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Energy modeling
Process of building computer models of energy systems in order to analyze them From Wikipedia, the free encyclopedia
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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.[1] 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.[2] 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.[2]
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Model types
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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 behaviour
- energy and non-energy capital investment and labour market adjustment dynamics leading to economic restructuring
- infrastructure deployment and urban planning"[3]: S28–S29 : point form added
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 optimisation, 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. Open-source energy models are increasingly used to enhance transparency and collaboration, allowing diverse stakeholders to contribute to and validate model development. 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.[4][5] 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.[6]
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.[7][8] 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,[9] MESSAGE, and REMIND.[10]
Published surveys on energy system modeling have focused on techniques,[11] general classification,[12] an overview,[13] decentralized planning,[14] modeling methods,[15] renewables integration,[6][16] energy efficiency policies,[17][18] electric vehicle integration,[19] international development,[20] and the use of layered models to support climate protection policy.[21] Deep Decarbonization Pathways Project researchers have also analyzed model typologies.[3]: S30–S31 A 2014 paper outlines the modeling challenges ahead as energy systems become more complex and human and social factors become increasingly relevant.[22]
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.[23][24][25]
Energy system models
In addition to the electricity sector, energy system models include the heat, gas, mobility, and other sectors as appropriate.[26] These models often integrate multiple sectors to analyze cross-sectoral interactions, such as the coupling of electricity and heating systems for decarbonization. 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.[27]
Producing hybrid top-down/bottom-up models to capture both the economics and the engineering has proved challenging.[28]
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Established Models
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This section lists some of the major models in use.[1] These are typically run by national governments, private companies, or are open-source programs. In a community effort, a large number of existing energy system model databases were collected in model fact sheets on the Open Energy Platform.[29]
Integrated Planning Model (IPM)
The Integrated Planning Model is a long-term optimization model for power market forecasting, capacity expansion, and analysis by environmental regulators. The model provides a comprehensive tool to study power markets and environmental policies under regulator constraints such as carbon dioxide caps and emissions trading programs. The model is practical for energy analysis within the United States. The model projects wholesale energy prices over multi-decade horizons and emphasizes long-term planning reserves. [30]
PROMOD
PROMOD is an energy modeling service developed by Hitachi Energy for the model generation of the transmission power market. It is a short term product cost model that performs hourly market simulations, congestion analysis, and financial transmission rights valuations. The model focuses on modeling market dynamics and locational marginal prices to inform decision-making in deregulated electricity markets. The model incorporates environmental regulations via emissions costs and carbon prices, but does not optimize compliance strategies. [31]
TEMOA
TEMOA stands for the Tools for Energy Model Optimization and Analysis. The TEMOA power model is an open source long-term energy system optimization model. This model is designed for long term planning and is used to explore decarbonization pathways, carbon pricing, renewable energy targets, and technology cost reductions. It also has the capability to analyze future energy systems under different public policy scenarios. The model can be used to explore decarbonization policy pathways since it includes carbon prices, emission caps, and renewable energy mandates. [39]
Haiku
Haiku is an energy modeling software owned by Resources of the Future (RFF). It is a regional electricity market simulation model that accounts for capacity planning, investment, and retirements. The model focuses on short term planning and accounts for system operations across seasons and times of day. The model represents fuel markets and interregional electricity trade within the United States. [33]
Plexos
The Plexos model by Energy Exemplar is a medium term capacity expansion and analysis model. It focuses on the eastern United States and provides nodal analysis to identify operation reserves, and reserve margins. The model includes environmental regulation costs by including plant level sulfur dioxide and nitrogen monoxide pricing. [34]
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. [35]
PyPSA
PyPSA is an open source modeling system that focuses on electricity networks with detailed power flow modeling and optional multi-sector coupling, such as heat and industry. The model optimizes modern energy systems including generation, storage, sector coupling, and transmission to analyze renewable integration. . [36]
NEMS
NEMS (National Energy Modeling System) is a long-standing United States government policy model, run by the Department of Energy (DOE). NEMS computes equilibrium fuel prices and quantities for the US energy sector. To do so, the software iteratively solves a sequence of linear programs and nonlinear equations.[55] NEMS has been used to explicitly model the demand-side, in particular to determine consumer technology choices in the residential and commercial building sectors.[56] The model includes an emissions policy module that allows it to analyze regulator compliance, calculate carbon dioxide emissions, and identify policy impacts. [37]
NEMS is used to produce the Annual Energy Outlook each year – for instance in 2015.[57]
REPEAT
REPEAT stands for Rapid Energy Policy Evaluation and Analysis Toolkit and is a policy assessment energy model. The model has a broad framework that allows for economy wide coverage and a RIO component for power sector capacity expansion. The model evaluates environmental and economic impacts on federal energy and climate policies. It allows for spatial emissions modeling and renewable energy generation integration. [38]
Energy Policy Simulator (EPS)
The Energy Policy Simulator is an economy-wide policy impact model that analyzes how energy and environmental policies influence emissions, energy use, cash flows, costs, and health. It provides a transparent, educational tool for comparing policy scenarios and supporting informed stakeholder decisions. The model simulates climate and energy policies—CO₂ pricing, emission caps, greenhouse gas volume, pollutant emissions, and related environmental and health metrics.[39]
MARKAL/TIMES
MARKAL (MARKet ALlocation) is an integrated energy systems modeling platform, used to analyze energy, economic, and environmental issues at the global, national, and municipal level over time-frames of up to several decades. MARKAL can be used to quantify the impacts of policy options on technology development and natural resource depletion. The software was developed by the Energy Technology Systems Analysis Programme (ETSAP) of the International Energy Agency (IEA) over a period of almost two decades.
TIMES (The Integrated MARKAL-EFOM System) is an evolution of MARKAL – both energy models have many similarities.[58] TIMES succeeded MARKAL in 2008.[59] Both models are technology explicit, dynamic partial equilibrium models of energy markets. In both cases, the equilibrium is determined by maximizing the total consumer and producer surplus via linear programming. Both MARKAL and TIMES are written in GAMS.
The TIMES model generator was also developed under the Energy Technology Systems Analysis Program (ETSAP). TIMES combines two different, but complementary, systematic approaches to modeling energy – a technical engineering approach and an economic approach. TIMES is a technology rich, bottom-up model generator, which uses linear programming to produce a least-cost energy system, optimized according to a number of user-specified constraints, over the medium to long-term. It is used for "the exploration of possible energy futures based on contrasted scenarios".[60]: 7
As of 2015[update], the MARKAL and TIMES model generators are in use in 177 institutions spread over 70 countries.[61]: 5
Switch
Switch is an open source platform that focuses on long-term electric power planning. The model co-optimizes generation, storage, and transmission to meet electricity demand at lowest cost. It is used to plan least-cost power system development while achieving specified policy goals.[42]
GenX
GenX is an open source modeling software developed by researchers at MIT and Princeton that supports modeling of the electricity market and includes a decarbonization model that represents least-cost electricity generation with flexibility constraints. The model supports research and decision-making by exploring how policies, technologies, and market structures shape cost-effective, reliable pathways for power sector decarbonization.[43]
Hitachi Gridview
The Hitachi Gridview model (formerly ABB Gridview) simulates operation of competitive electricity markets by enforcing engineering constraints in chronological sequence over time spans from a day to several years. The model helps energy producers identify optimal operating strategies and make informed short-term financial decisions that minimize system-wide costs. This model does not include any explicit environmental compliance or dedicated environmental modeling features. [44]
EnCompass (Yes Energy)
Encompass is a comprehensive and integrated power forecasting and simulation software that helps utilities and market participants manage risk, decarbonization, and reliability. The model optimizes power supply decisions from short-term scheduling to long-term planning using multi-scenario forecasts and nodal market simulation capabilities. This model supports cost analysis and includes some environmental considerations. EnCompass has explicit modeling capabilities for emission compliance based on current policies. [45]
Synapse
The Synapse model is designed to perform long term forecasting of regulatory compliance costs. The model utilizes existing information provided by utilities and project developers to identify future costs and plan operational reserve requirements. The model does not include any environmental optimization analysis, but does model decarbonization and emissions pathways. [46]
Power Systems Optimizer (Polaris)
The Polaris model is an industrial-grade engine for power system optimization that addresses unit commitment, economic dispatch, resource adequacy, and co-optimized gas–electric operations. This model includes production cost market simulator that optimizes energy flows, commitment, and resource deployment and simulates energy storage based on efficiency. Polaris also improves environmental outcomes via more efficient, low-carbon dispatch but lacks explicit carbon-pricing or emissions-cap modeling. [47]
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.[48]
US-REGEN
The US-REGEN model was developed by the Electric Power Research Institute. It is an open-source program that links detailed electric sector capacity planning and fuels supply system with a detailed consumer choice model of end-use services and energy demands. This model serves as EPRI’s U.S. Regional Economy, Greenhouse Gas, and Energy model for energy modeling and technology analysis.[49]
OSeMOSYS
The Open Source Energy Modeling System (OSeMOSYS) is a long-run integrated assessment and energy planning model designed to be simple and transparent. The model supports long-term energy system decarbonization. OSeMOSYS is a free, open-source energy system model for national planning with low computational requirements and a relatively small learning curve.[50]
Resource Planning Model (RPM)
The Resource Planning Model (RPM) is owned by the National Renewable Energy Laboratory. This model focuses on capacity expansion and optimizes generation, transmission, and energy storage investments to meet long-term energy policy goals. The model imposes policy constraints such as renewable portfolio standards and emission requirements to assess compliance and environmental impacts.[51]
LEAP
LEAP, the Low Emissions Analysis Platform (formerly known as the Long-range Energy Alternatives Planning System) is a software tool for energy policy analysis, air pollution abatement planning and climate change mitigation assessment.[62][63]
LEAP was developed at the Stockholm Environment Institute's (SEI) US Center. LEAP can be used to examine city, statewide, national, and regional energy systems. LEAP is normally used for studies of between 20–50 years. Most of its calculations occur at yearly intervals. LEAP allows policy analysts to create and evaluate alternative scenarios and to compare their energy requirements, social costs and benefits, and environmental impacts. As of June 2021, LEAP has over 6000 users in 200 countries and territories
Power system simulation
General Electric's MAPS (Multi-Area Production Simulation) is a production simulation model used by various Regional Transmission Organizations and Independent System Operators in the United States to plan for the economic impact of proposed electric transmission and generation facilities in FERC-regulated electric wholesale markets. Portions of the model may also be used for the commitment and dispatch phase (updated on 5 minute intervals) in operation of wholesale electric markets for RTO and ISO regions. ABB's PROMOD is a similar software package. These ISO and RTO regions also utilize a GE software package called MARS (Multi-Area Reliability Simulation) to ensure the power system meets reliability criteria (a loss of load expectation (LOLE) of no greater than 0.1 days per year). Further, a GE software package called PSLF (Positive Sequence Load Flow) and a Siemens software package called PSSE (Power System Simulation for Engineering) analyzes load flow on the power system for short-circuits and stability during preliminary planning studies by RTOs and ISOs.[64][65][66][67][68][69][70][71] Since 2020, newer tools like PyPSA (Python for Power System Analysis) and PowerFactory have gained traction for simulating high-renewable grids, incorporating real-time data and stochastic methods for variable renewable energy integration.
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Criticisms
Public policy energy models have been criticized for being insufficiently transparent. The source code and data sets should at least be available for peer review, if not explicitly published.[72] To improve transparency and public acceptance, some models are undertaken as open-source software projects, often developing a diverse community as they proceed. OSeMOSYS is an example of such a model.[73][50] The Open Energy Outlook is an open community that has produced a long-term outlook of the U.S. energy system using the open-source TEMOA model.[74][75][76][77]
Not a criticism per se, but it is necessary to understand that model results do not constitute future predictions.[78]
See also
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General
- Climate change mitigation – actions to limit long-term climate change
- Climate change mitigation scenarios – possible futures in which global warming is reduced by deliberate actions
- Economic model
- Energy system – the interpretation of the energy sector in system terms
- Energy Modeling Forum – a Stanford University-based modeling forum
- Open Energy Modelling Initiative – an open source energy modeling initiative, centered on Europe
- Open energy system databases – database projects which collect, clean, and republish energy-related datasets
- Open energy system models – a review of energy system models that are also open source
- Power system simulation
Models
- iNEMS (Integrated National Energy Modeling System) – a national energy model for China
- MARKAL – an energy model
- NEMS – the US government national energy model
- POLES (Prospective Outlook on Long-term Energy Systems) – an energy sector world simulation model
- KAPSARC Energy Model - an energy sector model for Saudi Arabia [79]
- Integrated Planning Model[30]
- PROMOD[31]
- TEMOA (Tools for Energy Model Optimization and Analysis)[32]
- Energy Policy Simulator (EPS)[39]
- TIMES (The Integrated MARKAL-EFOM System)[41]
- Switch[42]
- GenX[43]
- Hitachi Gridview[44]
- EnCompass (Yes Energy)[45]
- Synapse[46]
- Power System Optimizer (Polaris)[47]
- Dayzer[48]
- US-REGEN[49]
- OSeMOSYS[80]
- Resource Planning Model - RPM[51]
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Further reading
References
External links
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