
P-N junction ideality factor
Solar irradiance
Battery rated capacity
Short-circuit current
Battery internal reaction current
Parasitic gassing current
Battery current
PV cell’s Reverse leakage current
Fractional-Order PID
Fractional-Order PI
Fuzzy Proportional-Integral
Genetic Algorithm
Grey Wolf Optimizer
Hybrid Adaptive Ant Lion Optimization
Fractional-Order Proportional-Integral
Islanded Hybrid Power System
Improved Salp Swarm Optimization
Model Predictive Control
Maximum Power Point Tracking
Maximum Power Point
Model Reference Adaptive Control
Opposition-based SHO
Proton Exchange Membrane Fuel Cells
Proportional-Integral
Proportional-Integral-Derivative
PV cell’s output current
Photocurrent source
Boltzmann constant
Short-circuit current coefficient
Number of PV cells connected in series
Number of PV cells arranged in parallel
Load power
Diesel engine generator power
Photovoltaic power
Electron charge
Series resistance
Shunt resistance
P-N junction temperature
Battery operating temperature
Cell reference temperature
Settling time
Battery terminal voltage
PV cell’s terminal voltage
Open-circuit voltage
Maximum overshoot
Maximum undershoot
Elborlsy, M. S., Mostafa, R. M., Ghalib, M. A. & Keshta, H. E. A Review of Energy Management System Strategies for AC microgrids operating in islanded mode, 2023 3rd International Conference on Electronic Engineering (ICEEM), Menouf, Egypt, pp. 1–5, https://doi.org/10.1109/ICEEM58740.2023.10319600 (2023).
Elborlsy, M. S., Mostafa, R. M., Keshta, H. E. & Ghalib, M. A. Intelligent PI Controller for Frequency Enhancement of a Diesel-PV-Battery-Based islanded AC microgrid, 2023 24th International Middle East Power System Conference (MEPCON), Mansoura, Egypt, pp. 1–6, https://doi.org/10.1109/MEPCON58725.2023.10462262 (2023).
Shaker, H. K., Keshta, H. E., Mosa, A. M. & Ali, A. A. Adaptive nonlinear controllers based approach to improve the frequency control of multi islanded interconnected microgrids. Energy Rep. 9, 5230–5245. https://doi.org/10.1016/j.egyr.2023.04.007 (2023).
Article  Google Scholar 
Keshta, H. E., Ali, A. A., Malik, O. P., Saied, E. M. & Bendary, F. M. Adaptive control strategy for improved dynamic performance of two islanded interconnected microgrids. Elsevier Int. J. Electr. Power Energy Syst. 126, 1–16. https://doi.org/10.1016/j.ijepes.2020.106562 (2021).
Article  Google Scholar 
Amarawardhana, K. N., Enshaei, H. & Jayasinghe, S. D. G. Correction to: Review on constraint handling techniques for microgrid energy/power management systems. Energy Syst. Feb. https://doi.org/10.1007/s12667-023-00569-0 (2023).
Article  Google Scholar 
Shehata, E. G., Thomas, J., Mostafa, R. M. & Ghalib, M. A. An Improved Droop Control for a Low Voltage DC Microgrid Operation, 2018 Twentieth International Middle East Power Systems Conference (MEPCON), Cairo, Egypt, pp. 850–855. https://doi.org/10.1109/MEPCON.2018.8635258 (2018).
Tripathi, S. K., Singh, V. P. & Pandey, A. S. Robust load frequency control of interconnected power system in smart grid. IETE J. Res. 69 (8), 5351–5363. https://doi.org/10.1080/03772063.2021.1973595 (2021).
Singh, V. K., Verma, A. & Bhatti, T. S. Integration and control of renewable Energy-Based rural microgrids. IETE J. Res. 68 (6), 4492–4502. https://doi.org/10.1080/03772063.2020.1795940 (2020).
Alghamdi, B. & Cañizares, C. A. Frequency Regulation in Isolated Microgrids Through Optimal Droop Gain and Voltage Control, in IEEE Transactions on Smart Grid, vol. 12, no. 2, pp. 988–998, https://doi.org/10.1109/TSG.2020.3028472 (2021).
Pilehvar, M. S., Mirafzal, B. & Exposition A Frequency Control Method for Islanded Microgrids Using Energy Storage Systems, 2020 IEEE Applied Power Electronics Conference and (APEC), New Orleans, LA, USA, pp. 2327–2332 https://doi.org/10.1109/APEC39645.2020.9124179 (2020).
Bandic, L., Hivziefendic, J., Saric, M. & Tesanovic, M. Voltage Regulation of PV System with MPPT and Battery Storage in Microgrid, 2020 International Conference on Smart Systems and Technologies (SST), Osijek, Croatia, pp. 161–166. https://doi.org/10.1109/SST49455.2020.9264071 (2020).
Gulzar, M. M., Sharif, B., Sibtain, D., Akbar, L. & Akhtar, A. Modelling and Controller Design of Automotive Cruise Control System using Hybrid Model Predictive Controller, 2019 15th International Conference on Emerging Technologies (ICET), Peshawar, Pakistan, pp. 1–5,: https://doi.org/10.1109/ICET48972.2019.8994444 (2019).
Jabari, M. et al. A novel artificial intelligence based multistage controller for load frequency control in power systems. Sci. Rep. 14 (1). https://doi.org/10.1038/s41598-024-81382-2 (2024).
Jabari, M. et al. Efficient parameter extraction in PV solar modules with the diligent crow search algorithm. Discover Energy. 4 (1). https://doi.org/10.1007/s43937-024-00063-3 (2024).
Jabari, M. et al. Hybrid adaptive ant Lion optimization with traditional controllers for driving and controlling switched reluctance motors to enhance performance. Sci. Rep. 15 (1). https://doi.org/10.1038/s41598-025-97070-8 (2025).
Jabari, M. et al. Parameter identification of PV solar cells and modules using bio dynamics grasshopper optimization algorithm. IET Generation Transmission Distribution. 18 (21), 3314–3338. https://doi.org/10.1049/gtd2.13279 (2024).
Sahu, P. C. & Samantaray, S. R. Resilient frequency stability of a PV/wind penetrated complex power system with CSA tuned robust Type-2 fuzzy cascade PIF controller. Electr. Power Syst. Res. 225, 109815. https://doi.org/10.1016/j.epsr.2023.109815 (2023).
Sahu, P. C., Prusty, R. C. & Panda, S. Frequency regulation of an electric vehicle-operated micro-grid under WOA-tuned fuzzy cascade controller. Int. J. Ambient Energy. 43 (1), 2900–2911. https://doi.org/10.1080/01430750.2020.1783358 (2020).
Sahu, P. C., Mishra, S., Prusty, R. C. & Panda, S. Improved-salp swarm optimized type-II fuzzy controller in load frequency control of multi area islanded AC microgrid. Sustainable Energy Grids Networks. 16, 380–392. https://doi.org/10.1016/j.segan.2018.10.003 (2018).
Sahu, P. C. Impact and integration of electric vehicles on renewable energy based microgrid: frequency profile improvement by a-SCA optimized FO-Fuzzy PSS approach. Green. Energy Intell. Transp. 100191. https://doi.org/10.1016/j.geits.2024.100191 (2024).
Elborlsy, S. A. et al. Neuro-Fuzzy controller based adaptive control for enhancing the frequency response of Two-Area power system. Heliyon e42547. https://doi.org/10.1016/j.heliyon.2025.e42547 (2025).
Vidyarthi, P. K., Kumar, A., Shankar, R. & Control Improvement of AGC for Multi-Area Deregulated Power Systems with Integrated RESs using a modified TID., 2023 IEEE 3rd International Conference on Smart Technologies for Power, Energy and (STPEC), Bhubaneswar, India, pp. 1–6, https://doi.org/10.1109/STPEC59253.2023.10431292 (2023).
Vidyarthi, P. K., Kumar, A., Kumari, A. & Kumar, A. Renewable sources with virtual inertia penetration in multi area interconnected AGC using a modified MPC controller. Eng. Res. Express. 6 (2), 025320. https://doi.org/10.1088/2631-8695/ad408c (2024).
Vidyarthi, P. K., Kumar, A. & Raj, S. Chaos quasi-opposition sea-horse based modified new Tilt controller designed for multi-area deregulated AGC using deep learning against cyber-attacks. Chaos Solitons Fractals. 188, 115542. https://doi.org/10.1016/j.chaos.2024.115542 (2024).
Vidyarthi, P. K. & Kumar, A. A cascaded Tilt MPC controller for AGC in multi-area interconnected HPS with penetration of RESs and virtual inertia. Electr. Eng. Apr. https://doi.org/10.1007/s00202-024-02398-5 (2024).
Article  Google Scholar 
Sibtain, D., Mushtaq, M. A. & Murtaza, A. F. Adaptive design and implementation of fractional order PI controller for a multi-source (Battery/UC/FC) hybrid electric vehicle. Energy Sources Part. Recovery Utilization Environ. Eff. 44 (4), 8996–9016. https://doi.org/10.1080/15567036.2022.2128470 (2022).
Gulzar, M. M. et al. Modified cascaded controller design constructed on fractional operator ‘β’ to mitigate frequency fluctuations for sustainable operation of power systems. Energies 15 (20), 7814. https://doi.org/10.3390/en15207814 (2022).
Gulzar, M. M., Sibtain, D. & Khalid, M. Innovative design for enhancing transient stability with an ATFOPID controller in hybrid power systems. J. Energy Storage. 99, 113364. https://doi.org/10.1016/j.est.2024.113364 (2024).
Eslami, N., Rahbar, M., Bozorgi, S. M. & Yazdani, S. Whale optimization algorithm and its application in machine learning. Elsevier eBooks. 69–80. https://doi.org/10.1016/b978-0-32-395365-8.00011-7 (2024).
Hasanien, H. M. Whale optimization algorithm for automatic generation control of interconnected modern power systems including renewable energy sources. IET Generation Transmission Distribution. 12 (3), 607–614. https://doi.org/10.1049/iet-gtd.2017.1005 (2017).
Vanchinathan, K., Valluvan, K. R., Gnanavel, C. & Gokul, C. Numerical simulation and experimental verification of Fractional-Order PIλ controller for solar PV fed sensorless brushless DC motor using Whale optimization algorithm. Electr. Power Compon. Syst. 50, 1–2. https://doi.org/10.1080/15325008.2022.2135644 (2022).
Swethamarai, P., Lakshmi, P. & Prasad, S. G. Whale-optimized fuzzy-fractional order controller-based automobile suspension model. Eng. Optim. 54, 1110–1130. https://doi.org/10.1080/0305215x.2021.1913735 (2021).
Percin, H. B. & Caliskan, A. Whale optimization algorithm based MPPT control of a fuel cell system. Int. J. Hydrog. Energy. 48 (60), 23230–23241. https://doi.org/10.1016/j.ijhydene.2023.03.180 (2023).
Ghalib, M. A., Hamad, S. A., Elborlsy, M. S. & Ebied, M. A. Development and Operation of a Fuzzy Logic-Based MPPT Controller for Grid-Connected Photovoltaic Power Systems, 2023 24th International Middle East Power System Conference (MEPCON), Mansoura, Egypt, pp. 1–7, https://doi.org/10.1109/MEPCON58725.2023.10462428 (2023).
Elborlsy, M. S., Hussien, A. E. A. & Ebied, M. A. An Intelligent MPPT Technique based on Fuzzy Controller Applied to Grid-Connected PV Systems in Temperature Fluctuations, 2023 3rd International Conference on Electronic Engineering (ICEEM), Menouf, Egypt, pp. 1–6, https://doi.org/10.1109/ICEEM58740.2023.10319534 (2023).
Hamad, S. A., Ghalib, M. A., Munshi, A., Alotaibi, M. & Ebied, M. A. Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems. Sci. Rep. 15 (1). https://doi.org/10.1038/s41598-025-91044-6 (2025).
Bakundukize, A., Twizerimana, M., Bernadette, D., Pierre, B. J. & Theoneste, N. Design and modeling of PV power plant for rural electrification in Kayonza, Rwanda. J. Energy Res. Reviews. 7 (4), 31–55. https://doi.org/10.9734/JENRR/2021/v7i430197 (2021).
Article  Google Scholar 
Kanouni, B., Badoud, A. E., Mekhilef, S., Bajaj, M. & Zaitsev, I. Advanced efficient energy management strategy based on state machine control for multi-sources PV-PEMFC-batteries system. Sci. Rep. 14 (1). https://doi.org/10.1038/s41598-024-58785-2 (2024).
Duan, F. et al. Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms. Sci. Rep. 14 (1). https://doi.org/10.1038/s41598-024-64234-x (2024).
R, R., Chinnaiyan, K. & S. N, and Hybrid optimization approach for power scheduling with PV-battery system in smart grids. Energy 290, 130051. https://doi.org/10.1016/j.energy.2023.130051 (2023).
Mazumdar, D. et al. Performance analysis of drone Sqadron optimisation based MPPT controller for grid implemented PV battery system under partially shaded conditions. Renew. Energy Focus. 49, 100577. https://doi.org/10.1016/j.ref.2024.100577 (2024).
Grover, H., Verma, A. & Bhatti, T. S. DOBC-based frequency & voltage regulation strategy for PV-diesel hybrid microgrid during islanding conditions. Renew. Energy. 196, 883–900. https://doi.org/10.1016/j.renene.2022.06.140 (2022).
Chauhan, A., Upadhyay, S., Khan, M. T., Hussain, S. M. S. & Ustun, T. S. Performance investigation of a solar Photovoltaic/Diesel generator based hybrid system with cycle charging strategy using BBO algorithm. Sustainability 13 (14), 8048. https://doi.org/10.3390/su13148048 (2021).
Aruna, M. Load frequency control using PID and Particle Swarm Optimization-ITAE, in Lecture notes in electrical engineering, pp. 325–340. https://doi.org/10.1007/978-981-99-7633-1_24 (2024).
Hamad, S. A. et al. Optimizing power sharing accuracy in low voltage DC microgrids considering mismatched line resistances. Sci. Rep. 14 (1). https://doi.org/10.1038/s41598-024-74682-0 (2024).
Kant, R., Maurya, S. P., Singh, K. H., Nisar, K. S. & Tiwari, A. K. Qualitative and quantitative reservoir characterization using seismic inversion based on particle swarm optimization and genetic algorithm: a comparative case study. Sci. Rep. 14 (1). https://doi.org/10.1038/s41598-024-72278-2 (2024).
Alizadeh, S. M., Alruyemi, I., Daneshfar, R., Mohammadi-Khanaposhtani, M. & Naseri, M. An insight into the Estimation of drilling fluid density at HPHT condition using PSO-, ICA-, and GA-LSSVM strategies. Sci. Rep. 11 (1). https://doi.org/10.1038/s41598-021-86264-5 (Mar. 2021).
Marques, D. C., Silva, J. L., Lira, M. M. S. & Aquino, R. R. B. An emotional discrete controller PSO tuned and designed for a real industrial pumping system. Sci. Rep. 12 (1). https://doi.org/10.1038/s41598-022-08192-2 (2022).
Guha, D., Roy, P. K. & Banerjee, S. Whale optimization algorithm applied to the load frequency control of a mixed power system considering nonlinearities and PLL dynamics. Energ. Syst. 11 (3), 699–728. https://doi.org/10.1007/s12667-019-00326-2 (2019).
Elborlsy, M. S., Mostafa, R. M., Ghalib, M. A., Barakat, S. & Keshta, H. E. A case study of optimal design and techno-economic analysis of an islanded AC microgrid. Sci. Rep. 15 (1). https://doi.org/10.1038/s41598-025-94506-z (2025).
Kumar, N. K. et al. Fuzzy Logic-Based load frequency control in an Island hybrid power system model using artificial bee colony optimization. Energies 15 (6), 2199. https://doi.org/10.3390/en15062199 (2022).
Mishra, D., Sahu, P. C., Prusty, R. C. & Panda, S. A fuzzy adaptive fractional order-PID controller for frequency control of an islanded microgrid under stochastic wind/solar uncertainties. Int. J. Ambient Energy. 43 (1), 4602–4611. https://doi.org/10.1080/01430750.2021.1914163 (2021).
Kahani, R., Jamil, M. & Iqbal, M. T. Direct model reference adaptive control of a boost converter for voltage regulation in microgrids. Energies 15 (14), 5080. https://doi.org/10.3390/en15145080 (2022).
Kumar, M. & Tyagi, B. Design of A Model Reference Adaptive Controller (MRAC) for DC-DC Boost Converter for Variations in Solar Outputs using modified MIT Rule in an Islanded Microgrid, 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020), https://doi.org/10.1109/pesgre45664.2020.9070679 (2020).
Edrington, C. S., Ozkan, G., Papari, B. & Perkins, D. Distributed adaptive power management for medium voltage ship power systems. J. Mar. Eng. Technol. 21 (5), 281–296. https://doi.org/10.1080/20464177.2021.1894783 (2021).
Zolfaghari, M., Gharehpetian, G. B., Blaabjerg, F. & Anvari-Moghaddam, A. Model Reference Adaptive Control of UIPC in Islanded Hybrid Microgrids with Flexible Loads and Storages, 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&AmpCPS Europe), pp. 1–5, https://doi.org/10.1109/eeeic/icpseurope51590.2021.9584765 (2021).
Djebbri, S., Ladaci, S., Metatla, A. & Balaska, H. Fractional-order model reference adaptive control of a multi-source renewable energy system with coupled DC/DC converters power compensation. Energ. Syst. 11 (2), 315–355. https://doi.org/10.1007/s12667-018-0317-5 (2018).
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Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).
Process Control Technology Department, Faculty of Technology and Education, Beni-Suef University, Beni-Suef, Egypt
Mohamed A. Ghalib, M. S. Elbrolsy & R. M. Mostafa
Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Benha, Egypt
H.E. Keshta
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Author Contributions: M. A: Validation, For-mal analysis, Writing – review & editing. M. S: original draft, Writing – review & editing. R.M: Formal analysis, Software, Supervision. H.E: Investigation, Formal analysis, Software. All authors reviewed the manuscript.
Correspondence to Mohamed A. Ghalib.
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According to Solar Power World, the NSG Group has concluded a demonstration project involving the creation of float-glass using recycled material from solar panels. The trial involved cover glass that was separated at a recycling facility in Japan before being used in a raw material mix at a float furnace in Chiba.
The manufacturing trial evaluated both the quality of the resulting product and the effects on the production process. The company stated that the findings verified the recycled material could be used within specific parameters, establishing the viability of recycling the material into new float-glass.
This initiative aligns with a carbon neutrality vision for the glass industry announced by a Japanese manufacturers association the previous December, which seeks to establish a system for recycling waste glass. The NSG Group has several production sites in the United States, one of which is a float line in Ohio operated by its member Pilkington North America.
That particular production line provides glass to a solar panel manufacturer that also operates in Ohio.
Interactive table based on the Store Companies dataset for this report.
This report provides a comprehensive view of the float glass and surface ground or polished glass industry in Japan, tracking demand, supply, and trade flows across the national value chain. It explains how demand across key channels and end-use segments shapes consumption patterns, while also mapping the role of input availability, production efficiency, and regulatory standards on supply.
Beyond headline metrics, the study benchmarks prices, margins, and trade routes so you can see where value is created and how it moves between domestic suppliers and international partners. The analysis is designed to support strategic planning, market entry, portfolio prioritization, and risk management in the float glass and surface ground or polished glass landscape in Japan.
The report combines market sizing with trade intelligence and price analytics for Japan. It covers both historical performance and the forward outlook to 2035, allowing you to compare cycles, structural shifts, and policy impacts.
This report provides a consistent view of market size, trade balance, prices, and per-capita indicators for Japan. The profile highlights demand structure and trade position, enabling benchmarking against regional and global peers.
The analysis is built on a multi-source framework that combines official statistics, trade records, company disclosures, and expert validation. Data are standardized, reconciled, and cross-checked to ensure consistency across time series.
All data are normalized to a common product definition and mapped to a consistent set of codes. This ensures that comparisons across time are aligned and actionable.
The forecast horizon extends to 2035 and is based on a structured model that links float glass and surface ground or polished glass demand and supply to macroeconomic indicators, trade patterns, and sector-specific drivers. The model captures both cyclical and structural factors and reflects known policy and technology shifts in Japan.
Each projection is built from national historical patterns and the broader regional context, allowing the report to show where growth is concentrated and where risks are elevated.
Prices are analyzed in detail, including export and import unit values, regional spreads, and changes in trade costs. The report highlights how seasonality, freight rates, exchange rates, and supply disruptions influence pricing and margins.
Key producers, exporters, and distributors are profiled with a focus on their operational scale, geographic footprint, product mix, and market positioning. This helps identify competitive pressure points, partnership opportunities, and routes to differentiation.
This report is designed for manufacturers, distributors, importers, wholesalers, investors, and advisors who need a clear, data-driven picture of float glass and surface ground or polished glass dynamics in Japan.
The market size aggregates consumption and trade data, presented in both value and volume terms.
The projections combine historical trends with macroeconomic indicators, trade dynamics, and sector-specific drivers.
Yes, it includes export and import unit values, regional spreads, and a pricing outlook to 2035.
The report benchmarks market size, trade balance, prices, and per-capita indicators for Japan.
Yes, it highlights demand hotspots, trade routes, pricing trends, and competitive context.
Report Scope and Analytical Framing
Concise View of Market Direction
Market Size, Growth and Scenario Framing
Commercial and Technical Scope
How the Market Splits Into Decision-Relevant Buckets
Where Demand Comes From and How It Behaves
Supply Footprint and Value Capture
Trade Flows and External Dependence
Price Formation and Revenue Logic
Who Wins and Why
How the Domestic Market Works
Commercial Entry and Scaling Priorities
Where the Best Expansion Logic Sits
Leading Players and Strategic Archetypes
How the Report Was Built
Formerly Asahi Glass Co., Ltd.
Parent of NSG Group (Pilkington)
Major domestic float glass producer
Major processor for automotive, construction
Includes flat glass processing
Architectural and automotive glass processor
Joint venture with Indian operations
Architectural glass processor
Focus on specialty, not primary float
Architectural glass fabricator
Glass processing and distribution
Glass cutting, tempering, processing
Glass processor for construction
Glass processing and wholesale
Includes flat glass processing unit
Glass fabricator and distributor
Architectural glass processor
Glass processing in Hokuriku region
Includes glass processing division
Glass fabricator
Architectural glass processor
Flat glass processor and trader
Glass processing company
Not to be confused with cement company
Glass processor in Kanto region
Glass processor in Kansai region
Glass processor in Chubu region
Glass processor in Kyushu region
Glass processor in Tohoku region
Glass processor in Hokkaido region
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Here comes the sun: New bill would let New Yorkers hang solar panels from windows  Gothamist
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“If conditions are too windy, crops can be damaged; if too calm, crops risk mildew.”
Photo Credit: iStock
New evidence shows that solar panels may provide yet another benefit when paired with crops. 
Research from Cornell University has demonstrated that the panels can offer an extra layer of wind protection for crops, aiding their resilience, according to PV Magazine.
The researchers’ findings should hearten farmers exploring the use of agrivoltaics — the co-location of agricultural production and solar energy systems.
“Airflow under solar panels is a key consideration for agrivoltaic systems,” the study’s corresponding author, Max Zhang, told PV Magazine. “If conditions are too windy, crops can be damaged; if too calm, crops risk mildew.”
The researchers used computational fluid dynamics modeling to evaluate how various agrivoltaic designs can change crops’ wind exposure. By adjusting the panels’ configuration and tilt, they were able to maximize airflow.
The team compared the outcome of conventional agricultural windbreaks. The study, published in Agricultural and Forest Meteorology in April, found that single-axis sun-tracking solar panels can block strong winds when positioned low and provide airflow for aeration when tilted upward.
The scientists took great care to mimic how farms already use windbreaks so that a proposed solar system design could theoretically work within conventional agricultural production. The goal is to optimize mixed land use, growing strong crops while also capturing clean energy from the sun. This energy can generate passive income for farmers while also helping farms to reduce the use of fossil fuels.
Separate research shows that many crops thrive under the shade of the panels. Farmworkers and animals can also benefit from them in hot or wet conditions. Wind protection is thus a promising new dimension for the practice.
“The new lowered-first-row design offers an aerodynamic solution to the acceleration zone found in other agrivoltaic scenarios, achieving up to 86% protection in the shelter zone under extreme wind conditions,” Zhang told PV.
The findings emphasize scientists’ ongoing efforts to discover innovative strategies for cultivating abundant harvests while harnessing sustainable energy.
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Next up, the team will look at how farmers might manage solar panels in real time to maximize their benefits relative to wind.
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Partly cloudy early with increasing clouds overnight. Low 59F. Winds WSW at 5 to 10 mph..
Partly cloudy early with increasing clouds overnight. Low 59F. Winds WSW at 5 to 10 mph.
Updated: April 14, 2026 @ 6:35 pm

LEWISBURG — The East Buffalo Township supervisors approved a feasibility study to determine the economic benefits of installing solar panels on the roof of the township garage.
At Monday night’s public meeting, the supervisors said the study from the Pennsylvania Solar Center, a Pittsburgh-based nonprofit organization, will be at no cost to the township. The study is expected to take 15 days.
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A map of the Clean Air Solar Farm proposals(Image: Clean Air Solar Farm)
Fresh proposals for a 500MW solar farm near Beverley have been announced by renewable energy specialists and joint partners, PS Renewables and Ørsted Onshore. The proposed 'Clean Air Solar Farm' represents a revised version of the Kingfisher Solar Farm, which was first announced in January 2025.
The new scheme features a revised site boundary and would generate sufficient electricity to power approximately 160,000 UK homes. It would make it one of the biggest planned, coming just a week after the Government gave the green light to what is set to become the UK's largest solar farm, rated at 800MW.
The project would be spread across two sites near Beverley. A northern site would sit roughly three miles north of Beverley, to the east of the A164. Plans for this land were put before the public during a consultation in February 2025 under the Kingfisher Solar Farm name.
The southern site would be positioned to the southwest of the A1079. The project would tie into the planned Wanlass Beck substation, which forms an extension of the existing Creyke Beck substation.
Given the volume of electricity the Clean Air Solar Farm would produce, it is classified as a Nationally Significant Infrastructure Project (NSIP). This means the decision on whether to grant final consent for the development would rest with the Secretary of State for Energy Security and Net Zero, rather than the local council, as would ordinarily be the case with planning matters.
A planning ruling is anticipated in 2028. Should consent be granted, the Clean Air Solar Farm is projected to be operational by 2033, reports Hull Live.
Randall Linfoot from the Clean Air Solar Farm team said: "Since we first introduced Kingfisher Solar Farm, there have been significant changes. The project was originally developed to make use of spare grid capacity associated with Ørsted's Hornsea 4 offshore wind project. Since then, Hornsea 4 has returned to development, and we have been following the statutory National Energy System Operator (NESO) Gate 2 process to secure a new grid connection.
The project would include two sites near Beverley(Image: Clean Air Solar Farm)
"New project partners PS Renewables are a highly experienced, UK renewable energy developer. Together with Ørsted Onshore, the project proposals and site boundary have since evolved. To reflect these collective changes and a fresh start to our proposal, we took the decision to rename to Clean Air Solar Farm.
"Clean Air Solar Farm will be able to power approximately 160,000 UK homes, making a significant contribution toward meeting the country's ambitious plans to achieve net-zero carbon emissions by 2050. We are committed to making a long-term, positive impact with these proposals and feedback from the community is critical. We would like to thank everyone for the time taken to engage with Kingfisher Solar Farm. All the feedback received to date has been carefully reviewed and fed into our plans."
A series of Public Information Days regarding the scheme will take place in the local area during June 2026. These drop-in sessions will give local communities near the site an opportunity to discover more about the proposals, speak directly with the project team and share their views on the developing design. This will be followed by a consultation period in Autumn 2026.
Drop in sessions take place in June in Lockington, Beverley and Walkington.
To find all the planning applications, traffic diversions, road layout changes, alcohol licence applications and more in your community, visit the Public Notices Portal.
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Plug-in solar panels are a cheaper, simpler alternative to professionally installed panels. But can they really reduce energy bills and are they safe? Matthew Sparkes investigates
By Matthew Sparkes
1 April 2026

Plug-in solar panels can easily be installed on balconies
imageBROKER.com / Alamy Stock Photo
The global surge in solar power is nothing short of extraordinary. Over the past 15 years, the cost of installing a solar system has dropped by 90 per cent and the technology now accounts for over 80 per cent of the world’s new electricity capacity each year. So when oil and gas prices soared as a result of the ongoing conflict in the Middle East, solar was the obvious place to look for relief for many countries. 
But in the UK, it wasn’t just a case of advocating for more of the same – the UK government has said that it will legalise a currently illegal form of solar. So-called plug-in kits will be available “within months” from high-street shops and supermarkets.  
These kits are DIY in nature, you simply bring home some panels, place them in a sunny spot and plug them in. There’s no cost of installation and you can start using the sun’s energy to power your home immediately. If you move, just pack up your panels and bring them with you. Solar energy has seemingly been made even cheaper and available to even more people.  
Many countries have already taken to plug-in solar and there are reasons to be excited about it on a global scale, but can it really help alleviate energy price rises? How cheap is it? And is it actually safe? 
Despite the rapid decreases in cost, installing a traditional solar system isn’t cheap. For an average UK home, estimates for a 4-kilowatt system to cover most energy needs is around £7000. In the US, the average home uses roughly double the energy and the cost of installing a solar system to cover it is around $20,000. These costs include having the panels professionally mounted and a registered electrician installing the system and making alterations to the electricity meter so that excess power can be sold back to the grid – lowering bills or perhaps even generating profit. 

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Plug-in solar is a simpler proposition. The kits are smaller than a full-scale install, so you might expect to purchase an 800-watt system for around £400 and hope for it to cover something like 20 per cent of an average UK home’s energy needs. Installation is free because it is nothing more than tying the panel to balcony railings, a garden fence or a garage roof and plugging a cable into a wall socket. Once you’re plugged in, you can start using any energy that is generated.  
With plug-in solar, excess energy ends up back in the grid but without a professional installation you can’t earn money from it. “Ultimately that energy just gets used by the next-door neighbour,” says Mark Golding at UK solar panel installer Spirit Energy. 
Plug-in solar is already an established technology outside the UK. More than a million plug-in solar systems were registered in Germany as of July last year, for instance. Estimates suggest that they cumulatively have capacity of between 1.6 and 2.4 gigawatts there – enough to simultaneously boil half a million kettles. 
Germany is the only country attempting to track plug-in solar in any meaningful way, so statistics are hard to come by. But one estimate says that there could be as many as 5 million kits in use across Europe. Plug-in solar is only a small fraction of the overall energy mix, but for individuals, it could take the sting out of bills and cumulatively boost a country’s renewable-generation ability.  
Jan Rosenow at the University of Oxford says that uptake could soar if governments keep legislating to allow people to install their own panels. “While individual systems are small, their aggregate impact is becoming meaningful, both in terms of distributed generation and consumer engagement in the energy transition,” says Rosenow.  
Plug-in panels are mostly outlawed in the US at present, but Utah became the first state to legalise them last year and many states have similar legislation in the works. Cora Stryker at Bright Saver, a pro-solar non-profit in the US, says that outside of Utah, people have to go through the same amount of admin to install a few solar panels at home as somebody would to construct a 20-megawatt solar farm – a situation that she says is “patently ridiculous”.  
Stryker hopes that plug-in solar can alleviate financial hardship, help slow climate change and act as the thin end of the wedge to bring the US up to speed on renewable power. “This is the watershed moment, the tipping point toward a world where the dirt-cheap cost of renewables is actually passed on to the consumer,” she says. Bright Saver estimates that 24 million US households will use a plug-in solar system by 2035. 
But, despite the already widespread use, there is worry among some experts about the safety of plug-in kits. Mark Coles, the head of technical regulations at the UK Institution of Engineering and Technology (IET), recommends that before anyone buys a plug-in solar kit they have the wiring in their house checked for safety first. And, even after that, the organisation has identified areas of concern. 
One issue surrounds residual current devices (RCDs), the safety devices found in fuse boxes that sense when current is leaking to ground – a sign of electrocution or a short circuit – and almost instantaneously cut power. Most RCDs used in the UK aren’t suitable for current flowing in both directions and so could malfunction. In the US, the set-up is different but there are similar problems. One reason why Germany has managed to move so quickly is that by coincidence it standardised bi-directional RCDs in the 1980s. 
Another worry of the IET’s is what happens if there are multiple kits and a power cut. In theory the plug-in kits should also shut down in order to prevent “islanding” where one house’s power stays live. But if they’re still generating power then they could deceive each other into thinking the grid is live and keep running. The problem then is that power can jump past the fuse box and electrocute maintenance workers in the area fixing the outage. 
“That’s putting those people in danger,” says Coles. “It kind of goes against the concept of ‘just buy this and plug it in’, but in reality we are concerned that there’s a public safety risk here.” 
Coles agrees that plug-in solar could bring enormous benefits but wants to ensure manufacturers can prove their systems will behave safely, even in unusual scenarios.  
New Scientist put the IET’s safety concerns to the UK Department for Energy Security and Net Zero and a spokesperson said: “Our tests have shown plug-in solar is safe to use on UK domestic circuits. All products will need to meet UK product safety standards, and we have commissioned an independent study to inform further regulations ahead of their sale.”  
Stryker says that, given the catastrophic impact of climate change and soaring energy costs placing many into fuel poverty, the greatest risk to consumers is inaction. She argues that people will adopt technologies like this regardless of whether they are officially sanctioned and regulated, so the pragmatic approach is to help people do it as safely as possible. “Solar is the cheapest energy on the planet, full stop. It’s actually the cheapest energy humanity’s ever produced,” she says. 
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Power play: Malaysia races to go green as Iran war squeezes oil supply  South China Morning Post
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Solar panel maker to open new $350M plant in SC’s Upstate  SC Daily Gazette
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Mulilo has reached financial close for two utility-scale, renewable energy IPPs: one solar PV and one battery energy storage system.
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As global solar demand surges, LONGi’s leadership in high-efficiency panels positions it for growth that could benefit your portfolio. Here’s why it matters for investors in the United States and across English-speaking markets worldwide. ISIN: CNE100001FR6
You might be wondering if LONGi Green Energy Technology stock (CNE100001FR6) offers a compelling play on the renewable energy boom, especially as solar power gains traction worldwide. This Chinese leader in photovoltaic manufacturing dominates with cutting-edge silicon wafers, cells, and modules, powering installations from rooftops to massive utility-scale farms. For you as an investor in the United States and English-speaking markets, LONGi’s scale and technology edge make it a key name to watch amid the push for clean energy independence.
Updated: 14.04.2026
By Elena Vasquez, Senior Markets Editor – Exploring how global solar giants shape investor opportunities in renewables.
LONGi Green Energy Technology builds its business around a fully vertically integrated model in the solar supply chain, from polysilicon production to finished modules. This approach allows the company to control costs, ensure quality, and scale efficiently as demand rises. You benefit from this structure because it translates to competitive pricing that accelerates solar adoption globally.
The model emphasizes monocrystalline silicon technology, which delivers higher efficiency rates than alternatives like polycrystalline. LONGi invests heavily in R&D to push cell efficiencies beyond 25%, setting industry benchmarks. This focus on technological leadership supports steady margin expansion even in commoditized segments.
Revenue streams diversify across modules, wafers, and cells sold to project developers, utilities, and distributors. Overseas sales, particularly to Europe and emerging markets, now form a growing portion, reducing reliance on domestic demand. For your portfolio, this global footprint hedges against regional policy shifts.
Official source
All current information about LONGi Green Energy Technology from the company’s official website.
LONGi’s flagship products include high-efficiency PERC, HJT, and TOPCon solar cells and modules, tailored for residential, commercial, and utility applications. These innovations capture more sunlight per square meter, lowering levelized cost of energy (LCOE) for end-users. You see this edge in markets where space-constrained installations demand top performance.
Key markets span China, Europe, the Americas, and Asia-Pacific, with modules powering gigawatt-scale projects. The company’s Hi-MO series modules lead in bifacial designs, reflecting ground light for extra yield. This product superiority helps LONGi secure long-term supply contracts with major EPC firms.
Competitively, LONGi holds the largest market share in silicon wafers and modules, outpacing rivals through capacity expansions and cost discipline. Unlike less integrated peers, its upstream control buffers against raw material volatility. For you, this moat supports sustained profitability in a price-sensitive industry.
Market mood and reactions
The solar industry benefits from plunging costs, policy incentives, and net-zero commitments driving installations past terawatt milestones annually. Technological advances like larger wafers (210mm format) further cut BOS costs, where LONGi leads. You can expect these tailwinds to propel demand for its high-end products.
Energy storage pairings and green hydrogen projects expand addressable markets beyond pure PV. Supply chain localization efforts in Europe and the US create opportunities for LONGi partnerships. This dynamic positions the company to capture value as renewables integrate into grids.
Global capacity auctions and corporate PPAs underscore the shift to utility-scale solar, LONGi’s sweet spot. Rising electricity prices amplify ROI for solar assets, benefiting module suppliers. For your investments, these drivers signal multi-year upside.
In the United States, LONGi’s modules support the IRA-fueled solar surge, with domestic projects increasingly sourcing efficient imports despite tariffs. You gain indirect exposure to America’s 30%+ annual PV growth without pure-play US firm risks. English-speaking markets like Australia, with world-leading rooftop penetration, rely on LONGi tech for affordability.
UK and Canadian investors benefit from LONGi’s role in offshore wind-solar hybrids and community energy schemes. The company’s ESG credentials align with mandatory disclosures in these regions. This makes LONGi a diversified renewable bet for your portfolio amid energy transition policies.
Supply agreements with US developers highlight LONGi’s navigation of trade barriers via third-country manufacturing. For you tracking global clean energy, it offers scale unmatched by smaller players. Watch how US content rules evolve to impact sourcing.
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More developments, headlines, and context on the stock can be explored quickly through the linked overview pages.
Reputable analysts from banks like Goldman Sachs and JPMorgan highlight LONGi’s market leadership and cost advantages as key strengths, with recent notes emphasizing its resilience amid industry consolidation. Coverage often points to robust demand forecasts supporting capacity utilization above 90%. However, some caution on pricing pressures in oversupplied segments, recommending focus on premium products.
Consensus leans toward positive outlooks tied to global solar deployment targets, though valuations reflect execution risks. Institutions track LONGi’s expansion into n-type cells as a margin catalyst. For you, these views underscore the stock’s role in renewable portfolios, balanced against cyclicality.
Trade tensions and tariffs pose risks to export growth, particularly into the US and Europe, potentially squeezing margins. Overcapacity in China could trigger price wars, testing LONGi’s pricing power. You should monitor how the company deploys cash amid these headwinds.
Technological leaps by competitors or shifts to perovskites challenge current silicon dominance. Policy reversals in key markets add uncertainty. Key questions include overseas revenue ramp-up and R&D success rates.
Supply chain dependencies on Xinjiang polysilicon raise ESG scrutiny, impacting financing. Debt levels from expansions warrant watching. For your decisions, these factors frame the risk-reward balance.
Disclaimer: Not investment advice. Stocks are volatile financial instruments.

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Solar panels not only help crops grow, they offer crucial wind protection  Yahoo
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Solar Energy
The capital of Paraná has initiated a preventive maintenance task force focused on cleaning photovoltaic panels to maximize solar capture in its main energy parks.
The service covers the panels of the Solar Pyramid of Caximba and the Gallery of the Four Seasons, located in the Botanical Garden. The technical team therefore removes layers of dust, debris, and urban waste that accumulate on the glass surface, blocking the passage of light to the silicon cells.
This simple yet strategic intervention increases the efficiency of electricity generation by up to 25% immediately after the process is completed. By keeping the assets clean, the municipality ensures that public investment in sustainable infrastructure delivers the expected return in kilowatt-hours (kWh).
Controversial: 5 Crucial Reasons to Unite Temples with the Solar Energy Program and Unlock an Overwhelming Economy Right Now
The Brazilian Northeast is receiving the largest wave of investments in clean energy ever seen in the country, with R$ 200 billion in wind and solar, 9,000 km of transmission lines, and a promise that could change the economy of 60 million people.
Chinese company creates solar cell that breaks the physical limit of silicon with 34.85% efficiency and promises to revolutionize electricity bills with panels that generate 20% more energy starting in 2026.
Jacareí advances with a photovoltaic plant and bets on sustainability to transform public management with clean solar energy, reducing operational costs and strengthening energy efficiency in essential services.
The action demonstrates how periodic maintenance directly influences the energy autonomy of public buildings and reduces costs with the traditional electrical grid. The work uses specialized equipment and reused water to preserve the sustainability concept that guides the Curitiba More Energy project.
Many owners of solar energy systems overlook the impact of dirt on daily electricity production. Cleaning photovoltaic panels acts as a determining factor for the system’s performance. The accumulation of particles, technically known as soiling, creates a physical barrier that prevents photons from reaching the photovoltaic cells.
In cities with high vehicle traffic or constant construction, this layer of dirt forms quickly, creating internal shadows that hinder the operation of the entire arrangement of panels.
The Solar Pyramid of Curitiba, installed over an old landfill, has thousands of modules that require constant monitoring. When dust covers the glass, the system needs more radiation to generate the same amount of energy.
The cleaning removes pollution stains and bird droppings, which can cause hotspots (hotspots) and permanently damage the equipment. Proper maintenance prolongs the lifespan of the modules, which generally exceeds 25 years under good conservation conditions.
The rainfall regime in Curitiba aids in superficial cleaning but does not replace professional technical washing. Light rain often merely “settles” the dust, creating a crust that is harder to remove.
Therefore, the city hall establishes a fixed schedule for the cleaning of photovoltaic panels, regardless of the season. During prolonged dry periods, the frequency of washings increases to avoid sharp drops in generation.
Remote monitoring through software allows engineers to identify the exact moment for intervention. When the system shows a drop in performance not justified by cloudiness, the maintenance team springs into action.
This data-driven management optimizes public resources and ensures that the Solar Pyramid and the Botanical Gallery always operate at their nominal capacity.
The city hall employs methods that respect the environment during the cleaning of photovoltaic panels. The teams use reused water from treatment processes, avoiding the waste of drinking water.
Additionally, the technicians use soft-bristled brushes specifically designed not to scratch the tempered glass of the panels. Scratches on the surface can cause light refraction, permanently reducing efficiency.
Another interesting detail involves the timing of the cleaning. The technicians preferably carry out the work in the early morning or late afternoon.
Washing the panels under the strong midday sun can cause thermal shock to the glass, which is at a high temperature, leading to micro-cracks in the silicon cells. This technical care preserves public assets and ensures the safety of the operators.
The energy generation at the Solar Pyramid and the Botanical Garden directly offsets the electricity costs of the city hall’s public buildings. By investing in the cleaning of photovoltaic panels, Curitiba saves millions of reais annually.
Each extra percentage of efficiency gained through cleaning represents thousands of reais that do not leave the public coffers to the energy companies.
This saved resource returns to the population in the form of investments in health, education, and paving. The Solar Pyramid, for example, generates enough energy to power about 8,000 homes or offset the consumption of various administrative buildings.
Keeping this “urban power plant” operating at peak performance is a matter of fiscal and administrative responsibility.
The cleaning of photovoltaic panels at the Gallery of the Four Seasons and the Solar Pyramid requires strict safety protocols. Technicians use personal protective equipment (PPE) for work at height and secure anchoring systems. Since the panels are installed on sloped structures, the risk of falling is real and must be mitigated.
In addition to physical safety, there is electrical safety. Even during cleaning, the system continues to generate electricity if there is sunlight. Operators receive training to avoid contact with energized parts and to ensure that water does not infiltrate connectors or junction boxes.
Professional maintenance ensures that the system continues to operate without risks of short circuits or unexpected interruptions.
The Gallery of the Four Seasons, in the Botanical Garden, serves as a technological showcase for the millions of tourists who visit the site. By watching the teams perform the cleaning of photovoltaic panels, visitors understand that solar energy requires care and maintenance, just like any other engineering system. This demystifies the idea that solar panels “need nothing” after installation.
The Solar Pyramid also plays an educational role by transforming an environmental liability (the old waste) into an energy asset (sun). Constant cleaning keeps the visual aspect of the work impeccable, reinforcing the image of Curitiba as an innovative and clean city.
This example encourages local residents and businesses to adopt photovoltaic systems on their roofs, boosting the green economy in the region.
Technical studies indicate that the lack of cleaning of photovoltaic panels can lead to accumulated losses of up to 30% in a year. In large-scale systems, such as those in Curitiba, this loss is equivalent to shutting down hundreds of panels. Below, see the direct benefits of maintenance:
Increased current: Light reaches the cell without obstacles, generating more electrons.
Temperature reduction: Dirt can retain heat, and excessive heat decreases silicon efficiency.
Corrosion prevention: Acidic pollution residues or bird droppings can corrode the aluminum frame.
Aesthetics: Clean panels reflect the management’s commitment to the heritage.
The success of the Solar Pyramid and the Botanical Garden gallery motivates the expansion of the Curitiba More Energy program to other areas of the city. The city hall is studying the installation of new panels at bus terminals and municipal schools.
All these new projects will follow the same maintenance standard, prioritizing cleaning photovoltaic panels as a strategy for maximizing energy profits.
The city is on its way to becoming a hub for urban renewable energy. The integration of different sources, combined with excellent maintenance, places the capital of Paraná at the top of the ranking of smart cities in the world.
Continuous investment in technology and conservation ensures that Curitiba is prepared for the challenges of a future with low carbon emissions and high electrical demand.
The task force for cleaning photovoltaic panels at the Solar Pyramid and the Botanical Garden proves that sustainability goes beyond the installation of new equipment. Managing energy requires attention to detail and rigorous maintenance.
By removing dirt from the panels, the city hall of Curitiba not only cleans glass but unlocks the true potential of the sun to illuminate and move the city.
This initiative serves as a model for other city halls and the private sector. The real impact appears on the electricity bill and in air quality, consolidating solar energy as the foundation of the modern energy matrix.
With clean panels and systems operating at maximum capacity, Curitiba reaffirms its commitment to the environment and to the taxpayer’s wallet, consistently and efficiently transforming light into progress.
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Sigenergy has entered the European utility-scale photovoltaic systems market. Together with Arausol, a German-based PV specialist, and the European distributor Memodo, it is developing Germany’s largest PV plant with decentralised storage systems that operate on direct current (DC)
The project, located in Weissach im Tal, is currently under construction and will include an installed peak PV capacity of 11.6MWp and a battery capacity of 20MWh. This capacity will be distributed across 1,660 Sigenergy battery modules, each with 12kWh capacity, installed in stackable SigenStacks and deployable in a decentralised manner.
Installing SigenStacks on the Arausol mounting structure, similar to PV module racks, requires no complicated cabling, cranes, or other heavy equipment. The solution helps to avoid soil sealing, which is common in projects involving large central batteries housed in containers.
Compared with AC-coupled systems, it eliminates the need for multiple conversions between DC and AC. Instead, excess photovoltaic DC power is fed to the batteries and converted to AC by the inverters when it is time to feed power to the grid. DC coupling increases the overall system’s efficiency by at least 4% and can eliminate the need for duplicate inverter infrastructure.
The DC mode also enables Arausol to increase the PV system's output, further enhancing the project's economic viability.
In comparison, AC-coupled systems have technical limitations. As a result, consistent use of DC coupling in large-scale PV projects would enable a smaller-scale expansion of the power grid required for Germany's energy transition, helping to keep costs low for customers. 
Sigenergy is also supplying Arausol with other electrical components, including medium-voltage transformer stations with pre-installed low-voltage connections. Memodo ensures reliable procurement through its delivery capability and market knowledge, whereas Arausol is responsible for construction and project management, as well as for providing substructures from its own facilities. Connection to the grid is scheduled for July 2026.
Emanuel Spahrkäs, senior account manager at Sigenergy, said, "This project sends a clear message: DC coupling enables utility-scale energy systems to be built faster, smarter, more efficiently, and in a more environmentally friendly way. By combining Sigenergy's unique DC-coupled solution with a decentralised battery architecture and Arausol's easy-to-install mounting system, we achieve faster commissioning, higher performance, and lower operating costs."
Jaime Arau, CEO and founder of Arausol, said, "As a leading systems integrator and project developer for photovoltaic systems, we are committed to implementing the latest technology. Thanks to its innovative DC coupling, Sigenergy is an ideal partner for realising this goal."
Memodo worked closely with the customer to define the system architecture and position Sigenergy as a suitable partner.
Jonas Hollweg, head of Sales at Memod, said, "Our strength lies in actively bringing innovations to the market and supporting projects across the entire value chain. The project underlines the potential of close and strategic cooperation between manufacturers, project developers and distributors in delivering advanced energy solutions."

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Elgin Secures £500 Million to Fund 1 GW UK Solar and Storage Pipeline  SolarQuarter
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“In this country, it’s true that the electricity situation has always been difficult.”
Photo Credit: YouTube
After having oil shipments into the country restricted by the U.S., Cuba has begun to focus more on clean energy from solar power, Reuters reported in a recent video.
According to solar installer Radel Carno, both homes and businesses in Havana have begun installing solar panels to protect against power outages, as they are the best way to achieve independence from the grid.
Even in America, where fuel is plentiful, solar panels are one of the best ways to save money on your monthly energy bill and protect yourself from power outages. 
If you want to install solar panels at home, EnergySage’s free tools can get you started with competitive quotes from trustworthy installers in your area.
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To get started, just answer a few questions about your home — no phone number required. Within a day or two, EnergySage will email you the best options for your needs, and their expert advisers can help you compare quotes and pick a winner.
“Demand has increased,” Carno says in the video. “Customer requests have been growing. 
“And because of the increase in demand for electricity, due to power outages in Cuba and all that, and in private businesses, due to the power consumption plans they have to comply with, they have been forced to install panels so as to not exceed those plans and to save a little electricity.
“And in homes, individuals have been forced to install this type of equipment.”
Solar panels are also powering Cubans while on the move, as demonstrated by rickshaw driver Alejandro Aratola. 
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“In this country, it’s true that the electricity situation has always been difficult,” Aratola says. “I used to have an old rickshaw. I put two panels on it, but it wasn’t enough. I managed to trade it in for this hybrid one and put this panel on it about eight or 10 months ago. It helps me a lot. 
“It extends my range, and I don’t have to use gasoline. When the power goes out, I put it in front of the house and charge it. It has also helped a lot that people after seeing me are doing this, too. They are inspired and are also trying to put panels on their houses and rickshaws.”
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YouTube commenters loved the ingenuity of this solution. “Transforming the dynamics of power, in every sense of the word,” said one user.
If you also want to follow this example, EnergySage can help you save up to $10,000 on your solar installation in the U.S. Through its helpful mapping tool, you can discover all the incentives available to help cover the cost of solar panels in your state and get a baseline for the price of installation in your area. 
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For true independence from the grid and maximum savings, you’ll want to add a battery backup to your solar setup. EnergySage can help with this as well.
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Photo: Ameresco
Published
April 14, 2026
Country
Greece
Author
Igor Todorović
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Published:
April 14, 2026
Country:
Greece
Author
Igor Todorović
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German energy infrastructure asset manager Luxcara has awarded an engineering, procurement and construction (EPC) contract to joint venture Ameresco Sunel Energy. The Kozani solar project in northern Greece is for nearly 130,000 photovoltaic modules on one-axis trackers, for 83 MW.
The deal includes a medium-voltage grid connection and an extension of the 400/33 kV high-voltage substation. Construction works are already underway, the companies revealed. Luxcara holds a majority stake in the project. The site is in the Western Macedonia province. It is Greece’s only remaining coal mining and power hub, undergoing decarbonization and modernization.
“Projects like this reflect the growing maturity of the Greek renewable energy market, where scale, structure, and long-term stability are becoming increasingly important. As Ameresco Sunel Energy, we are focused on supporting this shift by delivering projects that meet the evolving expectations of international investors,” said Ameresco Sunel’s Vice President Konstantinos Zygouras.
The deal includes a medium-voltage grid connection and an extension of the 400/33 kV high-voltage substation
Investment Manager at Luxcara Lorenz Hahn praised the joint venture for being a trusted partner in advancing his firm’s solar power investment in the country.
Founded in 2000 in the United States, Ameresco is an energy infrastructure solutions provider. It has headquarters in Framingham, Massachusetts, and more than 1,500 employees. Sunel specializes in photovoltaics, battery energy storage systems and energy efficiency.
Headquartered in Athens, the company operates regional offices in London, Valencia, Milan, Bucharest and Tashkent, totaling over 400 employees. Since its establishment in 2006, Sunel developed, designed, and executed more than 2 GW of solar projects worldwide. It is currently executing more than 2 GW of PV projects in Greece, the United Kingdom, Spain, Italy, Romania and Uzbekistan.
Hamburg-based Luxcara acquires, structures, finances and operates energy projects with a long-term buy-build-operate approach. Its portfolio comprises wind and solar power assets, battery energy storage systems (BESS), charging stations for electric vehicles, and electrolyzers for the production of green hydrogen.
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Greece
14 April 2026 – Ameresco and its JV partner Sunel were selected for the installation of an 83 MW solar system in Greece’s coal land
Bosnia and Herzegovina
14 April 2026 – EPBiH, with support from the World Bank, plans to modernize the Salakovac hydropower plant, help install 15 MW of rooftop PV for prosumers, and build solar plants with batteries
North Macedonia
14 April 2026 – North Macedonia’s new NECP, covering the period from 2025 to 2030, brings 61 measures for a strong renewables growth and European standards
Region/EU
14 April 2026 – The European Commission unveiled the programme and launched the registration for the European Sustainable Energy Week – EUSEW 2026
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Solar cleaning related operating costs can be reduced by approximately 30 to 40 percent through robotic systems, particularly in high soiling environments. In addition to direct cost savings, consistent cleaning stabilises direct current input to inverters, reducing electrical stress and lowering fault incidence.
Yogesh Kudale, Co-Founder & CEO, TAYPRO
TAYPRO
India’s utility-scale solar sector is transitioning from a capital cost-focused growth phase to one where operational efficiency determines long-term viability. As equipment performance matures, sustained energy output over the plant life is becoming central to project economics. In this shift, automation and solar cleaning robots play a key role by ensuring consistent module cleanliness, stable performance ratios, and reduced manual dependence, helping limit soiling-related losses and preserve asset value.
In large scale solar plants, dust accumulation and soiling represent a major source of hidden energy loss. Technical assessments indicate that soiling can reduce energy generation by up to 30 percent under extreme conditions, particularly in arid, agricultural, and industrial environments. For a 1 MW ground mounted solar plant generating approximately 15 lakh units annually, a 3 percent soiling loss alone can result in nearly 45,000 units of unrealised generation each year. This level of loss directly affects revenue recovery, extends payback timelines, and compresses project returns, making soiling management a critical economic variable in utility scale solar operations.
Traditional manual and water based panel cleaning practices were not designed for the scale and geographic diversity of modern solar deployments. These methods rely heavily on labour availability, site access, and water logistics, all of which introduce operational variability.
Cleaning frequency often declines during monsoon periods or agricultural peak seasons, even though dust adhesion remains significant. Water consumption for routine cleaning creates additional cost and compliance challenges in water stressed regions. Repeated abrasive contact during manual cleaning also accelerates degradation of module surface coatings, increasing long term efficiency loss beyond expected degradation curves.
These structural constraints limit the ability of operators to maintain consistent performance ratios across large portfolios.
Solar cleaning robots address these limitations by introducing consistency, predictability, and scalability into panel maintenance operations. Designed for utility scale environments, these systems perform regular cleaning cycles without manual intervention and without reliance on water.
Modern solar cleaning robots integrate sensor based navigation, terrain adaptation, and automated scheduling to operate across fixed tilt and tracker based installations. By maintaining consistent module cleanliness, they prevent gradual performance degradation rather than reacting after generation losses become visible.
This approach shifts operations and maintenance from a reactive model to a preventive and predictive framework.
Contrary to early assumptions, the deployment of solar cleaning robots has demonstrated a net reduction in operating expenditure over the project lifecycle. Automated cleaning reduces labour dependency, eliminates water procurement and transport costs, and improves cleaning repeatability.
Analysis indicates that cleaning related operating costs can be reduced by approximately 30 to 40 percent through robotic systems, particularly in high soiling environments. In addition to direct cost savings, consistent cleaning stabilises direct current input to inverters, reducing electrical stress and lowering fault incidence.
These factors contribute to improved asset availability and reduced downtime across large installations.
Solar cleaning robots increasingly operate as data generating assets rather than standalone mechanical systems. Cleaning frequency, surface condition trends, environmental exposure, and operational health metrics are continuously captured and analysed.
This data enables plant operators to treat performance ratio as a controllable operational variable. Maintenance planning, cleaning intensity, and resource deployment can be optimised based on predictive insights rather than periodic inspections or generation shortfalls.
As India’s utility scale solar sector matures, project economics are increasingly driven by the ability to preserve generation rather than expand capacity. Soiling related losses have emerged as a material operational risk, making consistent and scalable maintenance essential. Automation and solar cleaning robots enable predictable, water independent, and data driven cleaning, reducing performance variability and operating costs. In this environment, robotic cleaning systems are becoming a core component of utility scale solar operations, directly influencing long term asset performance and financial stability.
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GoodWe appoints Photovoltaic Solar as its official India partner, aiming to expand market reach, strengthen distribution, and deliver advanced solar inverter solutions supporting the country’s growing renewable energy demand.
April 14, 2026. By News Bureau
GoodWe, the a PV inverter manufacturer and smart energy solution provider, has announced the appointment of Photovoltaic Solar, as its official partner for India. This strategic partnership is a milestone in GoodWe’s ongoing efforts to expand its footprint in the Indian market and provide customers with products and services.
The collaboration aims to leverage GoodWe’s innovative technology and comprehensive product range, ensuring that Indian consumers receive reliable and efficient solutions tailored to their specific needs.
With a rich history of success and a commitment to delivering the highest standards of quality, GoodWe is confident that Photovoltaic Solar’s distribution network will help accelerate growth and further solidify the brand’s presence in India and contribute to the nation's renewable energy goals. 
Nevil Thakkar, Founder of Photovoltaic Solar, said, “We are pleased to partner with GoodWe to strengthen our solar solutions portfolio in India. This partnership aligns with our vision to deliver reliable, high-performance energy solutions to the market. Together, we aim to set new benchmarks in quality, innovation and customer satisfaction in the rapidly evolving solar industry.”
Aniket Sawant, Country Manager– India, GoodWe added “Partnering with Photovoltaic Solar marks an exciting milestone for us as we work together to deliver cutting-edge solar inverter solutions to a wider audience in India. This partnership is a bold stride toward a more sustainable future for all.”

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Energy briefs
Oil prices plunged 8% on Tuesday
April 14, 2026

Opposition to a proposed 5,000-acre solar farm in Jackson County, Kansas led to recent calls for a moratorium on solar farms in the county located north of Topeka, Kansas.
The County Planning Commission held a recent meeting to consider urging the county commission to adopt an 18-month moratorium on the Jeffrey Solar project proposed by NextEra Energy Resources. The project was revealed some years ago and sparked opposition from residents and farmers who fear the loss of at least 2,000 acres of farmland west of Holton. The size of the proposed solar farm would cover about 6,600 football fields of land.
The Planning Commission considered changes to the county zoning laws and this week the issue will be considered by Jackson County Commissioners. It was not the first such meeting to deal with the solar farm. The Planning Commission held a similar lengthy meeting in October 2025, one lasting three hours.

The commission listened to dozens of residents who spoke about proposed solar regulations that filled 34 pages. The Holton Recorder reported the regulations were prompted by the proposed Jeffrey Solar project.
Sherman Bernett, lead developer for the Jeffrey Solar project, said the current draft of solar regulations will stop the project, according to the Recorder.
“This latest draft of regulations will not allow this project to move forward,” Bernett said. “The setbacks proposed are not based on science and engineering and take away landowner rights.”
NextEra contends the solar farm would bring employment opportunities to the area and generate $136 million in revenue. The website for the solar project stated, “The Jeffrey Solar project is an innovative solar project proposed for Jackson County, Kansas that will have a capacity of up to 500 megawatts of clean, renewable, American-made energy. The Jeffrey Solar project is more than solar panels — it represents a significant capital investment in Kansas. Once operational, it will create good-paying jobs and millions in additional revenue for landowners and the local community.”
If approved, the project would add solar capacity to Kansas where, according to the Solar Energy Industries Association, it totals 463 MW. Jeffrey Solar was scheduled to begin operations by February 2030 and cover nearly eight square miles of land.
The possible moratorium isn’t the first time the county has considered such a halt in solar farm development. It also explored a similar moratorium in 2022. Further, NextEra’s project led to at least one lawsuit in which landowners  filed suit to stop the company’s plans for another 5,000-acre solar farm. A U.S. District Judge in Kansas City later ruled against Thomas Hoffman, Joseph Strong, Vincent Shibler and David Shibler. Hoffman contended the project site would affect his local runway and his flying business.
In nearby Shawnee County, where Topeka is the capitol, county leaders recently approved new solar energy project regulations. Shawnee County Commissioner Aaron Mays told KSNT TV News, “Shawnee County right now doesn’t have a, prior to today at least, did not have any solar regulations at all,” Mays said. “And so we initially started talking about this a couple of years ago and decided that we needed to have some sort of a framework in place.”
The new rules apply to solar energy projects in unincorporated areas of the county. They not only created guidelines to review large-scale solar developments but also controls for project size limits, setbacks from homes and roads and requirements for land restoration at the end of the project.
 
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Solar Energy Corporation of India Limited (SECI), an Indian government renewable energy implementing agency, has invited bids for 4455 kW rooftop PV systems under RESCO mode across 14 institutional sites. The work covers design, engineering, supply, erection, testing, commissioning, grid connectivity, net-metering, and O&M during the PPA term. The bid processing fee is INR 6,000 including GST, while EMD is project-specific as per the RfS. The pre-bid meeting is scheduled for 20 April 2026, online bid submission closes on 11 May 2026, and offline submission and bid opening are set for 14 May 2026. Bidders must comply with SECI’s technical parameters, use ALMM List-I modules and ALMM List-II cells, and meet cumulative net-worth and liquidity thresholds for all lots bid for the total capacity they bid for.

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Indian solar cell manufacturers listed on the Ministry of New and Renewable Energy’s (MNRE’s) Approved List of Models and Manufacturers (ALMM) List-II expanded their manufacturing capacity from 27,753MW from 26,477MW in February, a move that has drawn a broadly positive response from industry stakeholders.
The latest revision includes the entry of heterojunction (HJT) solar cell technology into ALMM List-II for the first time through Gujarat-based solar manufacturer Reliance Industries, which secured 1,238MW of enlisted capacity.
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Reliance’s HJT cells, produced at its Gujarat facility, use 210mm × 105mm zero-busbar architecture and are rated at efficiencies of up to 25.6%, with wattage ranging from 5.28W to 5.66W. The company’s HJT modules were previously included in ALMM List-I with 1,716MW of annual capacity, covering monofacial and bifacial glass-to-glass configurations.
Other companies to have added manufacturing capacity include Jupiter Solartech, a wholly owned subsidiary of solar manufacturer Jupiter International, which has added 991MW of monocrystalline passivated emitter rear contact (PERC) bifacial cell capacity from its Himachal Pradesh facility.
Websol Energy System has also added 600MW of monocrystalline PERC P-type bifacial cells from its West Bengal plant, taking its total ALMM List-II capacity to 1,202MW. The additions are valid until April 12, 2030, and November 23, 2029, respectively.
Industry participants have highlighted the inclusion as a significant policy signal towards higher-efficiency technologies and domestic manufacturing capability building.
“The inclusion of heterojunction technology in the ALMM II list is a forward-looking move by MNRE. It signals a clear policy push toward high-efficiency modules and could accelerate technology upgrading among domestic manufacturers,” Gaurav Upadhyay, energy and finance specialist at Institute for Energy Economics and Financial Analysis (IEEFA), told PV Tech.
“However, it can also raise the bar on capital intensity and technical capabilities, potentially consolidating the market in favour of well-capitalised players.”
Under MNRE regulations, from 1 June 2026, all government-supported solar projects will be required to use modules manufactured with cells listed under ALMM List-II, reinforcing the importance of domestic cell capacity in project eligibility and procurement.
Industry experts also pointed to the revision as evidence of accelerating structural maturity in India’s solar manufacturing ecosystem.
“The latest revision of the ALMM List-II marks a defining moment for India’s solar manufacturing ecosystem,” said Prashant Mathur, CEO of module manufacturer Saatvik Green Energy Limited. “What we are witnessing is not just a policy milestone, but a clear signal of market-led confidence in domestic cell manufacturing.”
Mathur added that the entry of large industrial players and advanced technologies such as HJT reflects “a decisive shift towards higher efficiency and long-term competitiveness,” and a transition “from capacity creation to capability building.”
Echoing a similar sentiment, Nikhil Bansal, co-founder of solar developer Solarium, told us that MNRE’s inclusion of HJT technology in ALMM List-II is a “clear statement” that India is moving to lead in solar manufacturing technology rather than follow global trends.
“HJT is a step-change in efficiency and performance, and this will separate future-ready manufacturers from the rest,” Bansal said. “It raises the bar, but that’s exactly what the industry needs to compete globally on technology, not just cost.”
Additionally, the ministry has proposed the creation of ALMM List-III for wafers, with stakeholders noting that a potential gap in upstream capacity and a tight compliance timeline could impact supply chains and bidding behaviour.

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“It takes anywhere between 2 to 15 minutes for the toxin to take place in the dog.”
Photo Credit: iStock
South and Central America are well-known for their vibrant, diverse ecosystems. The multitude of species can present issues, however, if introduced to non-native regions.
Floridians, for example, recently received a critical reminder of how invasive species can disrupt not only the ecological balance of the local environment but also the lives of their pets, according to CBS12.
Cane toads, known for their ability to secrete deadly toxins when threatened, are rapidly spreading throughout South Florida, particularly in Palm Beach County.
Experts warned residents about the dangers that these toads pose to pets, especially to dogs, as they generally spend more time outdoors.
Austin Begon, a technician at the toad removal and prevention company Toad Busters, notes that even brief contact can be harmful to pets. If a dog encounters one by licking or sniffing it, symptoms can appear rapidly.
“It takes anywhere between two to 15 minutes for the toxin to take place in the dog,” he explained.
Pet owners are encouraged to keep an eye out for symptoms such as drooling, vomiting, and foaming at the mouth, as the toxin can quickly be fatal.
Outdoor lighting and water features, such as lakes and ponds, can make for appealing environments for toads, where they can easily breed and hide.
“Pretty much our homes are exactly what they’re looking for,” Begon warned.

To reduce the likelihood of these creatures entering residential areas, homeowners are advised to minimize outside lighting and clear thick vegetation that retains moisture. Pets should be kept on a leash at night, and it is highly recommended to check yards and other grassy areas before letting pets outside.
If a dog does come into contact with a cane toad, pet owners should wipe the gums, teeth, and roof of the dog’s mouth and immediately visit an emergency veterinarian.
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A contract has been established between Ameresco and Miami-Dade County, according to a report from Solar Power World. The agreement is a performance savings contract covering energy, water, and wastewater.
The collaboration focuses on implementing energy conservation measures at two county facilities: the Miami-Dade Childrens Courthouse and the E.R. Graham Building. The planned work involves the design and installation of rooftop solar photovoltaic systems at both locations. It also includes comprehensive retrofits of interior and exterior lighting to LED technology across the two buildings.
A senior official from the county’s Office of Real Estate and Development stated that the partnership aims to provide critical upgrades to energy infrastructure. The goal is to maintain reliable and efficient facilities for the community. The official also noted the project supports broader sustainability objectives while ensuring the continuity of essential public services.
The initiative is consistent with the county’s long-term strategy for building and infrastructure resilience. Utilizing on-site renewable energy, efficient lighting, and other advanced energy technologies is expected to decrease operating costs and lower energy consumption. The effort further aids in modernizing the county’s infrastructure.
A co-president of Ameresco highlighted the company’s existing partnership with Miami-Dade County, emphasizing the delivery of solutions intended to create lasting community value. The executive expressed pride in continuing this collaboration through the current contract, working to advance practical improvements that strengthen facilities and support operational goals.
Interactive table based on the Store Companies dataset for this report.
This report provides a comprehensive view of the solar cells and light-emitting diodes industry in the United States, tracking demand, supply, and trade flows across the national value chain. It explains how demand across key channels and end-use segments shapes consumption patterns, while also mapping the role of input availability, production efficiency, and regulatory standards on supply.
Beyond headline metrics, the study benchmarks prices, margins, and trade routes so you can see where value is created and how it moves between domestic suppliers and international partners. The analysis is designed to support strategic planning, market entry, portfolio prioritization, and risk management in the solar cells and light-emitting diodes landscape in the United States.
The report combines market sizing with trade intelligence and price analytics for the United States. It covers both historical performance and the forward outlook to 2035, allowing you to compare cycles, structural shifts, and policy impacts.
This report provides a consistent view of market size, trade balance, prices, and per-capita indicators for the United States. The profile highlights demand structure and trade position, enabling benchmarking against regional and global peers.
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Insulation Maker’s Solar Rooftop to Offset 771 Tons of CO2 Emissions Per Year  Manufacturing.net
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As Europe accelerates its transition toward a decarbonized energy future, a new study reveals a complex and paradoxical challenge emerging from the widespread adoption of solar photovoltaic (PV) systems. The phenomenon, known as the solar rebound effect (SRE), threatens to undermine some of the anticipated benefits of clean energy by driving up electricity prices and increasing carbon emissions through increased energy consumption. This nuanced dynamic, thoroughly examined by researchers Delic and Bucksteeg, exposes a critical vulnerability in Europe’s energy transition path, particularly highlighting the ramifications for electricity markets, infrastructure, and energy equity.
At the heart of the analysis lies the intricate interplay between solar-generated electricity and consumer behavior. When households install PV systems, they often increase their overall electricity consumption instead of reducing it, a counterintuitive outcome termed the rebound effect. This increased demand, fueled by the economic incentive of low-cost solar power, places additional pressure on the power system. It challenges grid operators to provide flexibility and balance, especially during periods when solar output is insufficient, such as evening hours or winter months. The study’s projections for 2040 paint a stark picture: wholesale electricity prices across Europe experience heterogeneous but often significant rises driven by rebound consumption patterns.
The research employs a detailed spatial and temporal simulation framework, assessing three distinct rebound demand profiles—dynamic, simultaneous, and sweeping—that characterize how increased electricity usage might unfold. The dynamic profile, which reflects varying demand peaks aligning with solar generation, induces moderate electricity price increases ranging from +€0.06 per megawatt-hour in France to +€0.76 per megawatt-hour in Belgium. This scenario reveals that flexibility, especially in Central European countries reliant on gas-fired power plants to cover solar shortfalls, is essential for mitigating cost escalations. The results emphasize that localized generation mixes and grid constraints significantly influence market outcomes.
Contrasting this, the simultaneous rebound profile, wherein consumers increase demand concurrently during solar peak production, demonstrates a more moderated impact on prices across many regions. Remarkably, in Southern and South-Eastern Europe, characterized by high solar resource intensity, this profile can even cause slight reductions in wholesale prices due to the natural alignment of demand surges with abundant solar output. These findings suggest that strategic demand shifting could leverage solar generation peaks to relieve stress on the grid and contain price spikes, underscoring the value of demand response programs in future power systems.
However, the sweeping rebound profile reveals the most alarming consequences. Representing a uniform increase in demand regardless of solar availability, this pattern precipitates large wholesale price surges, particularly in Central Europe. Countries like Germany and Slovakia face increases as high as +€1.55 and +€1.64 per megawatt-hour, respectively. The uniform demand load necessitates continuous reliance on gas-fired power plants, which are challenged by limited system flexibility and infrastructure bottlenecks during peak winter periods. The pronounced costs in these scenarios highlight the critical need for infrastructure upgrades and grid expansion to accommodate changing consumption dynamics driven by solar rebound.
Underlying these wholesale price impacts, the study delineates the interactions with escalating carbon dioxide (CO₂) prices linked to stringent decarbonization target policies for 2045. Rebound demand indirectly inflates CO₂ pricing by pushing gas generation usage higher to cover solar shortfalls, creating a feedback loop that compounds electricity system costs. This complexity illustrates the interconnectedness of market mechanisms, consumer behavior, and climate policies, requiring integrated solutions that simultaneously address demand-side management and supply system decarbonization.
Furthermore, the authors draw attention to equity concerns intensifying under the solar rebound effect paradigm. The capital-intensive nature of PV system adoption disproportionately favors higher-income households, especially given that ownership is largely confined to properties with suitable rooftops. This disparity in access to low-cost solar energy leads to a bifurcation in benefits, with wealthier adopters gaining more substantial financial relief while non-adopters bear increasingly higher systemic electricity costs. Notably, current regulatory frameworks exacerbate this divide by taxing grid-supplied electricity more heavily than self-consumed solar power, effectively subsidizing solar users’ increased consumption on the backs of all electricity consumers.
The resulting cost externalities generate implicit cross-subsidies from non-adopters—often lower-income households with limited options for self-generation—to adopters, further deepening energy poverty and inequality. This regressive effect is particularly pernicious because vulnerable populations spend a higher proportion of their income on energy, making wholesale price increases more burdensome. The study highlights the urgency of re-evaluating tariff structures and regulatory mechanisms to ensure a more equitable distribution of costs and benefits arising from the energy transition.
On a technological and policy front, the findings underscore the imperative for enhanced backup capacity investments and grid modernization to mitigate the negative price and emissions impacts of the solar rebound. Current energy infrastructure in Central Europe, with its reliance on gas-fired power plants during non-solar periods, faces significant strain. Investing in flexible generation, storage solutions, and demand side-flexibility is paramount to accommodating rebound-affected consumption patterns while achieving climate goals.
Moreover, temporal demand management emerges as a key lever. Aligning increased electricity use with periods of high solar production, as exemplified by the simultaneous rebound profile, offers a pathway to smooth wholesale price volatility and reduce reliance on fossil-fueled backup. These insights advocate for integrating smart grid technologies, dynamic pricing, and consumer education within energy policy frameworks to realign consumption behavior with clean energy availability.
This study’s holistic approach also brings to light the limitations of traditional, uniform cost allocation models for electricity markets. A system established during eras of steady marginal generation costs struggles to cope with increasing variability introduced by renewables and growing flexibility needs. Rethinking cost allocation schemes to reflect temporal and spatial heterogeneity in demand and supply conditions represents an essential policy challenge. By doing so, Europe can better manage the distributed costs of the energy transition while curbing inequity.
The temporal dimension of the solar rebound effect cannot be overstated. Shifts in household consumption to evening hours or wintertime, when solar generation dips, compel the system to mobilize expensive backup resources. These requirements manifest as elevated wholesale prices that indiscriminately affect all consumers, further exacerbating cross-subsidization concerns. The study’s sophisticated modeling captures these temporal interdependencies, providing a nuanced understanding of how seemingly beneficial solar adoption can inadvertently impose system-wide stresses.
Importantly, the findings resonate with earlier grid expansion analyses, corroborating the vulnerability of Central Europe’s energy infrastructure to demand perturbations coupled with renewable intermittency. These results suggest that regional policy coordination and targeted infrastructure investments are vital to ensure system resilience. They also emphasize that rebound effect timing and profile are as consequential as its magnitude in shaping infrastructure needs and consumer price outcomes.
Beyond technical and economic considerations, the social implications of this research are profound. Ensuring a just energy transition that fairly distributes the benefits and burdens of decarbonization remains a paramount political challenge. The solar rebound effect’s asymmetric cost distribution risks alienating lower-income households and exacerbating energy insecurity, potentially undermining public support for renewable energy policies. Policymakers must address these disparities through inclusive planning, equitable tariff reforms, and support mechanisms for vulnerable populations.
The analysis also sheds light on consumer behavior nuances, such as inaccurate perceptions of monetary savings from demand response programs, which influence solar self-consumption patterns. Addressing these informational gaps through targeted communication and behavioral interventions could enhance the effectiveness of rebound mitigation strategies. Integration of behavioral economics insights into energy policy design may offer innovative pathways to align consumer actions with system-wide decarbonization goals.
In conclusion, the solar rebound effect represents a multifaceted challenge that intertwines consumer behavior, market dynamics, infrastructure capacity, emissions trajectories, and social equity. Europe’s ambitious decarbonization agenda must grapple with these complexities to realize the full promise of solar energy. This study by Delic and Bucksteeg not only quantifies the scale and distribution of price and emissions impacts but also clarifies pathways for mitigating adverse consequences through targeted policy and technology solutions. Its findings call for a recalibrated approach to energy transition planning—one that accounts for human factors, temporal demand shifts, and the uneven geography of generation and consumption.
As Europe strides toward 2045 decarbonization objectives, addressing the solar rebound effect emerges as a critical frontier. By embracing dynamic demand management, revisiting regulatory frameworks, investing in flexible infrastructure, and prioritizing equity, the continent can safeguard the environmental and social promise of clean energy. This research paves the way for a more resilient, cost-effective, and just energy future that recognizes complexity rather than simplistically relying on technology alone.
Subject of Research: The impact of the solar rebound effect on electricity prices, infrastructure requirements, carbon emissions, and energy equity in Europe’s decarbonization pathway.
Article Title: Implications of the solar rebound effect for the European energy transition
Article References:
Delic, M., Bucksteeg, M. Implications of the solar rebound effect for the European energy transition. Nat Energy (2026). https://doi.org/10.1038/s41560-026-02031-8
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s41560-026-02031-8
Tags: consumer behavior and solar adoptiondecarbonization and energy equityelectricity market dynamics Europeenergy infrastructure stress from solarEurope energy transition challengesfuture projections of solar energy impactsimpact of solar PV on electricity pricesrenewable energy paradoxsolar energy and increased consumptionsolar power grid flexibility issuessolar PV system economic incentivessolar rebound effect
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A presentation on a potential community solar project for Harvey was interrupted Monday by a power outage, postponing a preliminary City Council vote on the project.
The proposal is from Marquis Matilla, who said his business, Evolved Living, develops community solar projects in underserved communities.
“What happens is, not everyone can get solar panels on their homes,” Matilla said. “We would develop this solar farm, and that solar farm feeds energy back to ComEd’s grid. ComEd then takes that electricity, credits it and everyone who’s a subscriber to that community solar project is basically a partial owner.”
Residents would not have to pay anything to subscribe, Matilla said.
The first step to judge the project’s viability would be a feasibility study. Matilla stressed there are several steps and studies before anything is certain.
“That feasibility study means that we will apply with ComEd and make sure that there’s enough space on the substation to accommodate this solar farm,” Matilla said. “This is the very preliminary stages of the project, OK? But it is a very good project and we’re hopeful.”
Matilla said there are multiple sites under consideration for the solar farm, but said choosing one would rely on the result of the feasibility study. Which site was chosen would determine the size of the solar farm, he said.
“It’s not definitive yet. There are a couple sites that we are talking to the City Council to possibly see if they are suitable for the project,” Matilla said. “One site is a little bit larger, one site is a little bit smaller, but definitively we won’t know until we get to the feasibility phase.”
Fourth Ward Ald. Tracy Key asked Matilla if there were other comparable community solar projects nearby in the south suburbs.
Matilla said there were a few community solar projects in Ford Heights, but that there were not many in and around Chicago due to a lack of available space.
“It’s difficult to find land in the city,” Matilla said. “The closest places that you could probably go to see these projects are central Illinois, Joliet, Rockford area, where they have more available.”
A proposed 6,100 acre solar farm in Will County, near Manhattan, has drawn opposition from residents, who filed a lawsuit to stop the project earlier this month.
Matilla said the community solar project was made possible by a grant from the Illinois Department of Commerce and Economic Opportunity.
“That grant is a new program that the state is offering, so projects like this wouldn’t be even economically viable,” Matilla said. “This type of program for a municipality and community is a new program that is possible because of the grant.”
Matilla said that the city would generate revenue both from renewable energy credits and from leasing the land.
“This would cost the city nothing,” Matilla said. “In fact, we’re taking vacant, unused, contaminated land and turning it into an asset that the city and the residents can take advantage of.”
Acting Mayor Shirley Drewenski emphasized the vote would just be on whether the city was interested in pursuing the project, starting with the feasibility study.
“Now, what happens if the land is perfect?” Drewenski said. “Then what will happen is they will come and get the permits, they will go through the zoning and making sure everything is OK. Then it will come back to the City Council for a vote.”
The vote did not happen Monday due to the power outage cutting the meeting short, and no public comment was heard.
Harvey’s next City Council meeting is scheduled for 7 p.m. on April 27 at Harvey City Hall, 15320 Broadway Ave.
elewis@chicagotribune.com
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With an output of 800 megawatts, the Springwell Solar Farm supports the UK’s efforts towards cleaner and more secure energy sources.
Located in Lincolnshire, the Springwell Solar Farm is expected to generate enough electricity to power more than 180,000 homes each year.
By approving the Springwell Solar Farm, the UK government has officially approved 25 national clean energy projects.
Altogether, these projects hope to generate enough electricity to power over 12.5 million homes.
By increasing the share of homegrown energy, the UK government aims to improve resilience against global supply disruptions.
Recent geopolitical tensions, including conflicts in Eastern Europe and the Middle East, have highlighted the risks of dependence on international fossil fuel markets. Fluctuations in supply and pricing have contributed to energy insecurity and rising costs for households and businesses.
In response, the UK has intensified efforts to diversify its energy mix and invest in renewable infrastructure. Large-scale solar projects like Springwell Solar Farm are seen as a key solution, offering stable and predictable energy generation without exposure to fuel price volatility.
The expansion of solar capacity is also expected to contribute to long-term reductions in electricity bills by increasing the availability of low-cost power.
The approval of Springwell Solar Farm builds on a series of recent government initiatives designed to boost solar adoption nationwide. These include measures to introduce plug-in solar technologies in retail environments and plans to make solar panels a standard feature on new homes in England.
The government is also moving ahead with plans to accelerate renewable energy actions, with the next round scheduled for July.
This is intended to bring more projects online faster and maintain momentum in the clean energy transition. This aims to make solar energy more accessible at the utility and household levels, ensuring that the benefits of renewable power are widely shared.
The Springwell Solar Farm is expected to deliver economic benefits through job creation during both construction and operation phases. It also supports the UK’s environmental goals by reducing carbon emissions and advancing progress toward net-zero targets.
As one of the largest solar developments in the country, Springwell Solar Farm represents a significant investment in sustainable infrastructure. Its approval highlights the growing role of solar energy in shaping the UK’s future energy landscape.
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US solar cell maker Suniva investing $350 million in South Carolina expansion  Reuters
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As the U.S. and China continue trade negotiations in Stockholm, one thing is clear: rare earth elements (REEs) are a powerful bargaining chip and a critical part of the future of American energy and our economy. 
The U.S. remains heavily dependent on China for access to these materials, many of which play important roles in the systems that support clean energy, like battery storage, inverters, and grid technologies. While the two nations aim to temporarily ease export restrictions, this moment also highlights that America’s long-term energy security depends on building out our own rare earth mining and processing capabilities. 
REEs are a group of 17 metallic elements on the periodic table. They are considered “rare,” not because they are scarce in Earth’s crust, but because they are dispersed and found in low concentrations, making them difficult and costly to mine. 
Their unique magnetic, phosphorescent, and catalytic properties allow them to play an essential role across many industries, from smartphones and fighter jets to MRI machines, and increasingly in the energy industry. 
To be clear, REEs are not used in solar panels themselves. However, they are important elements in grid technologies that support and stabilize clean energy systems. 
There are no rare earth elements directly used in photovoltaic (PV) solar modules, but they are key components of the inverters that convert direct current (DC) electricity generated by solar panels into alternating current (AC) electricity used on the electric grid. They also play a role in the converters that manage voltage and current flows in solar + storage systems. Specifically, yttrium, lanthanum, and sometimes cerium oxide are used in ceramic capacitors, and neodymium is used in magnets for internal sensors and fans. 
Grid-scale batteries need advanced cooling systems to operate safely and reliably. Rare earths like neodymium and dysprosium are used in the magnets that power fans and pumps. Other elements, like cerium and lanthanum, are used in sensors that monitor and manage battery temperature.  
Even though these materials are used in tiny amounts (on the gram-to-milligram scale), they are essential to preventing BESS systems from overheating.  
REEs are also found throughout the broader electric grid, albeit in specialized use cases. Sensors and actuators (i.e., motorized switches, tap changers, etc.) used in smart grid systems, substations, and other infrastructure often include compact, high-performance magnets made with REEs. 
Despite limited applications, REEs play an important role in advancing grid modernization and next generation technologies as more solar and BESS systems come online.  
As of 2024, the U.S. depends on imports to satisfy 80 percent of domestic demand for REEs. About 70% of those imports come directly from China, and even when we source from other countries like Malaysia or Japan, those nations often rely on rare earth materials that originated or were processed in China.  
China not only dominates the production of rare earths, but it also controls many of the refining and processing steps needed to turn raw minerals into usable components. In fact, even the one active, commercial-scale REE mine in the U.S. — Mountain Pass Mine owned by MP Materials in California — still sends some of its output to China for final processing. 
This has led to growing interest in reshoring key parts of the rare earth supply chain to reduce bottlenecks, improve domestic capacity, and strengthen U.S. energy security. The recent commitment by the U.S. Department of Defense to take an equity stake in MP Materials, along with a 10-year magnet offtake agreement and 10-year price floor for MP’s neodymium-praseodymium oxide, is a key example of U.S. commitment to onshoring rare earth supply chains.  
The current trade talks in Stockholm may provide some short-term relief by easing export restrictions, but this won’t solve the central problem. Without a reliable domestic supply chain that included mining, refining, and processing, the U.S. will remain vulnerable to geopolitical tensions and supply disruptions. 
The Trump administration has already taken steps to mobilize resources and expedite permitting for REE production, but more investment is needed to aggressively scale up. In the meantime, securing access to these materials through a trade agreement is essential to ensure the U.S. can continue manufacturing the technologies that power our electric grid, support national security, and drive economic growth. 
Battery Energy Storage Systems (BESS) are among the most thoroughly tested and code-governed energy infrastructure deployed, and their safety record is improving dramatically as the technology matu…
RICHMOND, VA — Yesterday Governor Abigail Spanberger signed a slate of energy bills that will help lower Virginians’ electricity bills, strengthen grid reliability, and boost the local econ…
“AI infrastructure companies, they’re facing grid interconnection queues that can be 5-7 years long, which is really untenable when their competitors are getting to move at startup’s pac…
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Australian mining giant Fortescue has accelerated its timeline for deploying a 1.8GW renewable energy portfolio, paired with 4-5GWh of batteries, which it now expects to begin commercial operation by 2028.
The portfolio will consist of 1.2GW of solar PV capacity, alongside 600MW of wind, which Fortescue dubbed “the world’s largest off-grid system” for heavy industry. The miner expects the first 290MW of solar capacity to be operational by early 2027 and a Fortescue spokesperson told PV Tech that it plans to use LONGi solar panels, BYD batteries and Envision Energy wind turbines across the portfolio.
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Fortescue has already announced plans to deploy 4-5GWh of battery energy storage systems (BESS) in Western Australia, and these batteries will help deliver “24-hour periods”, where fossil fuels are not used to power mining equipment, across the operations by late 2027.
The project accelerates the company’s ‘Real Zero’ decarbonisation plan, which initially aimed to reach zero emissions across its scope one and two emissions by 2030. In comments made on LinkedIn, Fortescue’s health and safety and R&D superintendent Daniel Hewitt said that the project would require US$6.2 billion in capex, but that it would “pay itself back in a few years” by slashing diesel-related opex costs by US$818 million per year from 2030.
The portfolio will be built in and designed to provide power for the company’s extensive mining operations in the Pilbara region of Western Australia. Earlier this year, the company started construction at a 440MW solar PV project in the region, which it claimed would be the largest such project in the state upon completion in 2028 and forms part of the renewables-plus-storage hub.
In December 2025 CEO Dino Otranto claimed the company had secured low “pricing that hasn’t really been seen” for its battery projects in the region.
Western Australia is home to some of the world’s deepest iron ore reserves, and these deposits have formed the bedrock of Fortescue’s work since its inception. The miner shipped a record 198.4 million tonnes of iron ore in the 12 months to June 2025, up from 191.6 million tonnes in the previous 12 months, and Fortescue said last July that it expects to grow iron ore export volumes by up to 3% in the next year.
On the same day as it announced its record mining exports, Fortescue cancelled two green hydrogen projects under development in the US state of Arizona, citing the Trump administration’s hostile attitude towards the energy transition. Following these cancellations, and the announcement of record iron ore sales, the value of the company’s shares increased by 4%.

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MyNu Energy has launched a mobile solar generation and battery energy storage system designed to reduce reliance on diesel as the Middle East crisis continues to cause concerns about fuel shortages and uncertain energy costs.
Image: MyNu Energy
From pv magazine Australia
Australian energy solutions provider MyNu Energy has unveiled a trailer-mounted solar and battery energy storage system designed to replace traditional diesel-fuelled generators as businesses look to manage energy costs and reduce exposure to fuel supply disruptions.
The PowerQub-M Mobile Power Station combines a 3 kW demountable solar array with battery storage options ranging from 60 kWh to 240 kWh and can deliver between 25 kVA and 160 kVA of power depending on configuration, with both three-phase and single-phase outlets.
The company said the mobile power plant can also be customized to meet specific customer requirements, including integrating with larger ground-mounted portable solar systems.
MyNu said the PowerQub-M is designed and assembled in Australia, with company co-founder Shaun Nugent highlighting that the ability to deploy power quickly in remote or temporary locations was a key driver behind the design.
“We wanted to create something that could be moved easily and set up quickly wherever power is needed, whether that’s a construction site, a farm, an event or even disaster recovery situations,” he said. “The trailer-based design means it can be deployed rapidly without the need for permanent infrastructure.”
MyNu expects demand across sectors including construction, agriculture, remote infrastructure, events and emergency response, particularly in regions where diesel logistics remain challenging.
“This isn’t just about sustainability, it’s about practicality,” Nugent said. “Businesses need power they can rely on, and increasingly they’re looking for solutions that aren’t tied to diesel.”
Nugent said the system also offers advantages for equipment performance, with battery-based power delivering more stable electricity compared to some diesel generators.
MyNu co-founder John Myler said the launch of the mobile energy plant comes amid a growing shift toward alternative power solutions as businesses look to exert more control over their energy use and costs.
“We’re hearing from farmers, construction operators and regional businesses who are struggling with both the cost and availability of diesel,” he said. “Power is critical to their operations, and they need solutions that are reliable, but also independent of fuel supply chains.”
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Suniva Announces New South Carolina Solar Cell Manufacturing Facility  Business Wire
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You’d have thought the quarter-sized hail that wreaked havoc on solar farms would have served as a giant red flag in between the rails these cathedrals to the Religion of Climate careened on. For my Lisle AWFL readers, ‘wreaking havoc’ means to destroy them and rendering them scrap’.
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If this bill passes, say goodbye to local control over all Illinois parks and expect to see open drug and alcohol use, needles, no sanitation and fire hazards, but no ordinary park users.

Summary and audio linked here.
Chicago’s political leadership is floating a pension buyout program as evidence it is seriously addressing the city’s thirty-six-billion-dollar unfunded pension liability, but Mark Glennon, founder of the Illinois policy research organization Wirepoints, said that the proposal moves debt from one column to another rather than reducing it, and that the broader fiscal picture facing the city continues to deteriorate across every measurable dimension. Audio here.
Expect no retraction or apology. This what they do.
The state’s existing buyout program for its own pensions is the precedent for Chicago, which should be a warning: Look out for similar exaggerated claims and shoddy analysis.
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