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Scientific Reports volume 16, Article number: 9030 (2026) Cite this article
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The utilization of renewable energy sources significantly increased in response to the growing global energy demand. Rising concerns about the environment, the photovoltaic (PV) systems emerged as prominent and widely adopted among all other renewable energies. The installation cost of PV systems is more however, the recent advancements in PV technology have made them feasible for a wide range of applications. In addition to module and inverter efficiencies, the overall efficiency of a PV system also depends upon the efficiency of the tracking method. The conventional and intelligent methods are experiencing low tracking efficiency under different irradiation and temperature conditions. PV grid integration can be done in two methods. The single stage grid connected systems can experience problems related to power quality and stability of the DC link voltage at low irradiance levels. To limit this problem, two-stage systems are preferable for maintaining a stable DC-link voltage. This research evaluates several MPPT strategies, including conventional approaches (perturbation and observation, incremental conductivity) and intelligent techniques (fuzzy logic controllers, ANFIS), and proposes a hybrid AGORNN controller, that is based on the Adaptive Grasshopper Optimization Recurrent Neural Network strategy. The performance of each method is assessed through parameters such as PV maximum power (Pmpp), DC link voltage (Vdclink), MPPT tracking efficiency η_MPPT (%), Vdc Ripple Vdcr (%), response time (Tres), overshoot Osh (%), utilization efficiency η_ut (%), THD, cost analysis, and payback period (PB). The results of simulations, which were examined using MATLAB/Simulink (R2023b), and compared with existing hybrid MPPT control methods. It exhibits that the proposed AGORNN controller is more effective than the standard and intelligent MPPT. The novel hybrid MPPT controller was examined under the conditions of STC and different irradiance and temperature, which were referred to as PTC. In STC tracking and conversion efficiencies are 99.86 and 96.55, respectively. The efficiencies under PTC are 96% and 91.50%, which ensures a promising solution to high efficiency grid-connected PV systems. The LCOE and Payback Period are also estimated and compared with other methods.
The government is working extensively to make its energy industry clean and sustainable. Hence, there is a big shift in use of fossil fuels, which are mostly coal to the high utilization of renewable energy sources like solar, wind, and biomass in India. This move is configured towards fulfilling the increasing electricity needs caused by a fast-growing economy that is estimated to be one of the largest in the world by 2050 with the aid of the Ministry of Energy and advice of the national policy documents such as the National Electricity Plan and the Renewable Energy Mission, and to lessen the reliance on fossil fuels. India is the most populous country in the world and one of the fastest-growing economies, and thus it has the largest electricity demand, and listed in top 10 most energy-consuming countries in the world, illustrated in the Fig. 1. India has the highest potential of electricity demand growth in the coming decade compared to any other major economy¹. In order to see that this ever-increasing electricity demand can be sustainably satisfied, the central government, as well as individual states, have initiated many programs that are aimed at promoting the growth of grid-connected solar electric generation systems including large-scale solar parks, residential rooftop photovoltaic systems as well as rural solar electrification systems. Although these initiatives prove the intent of India to reduce the emissions of carbon (as well as reflect the global trend of adopting renewable energy technologies), their effectiveness, reliability, and cost-efficiency, when applied in different climatic conditions, require further research. The power of PV should vary with the size of solar radiation as it is received by the sun and the temperature in the surroundings^1,2,3. However, the environmental conditions are consistently changing based on the geographic location, time of the day and seasonal patterns. Therefore, it is important to ensure that the PV system will run at Maximum power point (MPP) to maximize the energy collection. Maximum power point tracking (MPPT) algorithms are usually used to track MPPs⁴. Nevertheless, it is not an easy task because the relationship between the current and voltage in all the PV modules and the fast variation of the current and voltage under partial shading and weather variations are non-linear⁵. Moreover, the commercial PV modules are relatively inefficient and which made important to create efficient MPPT strategies, can reduce oscillations and energy loss and promote the high accuracy of MPP tracking. As the global trend towards the utilization of renewable energies reflects the increasing trend. The International Energy Agency (IEA) has forecasted that the total world-wide demand for electricity will be 281 Terawatt-hours (TWh) by 2025, representing an average annual growth rate of approximately 3 percent per annum between 2023 and 2025^6,7. Therefore, advancing intelligent MPPT techniques and reliable inverter control methods is necessary to improve not only the efficiency of the conversion process and the stability of the PV-based system under dynamic operating conditions but also to improve the long-term cost-effectiveness of the overall system^8,9,10.
Source: World Energy & Climate Statistics—Yearbook⁶.
The world’s top 10 most energy-consuming countries.
Among the many operational challenges experienced in grid-connected PV systems, synchronizing the inverter output with the utility grid, specifically matching the magnitude of the voltage and the phase angle of the voltage is essential to provide for smooth and continuous power flow between the PV system and the utility grid^11,12. Generally, two primary configurations exist for inverter designs one is single-stage and the other is two-stage configurations⁹. In a single-stage configuration, the inverter simultaneously performs both the DC-to-AC conversion function and the MPPT function. While this configuration reduces the complexity of the system design, it experiences difficulty in maintaining a sufficient DC-link voltage when the irradiance levels are low, resulting in poor and unstable operation of the power quality^1,9,12,13. In order to address these limitations, this paper proposed AGORNN based MPPT controller with two-stage conversion configuration. In this configuration, a DC-DC boost converter initially increases the PV-array voltage above the nominal voltage of the grid, thereby providing a stable DC-link voltage. This is followed by DC-AC conversion potential that is carried out by a three-phase Voltage Source Inverter (VSI) that enables the injection of high quality power into the grid^14,15. The primary focus of this paper is to come up with high performance controller of a two-stage three-phase VSI based grid connected PV station that can deliver strong MPPT functionality, enhanced dynamic response and enhanced grid compatibility. A comprehensive mathematical model with dynamic behavior on each of the converter stages and their interaction between the stages and the grid was drawn. Simulation studies in MATLAB/Simulink were used to assess the performance of the system under the conditions of different irradiance and temperature.The application of an adaptive AGORNN-based MPPT controller in this study, together with conventional controllers (P&O and INC) in STC were developed and tested. Comparison of performance involved monitoring accuracy, convergence rate, minimization of power ripples and measures of LCOE and PB that are cost-effective. In contrast the proposed paper gives an extensive evaluation of the possible viability and functionality of the advanced MPPT strategies in grid-connected PV systems.
The grid connected solar photovoltaic (PV) systems have recently gained a lot of momentum both due to the enhancement of the efficiency and economic viability of the PV panels. because they do not give off air pollutants or emissions^{1,2,3,4,5,6,7,8,9,14,15,16,17,18,19}. In addition, the growing number of electric vehicles (EVs) on the road, and their associated EV charging stations, has also led to a massive increase in solar PV system installs in both India and worldwide; this will likely lead to a greater demand for solar photovoltaic (SPV) systems²⁰. Solar PV was averaging approximately 25% per year until 2014²¹; however, in the last decade, the rate at which the solar PV industry is growing has been dramatically higher, with the industry now averaging around 42% per year, thanks to strong governmental support (i.e. tax breaks and rebates), low cost PV panels, and high demand for renewable energy^6,7,8,16. Nevertheless, the large scale integration of solar PV systems into the electrical grid results in several technical challenges with regards to the power quality and stability of the grid, including, but not limited to, voltage variations, frequency deviations, and harmonic distortion^{8,9,11,21,22,23}.
India’s installed capacity of solar power has grown substantially as indicated by the data available from the Indian Government’s Ministry of New and Renewable Energy (MNRE). By July 2025, India had installed 119.02 GW of solar power (See Fig. 2)¹⁶. The sizeable growth indicates that there is enormous potential for the use of grid-connected PV systems in India and that there is a pressing need to conduct research to develop advanced control equipment to enable the PV systems to operate quickly, reliably and stably.
Solar energy installed capacity of the top 10 states in India.
Fan Xie et al. reported that for low-power applications, a two-stage inverter-fed PV system has certain benefits in terms of cost, maximum power extraction, and DC bus point compared with a centralized inverter^24,25.
While many MPPT algorithms exist, the Perturb and Observe (P&O) technique remains one of the most widely used control methods in industry^9,17,26. However, a significant disadvantage of the P&O method is its tendency to cause power oscillations even under STCs, which can introduce interharmonics into the grid-side current^11,17,27. Furthermore, traditional MPPT methods struggle to quickly and accurately track the global maximum power point (MPP) under partial loading conditions (PSCs). To solve this kind of problem, new studies have been conducted on more sophisticated methods. Indicatively, research in²⁸ suggested the MABC algorithm and discussed on the failures of the traditional ones including P&O, particle swarm optimization (PSO) and ABC algorithms. The literature review is essential in carrying the various classifications and drawbacks of the available methods of MPPT^29,30,31,32. Hybrid MPPT approaches have been offered by other researchers as well^3,4,14,33, and one of them offers a fourfold classification according to the respective advantages and disadvantages⁹. When designing any MPPT control strategy to a PV system, various important issues are to be taken into consideration, including tracking efficiency, response time, overshoot, power oscillations, complexity of design²⁹. New designs of inverters have also been created by the development of advanced power controlling units. Voltage source inverters (VSIs) have so far been widely used in order to interface the PV modules with the grid.
A comparative study on the baseline strategies of MPPT has been carried out by Bidyadhar Subudhi et al. in 2013 where the authors have discussed hill-climbing, incremental conductance, short-circuit current, open-circuit voltage, and ripple correlation strategies¹⁹. The research came to the conclusion that these techniques are fast and simple to apply, but in ununiformed irradiance, they are not effective. They find it hard to follow the MPP within the partial loading situations (PSCs) and this decreases their energy efficiency. To address these challenges effectively and find optimal solutions, Debasish Dhua et al.³⁴ emphasized the need to develop and employ advanced optimization algorithms.
To address these challenges, researchers have categorized different MPPT techniques into four main groups, classic, intelligent, optimization, and hybrid based on their principles and strategies (Fig. 3). According to a comprehensive literature review⁹, hybrid methods are the most effective for enhancing tracking speed, stability, and accuracy. An in-depth overview of the implementation of small- to medium-scale solar PV plants (ranging from 1 to 100 kW), particularly for rooftop installations connected to the grid, is provided in⁵. A detailed literature review of different MPPT techniques is also summarized in Table 1.
Classification of tracking techniques based on operational strategies.
One of the aspects of the performance comparison of Maximum Power Point Tracking (MPPT) controllers in grid, connected photovoltaic (PV) systems that are worthy of Levelized Cost of Energy (LCOE) and payback period consideration is the economic impact of various algorithms’ efficiency. The MPPT controllers are vital for the PV system power output optimization under changes in environmental conditions like solar irradiance and temperature. The main MPPT techniques analyzed are Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Control (FLC), and more advanced methods like Adaptive Neuro Fuzzy Inference Systems (ANFIS), Particle Swarm Optimization (PSO). perturb and observation (P&O) algorithm has been widely used due to ease of implementation, but it fails to determine the true MPP under partial shading condition and more power oscillates about the MPP in STC condition The INC method is a bit more advantageous than P&O, but there is always a trade-off between accuracy and speed for these algorithms, computing time is more, Fuzzy/ANN/ANFIS system-specific, has moderate sensitivity and is also complex in design and, it remains susceptible to drift phenomena under rapidly varying operating temperatures and irradiance levels. This can be overcome by bio-inspired algorithms or GA algorithms, (PSO) is a metaheuristic approach, it has good global search ability However, the PSO major drawback is. the velocity equation consists of stochastic variables so the global best value is varying uncertainly, and also PSO search starts with random initial guess in duty cycle.Hence, initial duty cycle boundaries needs to be confined for faster convergence and improved efficiency of PSO method.There are lot of spam based algorithms are available like ACA,BA,BAT-A,GWO, but each one have some limitations as per Literature Survey. Hence in this paper proposed an hybrid-based MPPT techniques, traditional/ optimal MPPTs are combined with intelligent MPPT’s. The tracking strategy is separated into two steps in this combination MPPT, estimating the MPP in the first stage and fine-tuning that MPP using advanced algorithms in the second stage.
In recent years, as the number of PV installations has increased, research papers have begun to incorporate cost and payback period analyses. This analysis is typically performed before the installation of grid-connected rooftop and ground-mounted PV systems, as well as for hybrid systems. For example, Saleeb et al.⁵⁶ discussed the optimization, sizing, loss of power supply methods, and cost analysis of hybrid renewable energy systems via the levelized cost of energy (LCOE) method. The paper also details cost functions, including installation, annual operation, and maintenance costs.
In addition to reducing the energy pay back time (EPBT), efficient MPPT controllers can reduce the LCOE by maximizing the total amount of energy generated from a photovoltaic system by running at its maximum power point as much as possible. Furthermore, the application of advance hybrid algorithms could potentially lower the power loss and oscillation associated with an MPPT controller resulting in longer operation of the system and reduced operational cost^{48,57,58,59,60,61,62}. Hence, a hybrid MPPT is required to get higher economic returns. Payback period is also an important factor for the system owner to know the time required to recover his or her initial investment^60,63. The payback period can be significantly dependent upon how effectively the power is extracted from the solar array. There is no doubt that traditional methods (i.e., P&O and IC) are still the most common approaches to MPPT control today due to their simplicity and ease of implementation, but they are not performed well in cloudy conditions. Therefore, the potential exists for advanced MPPT control algorithms to provide better economic benefits to the owner of the grid-connected PV system than traditional methods through greater efficiency and stability.
The authors presented a cost function based on established methods such as the net present cost (NPC) and life cycle cost (LCC). A detailed literature review of various cost analysis methods is provided in Table 2. The authors of^58,59,60 highlighted that the calculation of a system’s levelized cost of energy (LCOE) considers four key factors: initial investment, life cycle cost, maintenance, and replacement costs.
The LCOE method is commonly used by solar PV companies and agents to provide installation proposals to customers. A modified LCOE method and its comparisons, as discussed in⁶⁴, are primarily used in power purchase agreements (PPAs). This research adopts the primary LCOE method, as it is best suited for systems installed at load centers, such as college buildings, homes, hospitals, or small businesses. This application, which often includes rooftop installations, eliminates transmission costs and significantly reduces maintenance overheads.
The payback period (PP) is also analyzed in this research. The PP is defined as the ratio of the total invested cost to the annual return^63,65,66. These references also address the relationship between PP and daily direct normal irradiation under various conditions. On the basis of a practical data analysis at BVRIT, Telangana, the typical payback period is found to be 4-to-4.2 years. For this research, a conservative value of 4 years is considered.
The novelty of this study is primarily in the integration of a hybrid MPPT controller combined with RNN and AGO operation. Consideration of one year long term, practical test scenarios and a single comprehensive techno economic analysis, elements that have not been thoroughly dealt with in the current literature.
Use of real, high, irradiance practical test conditions (PTC)
This study sets itself apart from the majority of the literature where MPPT performance is usually evaluated only under standard test conditions (STC) or irradiance profiles derived from simulations. In this work, the authors used real operation data taken at the BVRIT Narsapur campus with calibrated instruments, a pyranometer, RTDs, and a CR3000 data logger. The measured light values often go beyond 1000 W/m² which corresponds to extremely high, irradiance conditions that are typical of the practical Indian climates and have hardly been addressed in the MPPT literature so far. The monthly average irradiation and temperature profiles based on the long, term recording (September 2018 to August 2019) are taken into the simulation model, thus allowing a very realistic evaluation of the controller’s performance in actual field conditions.
Novel AGORNN, based hybrid MPPT controller architecture
The paper presents a new concept hybrid MPPT controller named AGORNN, which integrates a RNN for power prediction and an AGO algorithm for PI controller tuning. An additional benefit of this architecture lies in the fact that the RNN accounts for the time, related characteristic of power variation from the PV, while the AGO algorithm continually changes control settings to speed convergence and improve tracking accuracy. The combined dual, function solution boosts not only the tracking efficiency but also the dynamic performance to a level exceeding that of traditional (P&O, INC, IINC) as well as smart (Fuzzy, ANFIS) control methods.
Expanded performance metrics beyond conventional MPPT evaluation
Rather than maximum power extraction and efficiency that have been the primary focus of published works, this paper additionally presents major improvements in the following dynamic performance indicators, are rise time (Tr), time to reach maximum power after sudden irradiance change, rise time at DC link voltage, overshoot behavior, and overall conversion efficiency both in STC and PTC. The authors state that these factors expose aspects related to the stability of the controller, the transient behavior of the controller, and the grid that have hardly been covered in MPPT literature.
Demonstrated long, term energy yield improvement
The annual energy yield breakdown reveals that the newly introduced AGORNN controller can bring about a 5.15% increase in total energy generation relative to the existing, conventional methods. The AGORNN, based system yields 26, 349 kWh/year which is more than the P&O, INC, IINC, Fuzzy, ANFIS as well as other hybrid MPPT methods and thus proves that it is the best combination for long, term performance in real weather conditions.
Unified MPPT LCOE Payback Period (PB) Analysis with Degradation Consideration The major innovation of this research lies in the combined examination of MPPT effectiveness, Levelized Cost of Energy (LCOE), and Payback Period (PB) in single frame work. As per the existing literature, the works have been focusing on these matters individually. Besides, this work, unlike previous ones, quantitatively integrates the effect of PV module degradation in the economic analysis while determining the output over the system warranty period, thus leading to a more realistic and feasible cost estimation. The implemented algorithm brings down the levelized cost of energy to 2.049 per unit with a short payback time of 3 years 7 months, thereby proving the solution to be technically and economically viable.
Comprehensive benchmarking with recent literature (2024 to 2026)
The AGORNN controller proposed here is extensively compared against a list of state, of, the, art hybrid and triple hybrid MPPT techniques present in the recent literature (2024 to 2026), such as ABB+GA+BA, ANN+SMC, FA+ANFIS+P&O, ANFIS, and ANFIS+PSO. The findings suggest the enhancement of the tracking efficiency (99.86% under STC and 96% under PTC), lowering of the oscillations, the overshoot being almost negligible and the shortening of the response times thus the proposed technique is a viable, versatile solution for the grid connected PV systems.
The maximum-power-point tracking (MPPT) process is formulated as an optimization task that seeks to extract the highest power from the PV array under dynamic irradiance ((G)) and temperature ((T)) conditions.
The single-diode PV model is expressed as
where ({I}_{text{pv}}) and ({V}_{text{pv}}) are the array current and voltage; ({I}_{text{ph}}) the photo-current; ({I}_{0}) the saturation current; (R_{s}), (R_{p}) the series and shunt resistances; and (V_{T} = kT/qN_{s}) the thermal voltage.
The instantaneous PV power is
and the optimization objective is
where (D) denotes the boost-converter duty cycle constrained by (0le Dle {D}_{text{max}}).
For a DC–DC boost converter,
The control target is to generate (D^{*}) such that (P_{{{text{pv}}}} to P_{{{text{mpp}}}} left( {G,T} right)) while minimizing oscillations and ensuring DC-link voltage stability.
The proposed Adaptive Grasshopper Optimization Recurrent Neural Network (AGORNN) combines:
A recurrent neural network (RNN) that predicts the reference maximum power ({P}_{text{ref}}) using real-time (G) and (T);
An adaptive grasshopper optimization (AGO) algorithm that tunes the proportional–integral (PI) gains (({K}_{p},{K}_{i})) and RNN weights for optimal dynamic response.
At each time step (t):
where ({mathbf{x}}_{t} = [G_{t} ,,T_{t} ]^{T}) and (fleft( cdot right)) is the activation function.
The RNN is trained to minimize
AGO further refines the initial weights to prevent local minima and enhance generalization.
The operation of the proposed hybrid maximum power point tracking (MPPT) controller is illustrated in the flowchart shown in the Fig. 4. The controller integrates a recurrent neural network (RNN) with an adaptive grasshopper optimization (AGO) algorithm and a PI control structure to achieve accurate, fast, and stable maximum power extraction from the photovoltaic (PV) system under dynamically varying environmental conditions. The complete control strategy operates in a closed-loop framework, ensuring continuous tracking of the global maximum power point (GMPP) despite variations in solar irradiance and temperature conditions.
AGORNN MPPT algorithm flowchart.
Initially, the PV system behavior is described using the mathematical models of the PV array and the associated power electronic converter, as represented by Eqs. (1–4). At each sampling instant, the system measures the solar irradiance (G), cell temperature (T), PV voltage (Vpv), and PV current (Ipv). Using these measurements, the instantaneous output power of the PV array is computed as (Pact), which represents the actual operating power of the system. This real-time power measurement forms the primary feedback signal for the MPPT control loop.
The measured environmental variables, namely irradiance and temperature, are then supplied to the recurrent neural network (RNN), whose mathematical formulation is described in Eqs. (5–6). The RNN acts as an intelligent reference power estimator by learning the nonlinear relationship between environmental conditions and the corresponding maximum power point of the PV system. Due to its recurrent structure and internal memory states, the RNN is capable of capturing system dynamics and temporal dependencies, enabling it to predict the optimal reference power (Pref) corresponding to the maximum power point. Unlike conventional MPPT techniques, the RNN directly estimates the MPP operating point, thereby eliminating steady-state oscillations and significantly improving tracking speed and accuracy.
Subsequently, the predicted reference power (Pref) is compared with the measured actual power (Pact) to generate the power tracking error, expressed as (e(t)). This error signal quantifies the deviation of the system from the desired maximum power operating condition and serves as the input to the PI control stage. The PI controller, modeled by Eqs. (7–9), processes this error to produce a control signal that regulates the operating point of the PV system. The proportional term provides fast dynamic response, while the integral term eliminates steady-state error, thereby ensuring stable convergence toward the reference operating point.
To overcome the limitations of fixed-gain PI controllers under rapidly changing environmental conditions, the proposed system employs an adaptive grasshopper optimization (AGO) algorithm for real-time tuning of the PI controller gains. The AGO model, defined by Eqs. (10–11), initializes a population of grasshopper agents, where each agent represents a candidate solution vector consisting of the PI parameters (K_p, K_i). A fitness function based on tracking error, transient performance, and stability criteria is evaluated for each candidate solution. Through adaptive social interaction mechanisms, including attraction, repulsion, and adaptive coefficient control, the grasshopper population iteratively updates its positions in the search space. The inclusion of adaptive mechanisms, crossover, and mutation operations enhances global exploration and local exploitation capabilities, thereby avoiding premature convergence and local minima. At the end of the optimization process, the AGO algorithm yields the optimal PI gains (Kp^opt, Ki^opt), which are dynamically updated in the control loop.
The optimized PI controller output is then used to estimate the duty cycle of the DC–DC converter using the duty cycle estimation formulation given in Eq. (13). This duty cycle is converted into a PWM switching signal, which drives the semiconductor switch of the DC–DC converter. By regulating the duty cycle, the converter adjusts the PV operating voltage and current, effectively shifting the operating point of the PV array toward the predicted maximum power point.
As the converter operates, the updated PV voltage and current are measured again, and the new actual power is estimated. This updated power is fed back into the control loop, where it is continuously compared with the RNN-predicted reference power. The closed-loop structure ensures that the system iteratively minimizes the power error and converges toward the maximum power operating condition. A convergence condition is evaluated to determine whether the maximum power point has been reached. If the tracking error falls below a predefined threshold, the system is considered to have achieved MPP, otherwise, the optimization and control cycle continues.
The coordinated operation of the AGO-RNN, and PI controller forms an intelligent hybrid MPPT architecture in which the RNN provides fast and accurate reference power prediction, the AGO ensures adaptive and optimal tuning of control parameters, and the PI controller guarantees stable and smooth regulation of the converter dynamics. This synergistic integration enables rapid convergence to the maximum power point, eliminates steady-state oscillations, improves dynamic response, and enhances robustness against environmental uncertainties.
Overall, the proposed hybrid MPPT controller establishes a fully adaptive, intelligent, and closed-loop control framework for PV energy conversion systems. By combining data-driven learning through RNN with bio-inspired adaptive optimization through AGO, the system achieves high tracking efficiency, fast transient response, and superior performance under dynamic irradiance and temperature variations. This makes the proposed controller highly suitable for grid-connected PV applications where reliability, efficiency, and power quality are critical performance requirements.
The instantaneous error between predicted and actual power is
and the discrete PI law is
where (T_{s}) is the sampling period.
In continuous form,
with gains optimized by AGO.
Each grasshopper represents a candidate parameter vector ({{varvec{uptheta}}} = left[ {K_{p} ,K_{i} ,W_{xh} ,W_{hh} ,W_{ho} } right]).
The position-update rule is
where (sleft( r right) = fe^{ – r/ell } – e^{ – r}) and
The objective (fitness) function minimized by AGO is
where the weights (w_{i}) prioritize tracking error, settling time, power ripple, and DC-link stability.
Film cells and 10.95 per cent to 11.43 percentage of its polycrystalline cells. Conversely, the efficiencies provided by 1 soltech are better with polycrystalline panel designs being in the range of 13.72 to 14.67 percent and monocrystalline panel designs falling within the range of 14.37 to 16.22 percent. Recent developments by BHEL and IIT Bombay have also geared towards efficiency enhancement by implementing crystalline silicon solar cells which have recorded minimum efficiency of 21 per cent and average of 18 per cent to 22 per cent with STCs under normal conditions. Notably, the mean PV array efficiency is never equal to the cell efficiency. In most systems, such array efficiency is normally between 10 and 16 percent⁶⁷. In this study, a polycrystalline -based 1 soltech 215-P model is used to simulate the PV grid-connected system, as that model fits within the normal accepted range of efficiency of commercial systems.
Figure 5 shows the complete block diagram of a 3-phase VSI-based grid-connected PV system (SPVGC), which consists of different modules such as a PV array, a boost converter, an inverter, MPPT, and an AC grid.
A typical SPVGC power generation block diagram.
It starts with a PV array that is connected in a sequence of parallel lines. Due to the constant non-uniform solar irradiance and temperature, the array output is an unstable DC voltage. A DC to DC boost converter stabilizes and regulates this voltage. This is done by a MPPT algorithm which continually obtains as much power as possible out of the PV modules. The output of the MPPT block is entered. into a voltage source inverter (VSI) which transforms the DC power into AC power. This AC power is then injected into the utility grid after going through LC filters, which reduce the switching harmonics generated by the inverter, and transformers, which increase or reduce the voltage to match the grid requirements. The major elements in this conversion process are the MPPT controller, and the inverter that make sure that the power produced is equal to the voltage and frequency of the grid. It should be noted that the rated capacity of a solar PV plant is usually expressed in kilowatt-peak (kWp) units, which have been defined as the maximum electrical power that the plant can produce in ideal conditions.
For this research, a 21.315 kWp PV array is designed using 100 -1 soltech-215 modules, each rated at 213.15 Wp. This array is configured by connecting ten modules in series to form a single string. To achieve the required power capacity, ten such strings are then connected in parallel.
Maximum power generation
The characteristics of soltech modules V-I and P–V are shown in Fig. 6. It was developed via the MATLAB/Simulink tool. The output current and voltages are 7.35 A and 29 V, respectively, resulting in a maximum power of 213.15 W.
(a) I‒V and (b) P‒V characteristics of a single-diode module.
Figure 7 shows the P–V curve; the MPP is a single point on a PV array’s P–V curve that is identified as unique; this point shifts according to weather conditions. This technology adjusts the output voltage of a PV power conversion system by feeding the right duty cycle (D). After that, the PWM technique converts this D value into a signal.
P–V curve for a rapid irradiance change from A (low point) to D or C (high point).
An LC filter is used to attenuate the harmonics generated by the high-frequency switching of the inverter’s insulated-gate bipolar transistors (IGBTs). A 25 kVA, 260 V/25 kV three-phase transformer is also included in the model. A proportional-integral (PI) controller is employed within the voltage control loop (VCL) and current control loop (CCL) to maintain a constant DC-link voltage and achieve a unity power factor.
In the controller, a phase-locked loop (PLL) is added to provide the inverter voltage to be accurately synchronized to the grid voltage at the point of common connection (PCC) both in phase and frequency. This model uses a three-level neutral-point clamped (NPC) inverter that is the DC-AC power converter. The inverter is built as a three-arm device comprising of four switches and antiparallel diodes on each arm and two neutrally clamped diodes. Two sets of DC capacitors divide the DC bus voltage into three levels. The midpoint voltage also is set to ensure that the voltage across each capacitor is set to be the NPC circuit as shown in Fig. 8.
NPC 3 level inverter.
The diode-clamped inverter relies on three different modes of switching in order to form a stepped staircase like waveform of output voltage. The switching states of the power devices of an inverter and the capacitor voltage levels on the DC-link will determine about these modes. Depending on the switch position of the ON/OFF switches of switches S1 and S2 as illustrated in Table 3, the switching states of the 3-level inverter can be adjusted to give a leaf voltage at three levels: 0–500 V. In addition, Table 4 provides the summary of the inverter configuration where the effective electrical properties were given to enable effective integration of the PV system.
This research proposes a novel hybrid MPPT control scheme, referred to as RES, for the DC‒DC converter. The proposed control scheme integrates a recurrent neural network (RNN) with an adaptive grasshopper optimization algorithm (AGOA), forming a unified controller. To enhance the grasshopper’s search behaviors, the algorithm is adapted with specific functions that improve its local and global search capabilities. Within this hybrid approach, the AGOA is employed to optimize the gain values of a PI controller, enabling the generation of an ideal duty cycle (D) for the DC‒DC converter. Simultaneously, the RNN generates the maximum reference power based on V panel’s input irradiation and temperature data.
A feedforward neural network (NN) is employed to predict the maximum power point (MPP) of the PV array. This prediction is based on a real-world training dataset that includes environmental inputs such as irradiation (G) and temperature (T), as well as the PV output power. As illustrated in Fig. 9a, b is the general MPPT control block diagram, the proposed RNN model uses irradiation and temperature as inputs to generate a reference power (Pref). This predicted power is then compared with the actual power of the PV array. The resulting error signal is fed to the proportional-integral (PI) controller to give a correct duty cycle (D). This duty cycle is then transformed into a control signal (s) to the DC-DC boost converter through a pulse-width modulation (PWM) generator in order to have a continuous operation at the active MPP. The mathematical expression of the output signal of the PI controller is given below in Eq. (2). The ability of the RNN to predict is thus greatly improved in different atmospheric conditions.
(a) General diagram of MPPT controller in a PV grid connected system. (b). Proposed MPPT controller in a PV grid-connected system.
The perception on the optimal size of an invisible layer can be a major challenge to the design of a robust NN model. Having too many hidden layer units may cause overfitting where the model learns the noise present in the training data thus not generalizing well to new data. A simple, linear model can be economically inexpensive although it may not be accurate predictors of the data and be under fitted. Historically, trial-and error is a lengthy method of identifying the best size of a hidden layer. In a similar way, initial weights of a recurrent neural network (RNN) are crucial in the effectiveness of training as well as predictive performance of a neural network. In order to overcome these fundamental design issues, this study presents a combined AGO-RNN method. Being a quality search algorithm, adaptive grasshopper optimization (AGO) algorithm is used not only to optimize the PI controller gains but also to achieve the optimum initial weight values of the RNN. This dual-optimization strategy ensures that the network’s training is both efficient and effective, leading to a significant reduction in the error value for the PI controller.
The main aim of solar power installation is to reduce electricity bills. For this purpose, the popular operating system is grid connected, and no batteries are used. Excess power is sold to the government or distribution company if more solar power is produced. Several methods are available for the cost analysis of a PV grid-connected system, the most common method is the LCOE. According to the MNRE in India, the initial cost of the subsidy is 30%, which includes panels and inverters. The lifespan of solar panels typically ranges from 20 to 25 years, depending on the materials used. In this work, the polycrystalline silicon material used has a lifespan of 20 years.
NC = Sum of the cost over a lifetime = Net cost of the solar system (Rs), E = total KWH/day, AP = Estimated annual production of the solar system = E (times) 365 (KWH), C = total on-grid system investment cost (Rs), S = Value of the incentives (subsidy, tax credits, depreciation, SRECs, etc.) 30% as per Central Finance Assistance, MNRE, WP = warrantied period of the solar system, SP = System Production Over the Warranty Period (KW), LCOE = Levelized cost of energy (Rs).
S is the value of the incentives, including the investment tax credit (ITC), and any subsidy or tax credit such as the GST provided by the government, which need to be reduced. O&M is based on the number of panels used in the system. It depends on the system’s rating. Low-power ratings are significantly less valuable. LP is the loan payment amount/finance charges. It depends on the customer. In this research, without loan constraints, the LCOE is calculated. The degradation rate is considered to be 1.93% of the total energy production up to the warrant period.
The payback period is calculated per the first investment cost of the PV grid-connected system and energy charges per year⁴. Energy charges are the product of energy produced per year and the unit cost per electricity board. The PB period is demonstrated in Fig. 10 below.
Understanding the payback period.
The performance of the proposed AGORNN MPPT controller is evaluated, and its effectiveness is assessed by comparing it with that of conventional and intelligent MPPT controllers.
In this section, the test case is implemented under standard test conditions, i.e., solar irradiance is considered to be 1000 W/m², and the ambient temperature is 25 °C. Performance parameters such as the PV voltage (V_pv), PV current (I_pv), PV maximum power (P_mpp), DC link voltage (V_dclink), inverter output voltage (Vio), inverter output current (Iio), grid voltage (Vg), grid current (Ig), grid power (Pg), grid current THD (IgTHD), MPPT tracking efficiency ({upeta }_{MPPT}) (%), Vdc ripple Vdcr (%), response time (Tres), overshoot Osh (%), and utilization efficiency ({upeta }_{ut}) (%) are evaluated. Finally, a cost analysis is performed via the LCOE approach.
The PV voltage (Vpv) is 287.9 V, the PV current (Ipv) is 73.93 A, and the PV maximum power (Pmpp) is 21.29 kW, as shown in Fig. 11a–c.
PV output voltage, current, and power parameters with the AGORNN MPPT controller.
The Inverter output voltage (V_io) is 212.5 V, Inverter output current (I_io) is 74.88 A, Inverter output power (P_io) is 18.83 kW, as shown in Fig. 12a–c.
Inverter output parameters under the AGORNN MPPT controller.
The grid power (P_g) is 19.31 kW, as shown in Fig. 13, With the proposed controller, the MPPT tracking efficiency ({upeta }_{MPPT}) (%) is 99.86%, the V_dc ripple V_dcr (%) is 0.07%, the response time (T_res) is 0.02 s, the overshoot O_sh (%) is − 1.87%, and the utilization efficiency ({upeta }_{ut}) (%) 99.883% is 99.883%. The total energy produced by each MPPT controller-based PV grid-connected system is calculated from the simulation results, and comparative waveforms of the output power at the DC bus and the injected power at the AC grid are illustrated in (a) and (b) of Fig. 14 under STC conditions. Figure 14c illustrates the Grid current THD under STC conditions. The cost calculation was performed via the LCOE method. The sum of the monthly mean values is considered for the LCOE calculation.
Grid power (P_g).
PV grid-connected system output power comparison with various MPPT controllers under STC: (a) PV power and (b) grid power (c) Grid current THD.
The cost analysis is based on the levelized cost of energy (LCOE) approach. The LCOE is calculated via Eqs. (3–5). The evaluated parameters are tabulated in Table 5. The total warranty period (WP) is 20 years. The degradation rate of 1.93% is considered on the basis of a survey conducted by IIT Bombay.
The performance of the proposed AGORNN MPPT controller is evaluated with a practical test case, illustrated in Fig. 15a, b and its effectiveness is assessed by comparing it with conventional and intelligent MPPT controllers. All controller’s performance is analyzed under PTC condition, the proposed controller given improved results than other controllers, performance factors are tabulated, voltage and current oscillations are reduced which leads the improved THD at grid side, tracking efficiency is improved to 96%, percentage of power overshoots reduced and response time, rise time are compared.
Practical test condition (a) Irradiance and (b) Temperature.
In the above Fig. 16, the output power of the ANFIS controller is recoded as very low, the performance of ANFIS controller is dependent on the training data of the ANFIS in the practical test data. The irradiation at 0.7 seconds is more than 1000 w/m2, it is 1178 w/m2, and it nearly reached 1200 w/m2. This number is actually recoded in BVRIT Narsapur campus from march to May month. but, the ANFIS model is trained up to 1000 w/m2, solar irradiation value is outside the training data, hence it is not able to predicted properly, this condition is consider in PTC condition, to analysis the model if the irradiations occurrence in the practical conditions more than training data, what is the performance of the controller, at this instant the proposed AGORNN controller performed well and produce optimized solution. Fig. 17a illustrates the Grid power comparison of different MPPT controllers under PTC conditions, and Fig. 17b is a magnified view for easy understanding. The Inverter output current (I_io), Voltage (Vio) are illustrated in Figs 18 and 19 respectively. The THD at the grid side is illustrated in Fig. 20, the value is recoded as 3.19%. (Tables 6 and 7).
PV power comparison of different MPPT controllers under PTC.
(a) Grid power comparison of different MPPT controllers under PTC (b) Magnifying view.
Inverter output voltage (V_io).
Inverter output current (I_io).
Grid current THD (I_gTHD) using the proposed controller in PTC case.
An LCOE comparison with different MPPT controllers is tabulated in Table 8, the overall performance of different MPPT controller-based PV grid-connected systems under STC condition is tabulated in Table 6 and the performance under PTC condition is tabulated in Tables 7.
The payback period calculated under different MPPT controllers’ operations in a 20 kW PV grid-connected system is assessed.
The Payback Period is defined as the ratio between the total system cost and the annual monetary return from energy generation:
The payback period corresponds to the smallest integer (n) for which:
For the 20 kW grid-connected PV system analyzed in this work, tabulated in Table 9.
Thus, the payback period is calculated as:
A clear and straightforward understanding of years of savings is illustrated in Fig. 21. Table 10 compares the payback periods of various MPPT methods via capital consumption analysis. While P&O, INC, IINC, FUZZY, and ANFIS all have payback periods of approximately 3.9 years, AGORNN achieves the shortest payback period of 3.77 years, demonstrating superior economic performance. The proposed Hybrid MPPT controller efficiency is compared with other hybrid MPPT methods in Table 11.
Payback period of a 20 kW PV grid-connected system with different controllers. (a) P&O-based MPPT method, (b) INC-based MPPT method, (c) IINC-based MPPT method, (d) fuzzy-based MPPT method, (e) ANFIS-based MPPT method (f) AGORNN-based MPPT method.
This work investigates a hybrid maximum power point tracking (MPPT) method and its techno, economic analyses for grid, connected photovoltaic (PV) systems. The hybrid controller, called AGORNN, is an integration of a recurrent neural network (RNN) with an adaptive grasshopper optimization (AGO) algorithm. Here, RNN predicts the PV power output by analyzing the temporal patterns of irradiance(G) and temperature (T), and the AGO algorithm is employed to find the best proportional, integration (PI) controller gains, thus prediction and control are coordinated for MPPT operation.
The new controller is tested on a 20 kW grid, connected PV system modeled in MATLAB/Simulink under both standard test conditions (STC) and practical test conditions (PTC). The performance is measured with electrical and dynamic parameters, such as PV voltage and current, power extracted, inverter and grid power, response time, overshoot, tracking efficiency, and overall conversion efficiency. The results are compared with conventional and selected intelligent MPPT methods. The AGORNN, based controller reaches a tracking efficiency of 99.86% under STC and 96% under PTC, which are followed by the total conversion efficiencies of 96.55% and 91.50%. Voltage and current fluctuations are only 0.07% and 0.05%, respectively, and overshoot is limited to 0.508%, which demonstrates good transient stability.
The irradiance and temperature were logged for one year (September 2018August 2019) at BVRIT Narsapur campus by means of calibrated sensors and a data acquisition system, thus drastically improving the practical relevance of the study. Likewise, the dataset contains intense solar irradiation conditions of above 1000 W/m², and is therefore used as the monthly mean G and T values in the simulation model towards mimicking real operating conditions.
In addition to conventional MPPT metrics, the analysis includes dynamic performance indices i.e. rise time, time in reaching the maximum power after a sudden decrease of G, and DC, link voltage behavior under both STC and PTC conditions. These factors help to understand stability of controller and its transient response. The proposed controller is further benchmarked against recent hybrid and triple-hybrid MPPT techniques reported in the literature, including ABB + GA + BA, ANN + SMC, FA + ANFIS + P&O, ANFIS, and ANFIS + PSO, demonstrating improved performance across multiple metrics.
A performance-linked techno-economic assessment is conducted using the levelized cost of energy (LCOE) and payback period as evaluation criteria. Unlike most existing studies, which consider MPPT performance and economic analysis independently, this work explicitly relates MPPT tracking efficiency and dynamic response to long-term energy yield and cost metrics. The analysis incorporates PV module degradation over the system lifetime to obtain a realistic estimation of energy production. The results indicate that improvements in MPPT performance contribute to an annual energy yield of 26,349 kWh, a reduced LCOE of ₹2.049 per unit, and a payback period of 3 years and 7 months. Overall, the work provides a comprehensive assessment of PV system performance from both technical and economic perspectives.
The research paper presents an intelligent control algorithm to use in MPPT-based applications, and the performance of the algorithm was tested under both standard test conditions (STCs) and PTC. In future studies, for rooftop PV systems, additional extension may be the estimation of unit cost and PB in PTC conditions, and the comparative analysis of the ground-mounted PV systems in order to determine the cost-performance trade-offs under PTC conditions. Investigations can also be carried out by considering converter switching losses and performance assessment can be extended by considering different PV configurations. The work will extend by practical implementation, including HIL testing, and real world non idealities, including converter parasitic components, switching effects, and measurement noise.
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
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The authors gratefully thank the Prince Faisal bin Khalid bin Sultan Research Chair in Renewable Energy Studies and Applications (PFCRE) at Northern Border University for their support and assistance.
The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through project number “NBU-FFR-2026-332-02”.
Department of Electrical & Electronics Engineering, B V Raju Institute of Technology, Narsapur, Telangana, 502313, India
P. Chandra Babu, Sainadh Singh Kshatri, Chagam Reddy Subba Rami Reddy & Golla Naresh Kumar
Department of Electrical Engineering, College of Engineering, Northern Border University, Arar, 73222, Saudi Arabia
Abdulaziz Alanazi
Electrical Engineering Department, University of Business and Technology, 23435, Jeddah, Saudi Arabia
Moustafa Ahmed Ibrahim
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P Chandra Babu contributed: conceptualization, methods, simulation models, comparative investigations, cost analysis, and writing original draft preparation. K Sainadh Singh contributed simulation models, conceptualization, reviewing, editing, and investigation. Chagam Reddy Subba Rami Reddy contributed data curation, visualization, and Investigation. Golla Naresh Kumar contributed data curation, writing, and reviewing. Abdulaziz Alanazi contributed: Writing—Review & Editing and Moustafa Ahmed Ibrahim contributed project administration supervision, resources, writing, review & editing.
Correspondence to P. Chandra Babu or Moustafa Ahmed Ibrahim.
The authors declare no competing interests.
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Babu, P.C., Kshatri, S.S., Reddy, C.R.S.R. et al. Performance comparison of MPPT controllers in a grid-connected PV system: LCOE and payback period approaches. Sci Rep 16, 9030 (2026). https://doi.org/10.1038/s41598-026-39500-9
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Received: 12 October 2025
Accepted: 05 February 2026
Published: 14 February 2026
Version of record: 16 March 2026
DOI: https://doi.org/10.1038/s41598-026-39500-9
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