Global Energy andClimate Model

Overview of model and scenarios Since 1993, the IEA has provided medium- to long-term energy projections using a continually-evolving set of detailed, world-leading modelling tools. First, the World Energy Model (WEM) – a large-scale simulation model designed to replicate how energy markets function – was developed. A decade later, the Energy Technology Perspectives (ETP) model – a technology-rich bottom-up model – was developed, for use in parallel to the WEM. In 2021, the IEA adopted for the first time a new hybrid modelling approach relying on the strengths of both models to develop the world’s first comprehensive study of how to transition to an energy system at net zero CO2 emissions by 2050. Since then, the IEA has worked to develop a new integrated modelling framework: IEA’s Global Energy and Climate (GEC) Model. As of 2022, this model is the principal tool used to generate detailed sector-by sector and region-by-region long-term scenarios across IEA’s publications.

GEC Model scenarios The IEA medium to long-term outlook publications – the World Energy Outlook (WEO) and the Energy Technology Perspectives (ETP) – use a scenario approach to examine future energy trends relying on the GEC Model. The GEC Model is used to explore various scenarios, each of which is built on a different set of underlying assumptions about how the energy system might respond to the current global energy crisis and evolve thereafter. By comparing them, the reader is able to assess what drives the various outcomes, and the opportunities and pitfalls that lie along the way. These scenarios are not predictions – GEC Model scenarios do not contain a single view about what the long-term future might hold. Instead, what the scenarios seek to do is to enable readers to compare different possible versions of the future and the levers and actions that produce them, with the aim of stimulating insights about the future of global energy.

The scenarios highlight the importance of government policies in determining the future of the global energy system: decisions made by governments are the main differentiating factor explaining the variations in outcomes across our scenarios. However, we also take into account other elements and influences, notably the economic and demographic context, technology costs and learning, energy prices and affordability, corporate sustainability commitments, and social and behavioural factors. However, while the evolving costs of known technologies are modelled in detail, we do not try and anticipate technology breakthroughs (e.g., nuclear fusion).

GEC Model overview Modelling methodology The GEC Model is a bottom-up partial-optimisation model covering energy demand, energy transformation and energy supply (Figure 1.1). The model uses a partial equilibrium approach, integrating price sensitivities. It shows the transformation of primary energy along energy supply chains to meet energy service demand, the final energy consumed by the end-user. The various supply, transformation and demand modules of the model are dynamically soft-linked: consumption of electricity, hydrogen and hydrogen-related fuels, biofuels, oil products, coal and natural gas in the end use sector model drives the transformation and supply modules, which in turn feed energy prices back to the demand module in an iterative process. In addition, energy system CO2, CH4 and N2O emissions are assessed. The model contains a number of additional analysis features evaluating further system implications such as investments, critical minerals, employment, temperature outcomes, land use, and air pollution (see more details below).

Material trade between model regions is not modelled endogenously in the technology model, but rather is reflected in the activity projections developed in the activity and stock models. Apart from specific instances where announced policies or projected energy price signals provide relevant evidence to the contrary, trade patterns in material production and consumption are projected to follow current trends. Global total material demand is thus allocated into regional production based on these current trends. The capacity model contains data on historic and planned plant capacity additions and retrofits by plant type. Using assumptions about investment cycles, it calculates plant refurbishments and retirements. The resulting remaining capacity informs the main technology model. The capacity model also provides projections on the average age of plants at a given time.

The main technology model of each sector consists of a detailed representation of process technologies required for relevant production routes. Energy use and technology portfolios for each country or region are characterised in the base year using relevant energy use and material production statistics. Throughout the modelling horizon, demand for materials (as dictated by the activity model outputs) is met by technologies and fuels, whose shares are informed by real-world technology progress and the previous ETP TIMES optimisation model. That model used a constrained optimisation framework, with the objective function set to make choices that minimise overall system cost (comprised of both energy costs and investments).

The transport module The transport module of the GEC Model consists of several sub-models covering road, aviation, rail and navigation transport modes (Figure 3.4). The GEC Model fully incorporates a detailed bottom-up approach for the transport sector in all GEC Model regions.

For each region, activity levels such as passenger-kilometres and tonne-kilometres are estimated econometrically for each mode of transport as a function of population, GDP and end-user price. Transport activity is linked to price through elasticity of fuel cost per kilometre, which is estimated for all modes except passenger buses and trains and inland navigation. This elasticity variable accounts for the “rebound” effect of increased car use that follows improved fuel efficiency. Energy intensity is projected by transport mode, taking into account changes in energy efficiency and fuel prices.

Hourly load curves for end-uses are informed by research and survey data where available. Detail on modelling of hourly heating, cooling and lighting electricity demand across the year is included, with deep learning algorithms used to predict space heating and cooling demand for both residential and services buildings based on temperature, building occupancy rates and historical demand. Lighting hourly electricity demand is projected based on building activity and occupation rates, daylight times and insolation levels. The aggregate electricity demand of each end-use or subsector is then matched to the total historical hourly load profile of a given country. 7 An example of the load aggregation is displayed in Figure 3.11.

The model subtracts from the demand in each segment any generation coming from plants that must run – such as some CHP plants and desalination plants – and also generation from renewables. For generation from variable renewables, the amount of generation in each demand segment is estimated based on the historical correlation between generation and demand. The remainder of the demand in each segment must be met by production from dispatchable plants. The model determines the mix of dispatchable generation by constructing a merit order of the plants installed – the cumulative installed generation capacity arranged in order of their variable generation costs – and finding the point in the merit order that corresponds to the level of demand in each segment (Figure 4.3). As a result, plants with low variable generation costs – such as nuclear and lignite-burning plants in the Figure 4.3 example – will tend to operate for a high number of hours each year because even baseload demand is higher than their position in the merit order. On the other hand, some plants with high variable costs, such as oil-fired plants, will operate only during the peak demand segment.

Calculation of the capacity credit and capacity factor of variable renewables Power generation from weather-dependent renewables such as wind and solar power varies over time and the characteristics of the power supply from variable renewables have to be taken into account for the decisions on dispatch and capacity additions of the remaining, mostly dispatchable power plants. The effect of all variable renewables (solar PV, solar CSP without storage and wind on- and offshore) is taken into account via the capacity credit and the capacity factor in each load segment.

Value-adjusted Levelized Cost of Electricity Major contributors to the Levelized Cost of Electricity (LCOE) include overnight capital costs; capacity factor that describes the average output over the year relative to the maximum rated capacity (typical values provided); the cost of fuel inputs; plus operation and maintenance. Economic lifetime assumptions are 25 years for solar PV, onshore and offshore wind. For all technologies, a standard weighted average cost of capital was assumed (7-8% based on the stage of economic development, in real terms). The value-adjusted LCOE (VALCOE) is a metric for competitiveness for power generation technologies, building on the capabilities of the GEC Model hourly power supply model. It is intended to complement the LCOE, which only captures relevant information on costs and does not reflect the differing value propositions of technologies. While LCOE has the advantage of compressing all the direct technology costs into a single metric which is easy to understand, it nevertheless has significant shortcomings: it lacks representation of value or indirect costs to the system and it is particularly poor for comparing technologies that operate differently (e.g. variable renewables and dispatchable technologies). VALCOE enables comparisons that take account of both cost and value to be made between variable renewables and dispatchable thermal technologies.

The merchant hydrogen supply module uses a cost-optimisation modelling framework called TIMES, a
technology-rich modelling platform developed and further improved by the ETSAP Technology Collaboration Programme of the IEA. The hydrogen module depicts various technology options to produce hydrogen and hydrogen derived fuels (ammonia, synthetic liquid hydrocarbon fuels, synthetic methane) in terms of existing capacities, conversion efficiencies, fuel costs, operating and maintenance costs, CO2 emissions as well as CO2 capture rates for fossil fuel based technologies and capital costs for new capacity additions. Electrolyser capital costs represent a weighted average of likely deployment shares of different electrolyser technologies, which future cost reductions being derived by component-wise learning curves. Capital costs for all technologies also include all balance-of-plant and engineering, procurement and construction (EPC) costs, which can represent a high share of total installed costs.

Based on demands for merchant hydrogen and hydrogen-derived fuels from the end-use sectors, electricity and heat generation sector, refineries and biofuel production, the hydrogen supply module determines a least-cost technology mix to cover these demands. Besides these demands and the technical and economic characteristics of technologies, the module takes into account announced hydrogen production or trade projects (using for example the IEA’s Hydrogen Project Database) as well as policy constraints, such as CO2 prices or hydrogen deployment targets.


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