Energy modeling or energy system modeling is the process of building computer models of energy systems in order to analyze them. Such models often employ scenario analysis to investigate different assumptions about the technical and economic conditions at play. Outputs may include the system feasibility, greenhouse gas emissions, cumulative financial costs, natural resource use, and energy efficiency of the system under investigation. A wide range of techniques are employed, ranging from broadly economic to broadly engineering. Mathematical optimization is often used to determine the least-cost in some sense. Models can be international, regional, national, municipal, or stand-alone in scope. Governments maintain national energy models for energy policy development.
Energy models are usually intended to contribute variously to system operations, engineering design, or energy policy development. This page concentrates on policy models. Individual building energy simulations are explicitly excluded, although they too are sometimes called energy models. IPCC-style integrated assessment models, which also contain a representation of the world energy system and are used to examine global transformation pathways through to 2050 or 2100 are not considered here in detail.
Energy modeling has increased in importance as the need for climate change mitigation has grown in importance. The energy supply sector is the largest contributor to global greenhouse gas emissions. The IPCC reports that climate change mitigation will require a fundamental transformation of the energy supply system, including the substitution of unabated (not captured by CCS) fossil fuel conversion technologies by low-GHG alternatives.
Model types A wide variety of model types are in use. This section attempts to categorize the key types and their usage. The divisions provided are not hard and fast and mixed-paradigm models exist. In addition, the results from more general models can be used to inform the specification of more detailed models, and vice versa, thereby creating a hierarchy of models. Models may, in general, need to capture "complex dynamics such as: energy system operation technology stock turnover technology innovation firm and household behavior energy and non-energy capital investment and labor market adjustment dynamics leading to economic restructuring infrastructure deployment and urban planning"
Models may be limited in scope to the electricity sector or they may attempt to cover an energy system in its entirety (see below).
Most energy models are used for scenario analysis. A scenario is a coherent set of assumptions about a possible system. New scenarios are tested against a baseline scenario – normally business-as-usual (BAU) – and the differences in outcome noted.
The time horizon of the model is an important consideration. Single-year models – set in either the present or the future (say 2050) – assume a non-evolving capital structure and focus instead on the operational dynamics of the system. Single-year models normally embed considerable temporal (typically hourly resolution) and technical detail (such as individual generation plant and transmissions lines). Long-range models – cast over one or more decades (from the present until say 2050) – attempt to encapsulate the structural evolution of the system and are used to investigate capacity expansion and energy system transition issues.
Models often use mathematical optimization to solve for redundancy in the specification of the system. Some of the techniques used derive from operations research. Most rely on linear programming (including mixed-integer programming), although some use nonlinear programming. Solvers may use classical or genetic optimization, such as CMA-ES. Models may be recursive-dynamic, solving sequentially for each time interval, and thus evolving through time. Or they may be framed as a single forward-looking intertemporal problem, and thereby assume perfect foresight. Single-year engineering-based models usually attempt to minimize the short-run financial cost, while single-year market-based models use optimization to determine market clearing. Long-range models, usually spanning decades, attempt to minimize both the short and long-run costs as a single intertemporal problem.
The demand-side (or end-user domain) has historically received relatively scant attention, often modeled by just a simple demand curve. End-user energy demand curves, in the short-run at least, are normally found to be highly inelastic.
As intermittent energy sources and energy demand management grow in importance, models have needed to adopt an hourly temporal resolution in order to better capture their real-time dynamics. Long-range models are often limited to calculations at yearly intervals, based on typical day profiles, and are hence less suited to systems with significant variable renewable energy. Day-ahead dispatching optimization is used to aid in the planning of systems with a significant portion of intermittent energy production in which uncertainty around future energy predictions is accounted for using stochastic optimization.
Implementing languages include GAMS, MathProg, MATLAB, Mathematica, Python, Pyomo, R, Fortran, Java, C, C++, and Vensim. Occasionally spreadsheets are used.
As noted, IPCC-style integrated models (also known as integrated assessment models or IAM) are not considered here in any detail. Integrated models combine simplified sub-models of the world economy, agriculture and land-use, and the global climate system in addition to the world energy system. Examples include GCAM, MESSAGE, and REMIND.
Published surveys on energy system modeling have focused on techniques, general classification, an overview, decentralized planning, modeling methods, renewables integration, energy efficiency policies, electric vehicle integration, international development, and the use of layered models to support climate protection policy. Deep Decarbonization Pathways Project researchers have also analyzed model typologies. A 2014 paper outlines the modeling challenges ahead as energy systems become more complex and human and social factors become increasingly relevant.
Electricity sector models Electricity sector models are used to model electricity systems. The scope may be national or regional, depending on circumstances. For instance, given the presence of national interconnectors, the western European electricity system may be modeled in its entirety.
Engineering-based models usually contain a good characterization of the technologies involved, including the high-voltage AC transmission grid where appropriate. Some models (for instance, models for Germany) may assume a single common bus or "copper plate" where the grid is strong. The demand-side in electricity sector models is typically represented by a fixed load profile.
Market-based models, in addition, represent the prevailing electricity market, which may include nodal pricing.
Game theory and agent-based models are used to capture and study strategic behavior within electricity markets.
Energy system models In addition to the electricity sector, energy system models include the heat, gas, mobility, and other sectors as appropriate. Energy system models are often national in scope, but may be municipal or international.
So-called top-down models are broadly economic in nature and based on either partial equilibrium or general equilibrium. General equilibrium models represent a specialized activity and require dedicated algorithms. Partial equilibrium models are more common.
So-called bottom-up models capture the engineering well and often rely on techniques from operations research. Individual plants are characterized by their efficiency curves (also known as input/output relations), nameplate capacities, investment costs (capex), and operating costs (opex). Some models allow for these parameters to depend on external conditions, such as ambient temperature.
Producing hybrid top-down/bottom-up models to capture both the economics and the engineering has proved challenging.
Energy models This section briefly describes some of the major models in use, in a summary table followed by prose descriptions. These models are typically licensed by private companies or are open-source. Additional information about the models can be found in the much more detailed wiki editable table at this link: Electric Sector Models.
Model Model typeProduction Cost modeling projects system operation. Capacity Expansion modeling typically does that, and it also projects what generation, storage, and/or transmission capacity will be built and retired. Geographic region Availability Sectors included Special features Reference Aurora Production Cost / Capacity Expansion US Proprietary Power Dayzer Production Cost US and Canada (Regional) Commercial Power E4ST (Engineering, Economic, and Environmental Electricity Simulation Tool) Capacity Expansion / Production Cost US, Canada, northern Baja Open Source Power (detailed), gas (zonal), air quality (detailed) Detailed grid, physics-based flows, geographic detail, integrated air quality modeling, comprehensive benefit-cost analysis, user-modifiable EnCompass (Yes Energy) Production Cost / Capacity Expansion US Commercial Power Energy Policy Simulator Capacity Expansion Global Open to Public Power GenX Production Cost US Open Source Power GridPath Production Cost US Open Source Power Haiku Capacity Expansion / Production Cost US (regional) Commercial Power Emphasis on policy analysis Hitachi Gridview Production Cost US Commercial Power Integrated Planning Model Capacity Expansion US Proprietary Power Used by USEPA Multi-Area Production Simulation (MAPS) Capacity Expansion US Proprietary Power Markal Production Cost US Open Source Power NEMS Production Cost US Government Power Used by USDOE OSeMOSYS Capacity Expansion Global Open Source Power PlanOS Production Cost Global Commercial Plexos Production Cost US (regional) Proprietary Power Power System Optimizer (Polaris) Production Cost US Commercial Power PROMOD Production Cost US Proprietary Power PyPSA Capacity Expansion US and Europe Open Source Power REPEAT Capacity Expansion US Proprietary Power Resource Planning Model - RPM Capacity Expansion US Open Source Power Switch Production Cost US Open Source Power TEMOA Capacity Expansion Global Open Source Power TIMES Capacity Expansion US Open to Public Power US-REGEN Capacity Expansion US Open Source Power Wis:DOM Capacity Expansion US Commercial Power
Aurora Aurora is a model developed by Energy Exemplar to analyze long-term capacity expansion and evaluate the impact of battery storage on overall system performance. The model has several features for analyzing power market risks, price forecasting, and fuel price trending. The model includes some environmental factors by including carbon pricing and emission controls costs.
Dayzer Dayzer focuses on short term modeling. This includes a day-ahead locational market clearing price analyzer developed by Cambridge Energy Solutions. This model is designed to assists electricity market participants in understanding and managing transmission congestion costs in competitive electricity markets.
E4ST