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1 | Comparison Table of Bulk Energy Simulation Models | |||||||||||||||||||||
2 | Model Name | Scope | Purpose of the Model | Geographic Applicability | Key Users | Availability | Endogenous Generation Capacity Expansion | Endogenous Storage Capacity | Transmission Line Consideration | Endogenous Transmission Capacity Expansion | Market Price Projection | Forecasting Horizon | Representation of Reserve Requirements | Model type | Model Formulation | Uncertainty Representation | Compliance | Environmental Features | References | |||
3 | Aurora (by Energy Exemplar) | The power model offers comprehensive features across several domains, including operational analysis of emissions, limits, prices, and reserves; capacity expansion through long-term planning, renewable integration, demand forecasting, and hydro constraints; transmission analysis at nodal and zonal levels; policy analysis focusing on energy portfolios; and market analysis covering power market risks, price forecasting, and fuel price trends. | The model supports optimizing long-term capacity expansion by determining the most efficient generation mix. It enables electricity price forecasting at sub-hourly, hourly, daily, and annual levels. Additionally, it facilitates power market risk analysis through the simulation of various policy and fuel price scenarios. The model also supports renewable energy integration and evaluates the impact of battery storage on system performance. | North America (including WECC, EIC, and ERCOT). | Energy executives, developers, and resource planners; Northwest Power and Conservation Council. | Obtained through license, Proprietary of Energy Exemplar. | Yes, AURORA power sector model simulates endogenous generation capacity expansion through its Long-Term Capacity Expansion (LTCE) model. This model dynamically forecasts resource additions and retirements over a planning horizon based on economic conditions, demand forecasts, and system constraints. | Not explicitly mentioned however, the model treats storage technology as investable assets in long-term generation and transmission capacity planning. | Aurora’s transmission analysis includes both nodal and zonal modeling. In nodal mode, it uses DC optimal power flow (DC-OPF) to represent detailed grid components and enforces Kirchhoff’s First Law, while approximating the Second Law. In zonal mode, it uses a simplified pipe flow model that allows power flows between zones based on transmission limits, without strict adherence to Kirchhoff’s laws. Both approaches aim to approximate physical laws while ensuring line limits and economic dispatch are respected. | The AURORA power sector model does not explicitly include endogenous transmission capacity expansion. While the model supports long-term capacity expansion for generation resources, its transmission modeling focuses on evaluating transmission constraints, zonal constraints, and interregional power flows rather than dynamically optimizing new transmission investments within the model itself. AURORA considers transmission costs and limitations as inputs and uses transmission inflation rates and constraints to account for grid limitations and inte, rregional power exchange, which allows users to analyze the effect of different transmission scenario, but it does not simulate the automatic expansion of transmission capacity based on economic optimization as it does for generation expansion. | Power Market Risk Analysis, Power Market Price Forecasting, Fuel Price Forecasts | Not explicitly mentioned | Aurora model is able to represent short term reserve requirement by considering both spinning and operating reserves margin and its requirement. The model represents long-term reserve requirements by specifying a planning reserve margin that must be met in every year of the study. It will then decide what new capacity to build and where, to both serve peak load and meet reserve targets. | Capacity Model | AURORA uses Mixed Integer Programming (MIP) for solving complex capacity expansion problems, ensuring a realistic and computationally efficient approach to generation investment decisions | Using stochastic modeling techniques and Monte Carlo Simulations approach. Specifically, AURORA generates electric price distributions based on varying gas prices, hydro, and wind generation. It incorporates these stochastic variables into the hourly dispatch model and simulates hundreds of future potentials through stochastic iterations to assess risk and cost implications of different scenarios | Able to evaluate scenario of carbon pricing, emission regulations, environmental compliance. Able to assess the impact of RE sources and storage (compliance to the Renewable Energy Policies) LTCE ability to analyze policy measures, market rules, and market fundamental dynamics support the regulatory compliance | Provides capabilities for modeling environmental compliance includeing carbon pricing and emissions regulations. Enables the incorporation of environmental policies into energy portfolio analysis and resource expansion planning. | https://www.energyexemplar.com/aurora | |||
4 | E4ST (RFF is owner) | The model encompasses generation optimization, investment and retirement scenario analysis, simulation of responses to regulatory changes and market incentives, system reliability assessments, and environmental impact evaluations. Its scope includes four key system analysis dimensions: temporal resolution, spatial resolution, representation of end use, and representation of uncertainty, ensuring a comprehensive approach to energy system modeling. | To evaluate how the power sector will respond to changes in environmental and non-environmental policies and regulations, input costs, transmission investments, generation investments, and other factors. It determines operation, entry and exit of generators, prices including capacity/scarcity prices, all of the elements of social surplus (a measure of total net benefits), and other outcomes. | Three Major Grids in the U.S. and Canada. | Policymakers and regulators, utility companies and grid operators, researchers and analysts, as well as international and academic institutions all play vital roles in the development, oversight, and advancement of the power grid. | An older version is available as open-source software, although using the model may require technical expertise. The latest version is available under a licensing agreement. | Yes, The E4ST model includes endogenous generation capacity expansion by optimizing generator entry and retirement based on economic and policy conditions. It determines generator additions and retirements by comparing expected revenues with investment and operational costs. New generation capacity is built if projected revenues exceed investment costs over a defined horizon, while existing generators retire if their operational costs exceed expected revenues. | Yes, E4ST is capable of modeling storage operation. Explicit simulation of storage expansion is not mention but the model can be customized to represent storage operation. | E4ST incorporates a DC load flow model, which approximates power flows in the transmission network using a linear representation of Kirchhoff’s Laws, ensuring accurate simulation of electrical flows and network constraints within the transmission system. | Yes, E4ST includes endogenous transmission capacity expansion in a way that it models individual transmission line and how transmission investment responds to economic and policy conditions. - It covers 20,000 transmission line segments, including ALL high voltage (>200kV lines), selected lower voltage lines in congested areas, and equivalent lines to represent removed low-voltage lines. - The model determines where new transmission investments should occur by evaluating the benefits and costs of expansion in response to economic signals. The transmission expansion decisions are based on congestion costs, renewable energy integration, and regulatory constraints - It ensures that power transfers remain feasible within transmission constraints, allowing for dynamic adjustments based on changing grid conditions and policies | Locational Marginal Price, Wholesale electricity prices based on generator dispatch and costs , Price Responsiveness to demand model , End User electricity prices | up to 2050 | E4ST model provide insights on short term reserve requirement simulation by considering reliability standards and system contingencies, assessing the adequacy of reserves in short-term operations. For the long term reserve, the LTCE feature are able to simulate the level of reserve margin that must be maintain to handle future demand growth and unexpected outages. | Capacity Model | The E4ST model formulation is linear. It is structured as a large linear program that assumes perfect competition, optimizing generation, transmission, and investment decisions while not overlooking transmission constraints | By incorporating multiple fuel price scenarios and comparing their probability-weighted averages. Multiple fuel price scenarios means that the model considers three fuel price trajectories (high, medium, and low) and assesses their impact on electricity generation, fuel switching, and investment decisions. By Probability weighted average, E4ST compares outcomes using the weighted average of these fuel price scenarios versus the outcomes from a single fixed-price scenario. This method helps estimate the expected impact of fuel price volatility on system costs and generation shares | Represent environmental regulations including carbon pricing, emission limit, and emission impact of the power sector. The E4ST model also evaluates the benefits and costs of potential transmission projects by considering factors like congestion, reliability, and economic efficiency, aligning with regulatory requirements. | Models environmental policies, including carbon pricing and emissions limits, by simulating the economic and engineering aspects of the power system. It evaluates the benefits and costs of potential transmission projects, considering factors like congestion, reliability, and economic efficiency. | E4ST Description – E4ST | |||
5 | EnCompass (Yes Energy) | From Yes Energy: EnCompass is a software solution that helps you produce market price forecasts, analyze generation and transmission development, and make informed decisions in the transitioning power grid. EnCompass comprehensively evaluates new technologies for decarbonization and sustainability while maintaining reliability. "EnCompass is a comprehensive, integrated power forecasting software that features zonal capacity expansion looking 30+ years into the future, plus nodal market simulation capabilitie." | Optimize power supply decisions from short-term scheduling and trading to long-term capital investment. This is the only power market simulation software you need for all facets of power planning and forecasting. It combines power plants and complex contracts’ full operational detail with the ability to simplify and relax constraints for long-term simulations. | USA, intra-grid | Energy producers and investors "Utilities, co-ops, developers, traders, ISOs, regulators, and consultants" | Obtained through a license from Yes Energy (with free demo). | Yes; endogenous generation capacity expansion is the key feature with user-definable constraints, technology types, and policy overlays. | Yes; models multiple storage types with investment decisions and hourly dispatch. | Yes; allows zonal or nodal modeling with transmission limits and losses. Relies on user-defined inputs (capacity factor). | Yes; supports endogenous expansion of transmission corridors, with cost and constraint inputs. Relies on the same use inputs. | Benefit Cost Analysis for long-term investments | Up to 30 Years | Both | Reliability Model | EnCompass is a comprehensive power system planning model that uses mixed-integer linear programming (MILP) to co-optimize capacity expansion, unit commitment, and dispatch over both short- and long-term planning horizons. "EnCompass uses a security-constrained unit commit (SCUC) and security-constrained economic dispatch (SCED) to determine the least-cost solution to meet energy demand. SCUC determines when generators will be online or offline, while SCED determines how much energy is dispatched from online or available resources." | Uncertainty in EnCompass is primarily represented through scenario analysis and parameter sensitivity testing. Users can create alternative futures by adjusting assumptions such as fuel prices, technology learning rates, load growth, carbon policy, and renewable targets. | Yes, emissions compliance | Emissions modeling | https://www.yesenergy.com/encompass https://www.yesenergy.com/hubfs/Updated%20Solution%20Sheets/Yes%20Energy-EnCompass.pdf?hsLang=en | |||
6 | Energy Policy Simulator (EPS) | Policy impact analysis on emissions, energy use, and cash flows EPS’s scope is to provide a transparent, economy-wide view of how different energy policies shape emissions, costs, technology pathways, and health outcomes. It is more of a policy planning and education tool than a detailed operational power system model. | Evaluates the effects of energy and environmental policies. The purpose of the EPS is to evaluate how different energy and climate policies affect emissions, technology deployment, costs, and health outcomes across the economy. It is designed as a transparent, user-friendly tool to help policymakers and stakeholders compare policy scenarios and make informed decisions. | Global, including the US. | Policymakers and researchers. | Publicly available | No, The Energy Policy Simulator does not perform endogenous capacity expansion; instead, it relies on exogenous assumptions or user-defined policy inputs to simulate changes in capacity over time. EPS states that electricity generation is endogenously determined based on existing capacity, demand, and generation costs, with mechanisms for cost-driven retirements, new builds, and storage deployment built into the model. | No. EPS does not model endogenous storage capacity expansion; storage deployment is driven by policy inputs or assumptions rather than by cost-optimization or system needs. EPS explains that storage capacity is decided within the model, where it can build, charge, and discharge storage based on costs, profitability, and reliability needs. | No detailed modeling Yes, EPS includes transmission in a simplified way by accounting for costs and losses but it does not model detailed power flows or congestion. | No. EPS does not include endogenous transmission capacity expansion; any changes to transmission infrastructure must be specified through user-defined inputs or policy levers. | No explicit price projections | Annual resolution Yes, EPS runs in annual steps with a typical forecasting horizon from the present year out to 2050. | No information found. Yes, EPS includes a representation of planning reserve requirements, ensuring that capacity expansion maintains reliability margins. | | Varies depending on the tool used Yes, EPS has a documented model formulation built in a system dynamics framework using Vensim, with detailed equations and structure available in its open-source documentation. | The Energy Policy Simulator (EPS) handles uncertainty through scenario analysis and transparent, customizable assumptions. While it doesn't use probabilistic methods or produce confidence intervals, users can explore multiple policy scenarios to assess sensitivity and robustness. No, EPS does not explicitly model uncertainty. Users represent uncertainty by running alternative scenarios. | Yes, the Energy Policy Simulator (EPS) provides insights into environmental compliance by modeling the impacts of various climate and energy policies on emissions and other environmental metrics | Models policy impacts (CO2 pricing, emissions caps) Yes, EPS includes environmental features by tracking greenhouse gases and air pollutants and estimating health impacts from changes in emissions. | https://docs.energypolicy.solutions/models/us https://docs.energypolicy.solutions/ | |||
7 | GE Grid Solutions | GE Grid Solutions, is a family of softwares focusing on the introduction and integration of renewables into existing grids. | Modernizing the power grid by integrating new renewable generation and diversifying energy sources. It appears that specialized versions of the model are developed for various clients, making many of the characteristics adjustable** | Global | Power investors and policymakers. | Obtained through a license from GE Vernova. | No; GE’s system is not designed for capacity expansion planning; more focused on reliability and operational studies. | Limited endogenous storage capacity capabilities; can model storage as part of operational reliability, but not for investment planning. | Yes; includes detailed physical modeling of AC power flow and contingencies | No; GE Grid Solutions does not support planning new lines as part of an optimization. | Variable | Variable | Variable | | GE Grid Solutions provides several simulation-based models such as MARS (Multi-Area Reliability Simulation) and PSLF (Positive Sequence Load Flow). These tools are used for system reliability and operational studies rather than economic planning or optimization. | Uncertainty in GE tools is directly incorporated through probabilistic modeling. MARS, in particular, uses Monte Carlo simulations to account for variability in generator outages, demand, hydro inflows, and transmission availability. | Yes | Variable | https://www.gevernova.com/grid-solutions/ | |||
8 | GenX | Electricity market and decarbonization modeling. | Models least-cost electricity generation with flexibility. The purpose of GenX is to support research and decision-making by simulating the operation and investment of electric power systems to find cost-effective and reliable pathways for decarbonization. | US (National). It is a globally applicable, open-source electricity system optimization model that can be configured for any region or market context. | Energy system modelers, and researchers. | Open-source | Yes, GenX optimizes generation capacity additions over time to minimize system costs while meeting constraints. | Yes, Storage is modeled endogenously, including investment decisions, charging/discharging behavior, and operational constraints. | Models regional transmission flow limits | Yes, If candidate transmission lines and costs are included, GenX can endogenously expand transmission capacity. | Models wholesale and retail electricity prices | Long-term (decades) | Includes reserve capacity and flexibility | | Varies depending on the tool used | GenX (Generation Expansion Planning Model) handles uncertainty by supporting stochastic optimization and scenario analysis. It allows users to model uncertain variables like demand, renewable output, and fuel prices using multiple future scenarios, providing more robust investment and dispatch strategies under uncertainty. | Yes | Incorporates decarbonization pathways | https://genxproject.github.io/GenX.jl/stable/#What-is-GenX? https://energy.mit.edu/genx/ | |||
9 | GridPath (Owner is Sylvan Energy Analytics) | "Grid-analytics platform for capacity expansion, production cost, and resource adequacy analyses" | "GridPath was developed to catalyze innovation in resource planning and decarbonization strategy. It is designed to empower users to navigate the complexities of modern power systems. Whether it’s evaluating the integration of renewable energy sources, assessing the impact of new regulatory frameworks, or planning for long-term resource adequacy, GridPath's comprehensive capabilities can provide the detailed, actionable insights needed to make informed decisions." | Multiple Load zones, user defined | Utilities, system operators, regulatory agencies, national laboratories, consulting firms, and NGOs | Free and open for download on Github | Yes | Yes | Yes | Yes | "GridPath can include market functionality and can optimize the dispatch of a resource or a set of resources to maximize market revenue while meeting other system constraints." | 1 to 25 Years | "GridPath can allow a resource or a set of resources to participate in markets with pre-specified price streams, assuming resources are price-takers and subject to market volume limits." | Capacity Model | "Linear, mixed-integer, and non-linear formulations are possible depending on the selected modules" | No explanation of uncertainty | Allows for policy options | "GridPath can optionally impose targests for energy production by eligible projects, e.g., renewable portfolio standard requirements. GridPath can optionally impose an carbon cap constraint." | GridPath https://gridpath.readthedocs.io/en/latest/ | |||
10 | Haiku (RFF is owner) | The model accounts for capacity planning, investment, and retirement over a multi-year horizon in a perfect foresight framework, and for system operation over seasons of the year and times of day. | Haiku is a simulation model of regional electricity markets and interregional electricity trade in the continental United States\ | Regional within the continental United States | Policy makers, regulators, consultants | Available under a licensing agreement. | Haiku accounts for both capacity that is under construction or planned for construction in the data year, and for endogenous investment in new capacity. | Not specifically stated | Yes, transmission line losses and availability are considered | Only exogenous growth in interregional transmission capacity is allowed. Cost of this expansion is not considered. | "Haiku has reduced-form fuel market modules that endogenously determine prices for coal, natural gas, and biomass. The costs of wind and geothermal resources are represented as supply curves that reflect the increasing cost of those resources as more are taken up. This section describes each of these modules. Prices for oil, nuclear fuel, and landfill gas are specified exogenously and may change over time." | Multi-Year | "Haiku models the requirement for electricity generation and reserve services in each time block depending on electricity demand, the reserve margin requirement, interregional power trading, and losses in interregional and intraregional transmission and distribution. " | Capacity Model | "The mathematical program that Haiku solves is a zero finding problem. The model iterates to find an equilibrium in the spatially and temporally linked electricity markets that achieves simultaneous compliance with a large set of constraints. These constraints are designed to identify the minimum cost strategy for operation and capacity planning of the electricity system to satisfy price responsive electricity demand functions given a wide set of regulatory institutions. " | "Two techniques are used to overcome uncertainty; calibration and delta analysis. The model is calibrated to match a subset of the electricity sector outcomes published annually by the EnergyInformation Administration (EIA) in the Annual Energy Outlook (AEO) and the suite of documents that accompany it. Haiku uses delta analysis, which focuses on the comparison of a baseline scenario with alternative scenarios. This method renders erroneous point estimates irrelevant except to the extent that the calibrators are correlated with alternatives of scenario specification." | Haiku explores public policy changes and options on a regional and national scale | Pollution Controls, | Haiku Electricity Model https://media.rff.org/documents/RFF-Rpt-Haiku.v2.0.pdf | |||
11 | Hitachi Gridview (Formerly ABB Gridview) | Simulates the operation of competitive electricity markets while enforcing complex engineering constraints imposed, for example, by thermal and security restrictions or interface and simultaneous transfer limits. Specifically, GridView simulates the day-ahead energy market in an hour-by-hour chronological sequence, for time spans ranging from one day to several years. Simulate operation of electricity markets by enforcing engineering constraints. Strive to simualte the "day-ahead" energy market for time spans ranging from a single day to years. | Designed for energy producers to make the most informed short-term financial decisions possible. Find the optimal operating strategy to minimize the total system-wide costs. | Small Scale / Intra grid within North America. Functions by splitting large grids into smaller, computable sections. North America, with the abilty to split large grids into smaller more easily cimputable sections | Energy producers and grid operators. Electric grid operators, including Independent System Operators (ISOs) and Regional Transmission Organizations (RTOs) | Obtained through a license from Hitachi Energy. | Yes; supports planning of new generation resources based on cost optimization. | Yes; allows for the modeling of battery and other storage types with both investment and operational modeling. | Line-flow index modeling with kirchoff's law. | Yes; optional module to include transmission expansion decisions in the planning horizon. | Sub-hourly price projections based on transmission constraints, generation, and outages | 24 Hours to years | Short-Term | Production Cost Model | Production cost and long-term planning tool that uses mixed-integer linear programming (MILP) to simulate unit commitment, economic dispatch, and, optionally, capacity expansion. It allows detailed modeling of thermal generation, renewables, and storage technologies over multi-year timeframes, focusing on sub-hourly projections.. | Uncertainty in Gridview is generally addressed through scenario and sensitivity analysis. Users can define a range of deterministic scenarios with different assumptions for technology costs, demand growth, policy targets, and renewable penetration. Gridview supports the use of probabilistic inputs for renewable generation and demand, allowing for variability in these profiles across scenarios. | No | None | https://www.hitachienergy.com/news-and-events/press-releases/2021/07/hitachi-abb-power-grids-is-evolving-to-become-hitachi-energy-and-broadens-commitment-to-a-sustainable-energy-future https://www.hitachienergy.com/us/en/products-and-solutions/energy-portfolio-management/enterprise/gridview https://publisher.hitachienergy.com/preview?DocumentID=9AKK106930A8192&LanguageCode=en&DocumentPartId=A4-web&Action=Launch | |||
12 | Integrated Planning Model (IPM) | IPM is designed for power market forecasting, capacity expansion, and environmental compliance. It integrates economic dispatch, unit-level power plant operations, and environmental regulations in a long-term optimization model. | IPM serves as a comprehensive tool for power market and environmental policy analysis. It identifies the least-cost approach to meeting electricity demand while ensuring compliance with environmental constraints, such as CO₂ caps and emissions trading programs. | Primarily used in the USA and North America. Versions of IPM have also been used for European power markets, but its primary applications are for U.S. federal and state-level environmental and market policies. | Regulators like the EPA and FERC, as well as utilities and policymakers, use the Integrated Planning Model (IPM) for environmental and capacity planning. | Proprietary, licensed by ICF International. IPM is not publicly available and requires contracting through ICF. | Yes – IPM is explicitly a capacity expansion model that endogenously decides new generation builds. For each modeled plant, it includes decision variables for potential new capacity additions in future years | Yes – IPM treats storage as another resource that can be built. Its set of investable technologies includes storage options (e.g. pumped storage, batteries) alongside generation. | IPM does not explicitly model Kirchhoff’s Laws. Instead, it represents transmission as energy transfers among predefined regions, constrained by maximum transfer limits. This means it does not account for loop flows or voltage constraints. | Yes – IPM can model new transmission investments, although this feature is optional. IPM represents inter-regional transmission links and can add new line capacity as decision variables, but EPA typically disables it in base cases due to uncertainty. | IPM projects wholesale energy prices and capacity prices at a regional level, considering factors such as fuel prices, emission costs, and investment decisions. | IPM typically models multi-decade timeframes, ranging from 20 to 30 years, making it suitable for long-term planning of capacity expansion and environmental regulations. | Primarily long-term reserves. IPM ensures that sufficient generation capacity exists to meet peak demand and maintain long-term system reliability, but it does not model real-time dispatch of reserves. It accounts for planning reserve margins but lacks insight into operational reserves or short-term ancillary services like frequency regulation and spinning reserves. | | IPM is a linear programming optimization model. Its mathematical formulation is a large-scale linear program (LP) that guarantees a globally optimal solution. | IPM is a deterministic model with perfect foresight. It assumes all future conditions (demand, fuel prices, etc.) are known and does not internally model uncertainty or randomness. Uncertainty is handled via scenario analysis or sensitivity cases outside the model. | IPM is designed to evaluate compliance with environmental regulations, including cap-and-trade programs, emission limits, and air quality standards. | IPM forecasts emissions of CO₂, NO, SO₂, and other pollutants. It evaluates compliance costs and models market-based regulatory mechanisms like cap-and-trade systems. | https://www.icf.com/work/utilities/ipm | |||
13 | Markal | Regional energy deployment and capacity expansion. | Projects regional power generation and grid investment. | US (National). MARKAL can represent a single region or multiple regions connected by trade flows, allowing applications at national, regional, or global scales. | Utilities, regulators, and policymakers. | Open-source | Yes, MARKAL endogenously optimizes generation investments to meet energy service demands at minimum system cost. | Yes, Storage technologies can be modeled endogenously, allowing the system to invest in them if cost-effective. | Models regional transmission constraints and expansion. | No (or Limited), While extensions may allow some representation, standard MARKAL does not model detailed endogenous transmission expansion. | Projects energy market conditions | Long-term (decades) | Includes reserve requirements in grid planning | | N/A MARKAL is formulated as a linear programming problem that minimizes total discounted system cost subject to energy balance, capacity, and emission constraints. | N/A The model has stochastic and multi-scenario extensions (e.g., stochastic MARKAL) that represent uncertainty in future technology costs or policies. | Yes | Includes environmental policy impacts and emissions reductions | https://unfccc.int/resource/cd_roms/na1/mitigation/Module_5/Module_5_1/b_tools/MARKAL/MARKAL_Manual.pdf | |||
14 | NEMS (Owned by US EIA and OnLocation) | Economy-wide energy system including detailed demand sectors (residential, commercial, industrial, transportation), conversion (electricity, liquid fuels), and supply (all fuel types) with macroeconomic linkages | Comprehensive modeling framework that produces projections of U.S. energy markets with equilibrium solutions for energy supply and demand; basis for Annual Energy Outlook | USA | EIA, federal policy makers, regulators, utilities, energy analysts | Proprietary: Developed and maintained by U.S. EIA; note that system is being overhauled for future versions | Yes, the Electricity Market Module (EMM) determines optimal capacity expansion based on regional electricity demand, technology costs, fuel prices, and policy constraints; incorporates existing generating capacity, planned additions, economic retirements, and new builds; models both utility and non-utility generation; accounts for renewable portfolio standards and environmental regulations. | Limited endogenous storage capacity modeling primarily within the Electricity Market Module (EMM). Focuses on traditional technologies like pumped hydro storage with evolving representation of battery storage. Storage capacity expansion determined at regional rather than nodal level. Storage valuation includes energy arbitrage and capacity value but has limited representation of other value streams. | Yes, Included in the Electricity Market Module; appears to use a simplified transmission representation with regional transfer capabilities rather than detailed power flow. | Models limited transmission expansion between regions based on economic criteria; considers transmission costs for new generating capacity siting; lacks detailed geospatial transmission planning but includes aggregated interregional transmission capability expansion. | Projects wholesale electricity prices, capacity prices, and fuel prices through 2050; incorporates macroeconomic factors in price formation | Not explicitly stated, but likely extends to 2050 based on decarbonization pathway analysis | Likely considers reliability requirements and reserve margins in capacity planning modules, though not explicitly detailed | | Uses linear programming for electricity capacity expansion and dispatch; integrates non- linear formulations for some demand and supply relationships; implements hybrid equilibrium approach through iterative convergence between modules; combines optimization and simulation methods as appropriate for different sectors | Primarily uses scenario analysis to address uncertainties; includes alternative cases with varying assumptions for key drivers; sensitivity analysis available for specific parameters | Designed to analyze compliance with various regulations; Emissions Policy Module calculates impacts of environmental regulations | Emissions Policy Module calculates CO₂ emissions from fossil fuel consumption; can model impacts of climate and environmental policies | https://www.eia.gov/outlooks/aeo/nems/documentation/ | |||
15 | OSeMOSYS | Long-run integrated assessment and energy planning designed to be used as a model generator to support long-term energy system decarbonization efforts. | OSeMOSYS serves to be a free and open source energy system model that requires a less significant leanring curve compared to existing models | Global regions | Analysts and energy investors | Free and open for download on Github | Yes, OSeMOSYS Global can be used as a model generator to carry out power system capacity expansion analysis of different geographical regions in the world. | No, Results from OSeMOSYS Global do not yet include energy storage functionality and are later discussed in future work opportunities. | Yes, transmission line capcity is considered | Yes, OSeMOSYS affords the opportunity to increase transmission capacity to help balance loads and demand across larger regions in an effort to balance capcity and cost | Manual Input of pricing | Up to 25 Years | Not explicitly stated | Capacity Model | Linear Programming mathetical model | Scenario exploration and sensitivity checks | OSeMOSYS allows for policy analysis scenarios and can be used to predict policy outcomes at a local or global scale | Focused on how regional trade and carbon tax factors contribute to variable renewable energy investment pathways | OSeMOSYS - Home https://www.nature.com/articles/s41597-022-01737-0 | |||
16 | PlanOS' Production Cost (formerly MAPS) Owner is GE Vernova | Comprehensive software platform to plan for a reliable and stable energy systen using robust algorithmic capabilities to break down traditional silos. | Analyze the economics, reliiability and power flow of your system. | Global (12 countries) | Power investors and grid operators/stabilizers. | Obtained through a license from GE Vernova | No | Yes, models storage for resource adequecy | Yes, can model individual transmission lines where limits can be specified for the flow | No, only supports identification of transmission lines. | Production Cost Model | The model uses proven software (MAPS, MARS and PSLF) to calculate system reliability and capacity expansion. | https://www.gevernova.com/content/dam/Energy_Consulting/global/en_US/pdfs/planos/GEA35657-GEV-PlanOS-Production-Cost-Module.pdf | |||||||||
17 | Plexos (by Energy Exemplar) | PLEXOS models long-term capacity expansion (LTCE) planning to support informed decisions on retiring existing assets or investing in new projects. It performs market analysis and forecasting, evaluates renewable energy integration, and provides emissions modeling with plant-level emission rates for CO₂, SO₂, and NOₓ. PLEXOS also offers advanced visualization of analytical results and daily queries and refreshes for up-to-date datasets | To optimize long-term capacity expansion and market operations within the Eastern Interconnection system. | The majority of the Eastern Interconnection in North America, covering approximately 38 U.S. states and parts of Canada. | Non-profit utilities (e.g., Salt River Project), investor-owned utilities (e.g., NV Energy), renewable energy developers, government agencies, regional transmission organizations (RTOs) and independent system operators (ISOs), and consulting firms. | Obtained through a license; proprietary to Energy Exemplar. | Yes, PLEXOS model includes endogenous generation capacity expansion. It utilizes its Long-Term (LT) Plan module to optimize generation investments and retirements while minimizing the total system cost. PLEXOS integrates generation capacity expansion with transmission expansion planning, enabling a co-optimized approach that accounts for power flow constraints and infrastructure limitations. PLEXOS determines when new generating units should be built based on projected demand, resource adequacy, and market conditions. It also assesses economic retirements, where aging plants are shut down if they are no longer cost-effective | - Yes, Storage can be modeled as a generator-type object with investment cost parameters, technical lifespan, and unit constraints, making it eligible for capacity expansion decisions during LT Plan simulations. - Storage assets (like batteries or pumped hydro) can be modeled with charging/discharging behavior, round-trip efficiency, capacity, and cost parameters | This platform supports transmission expansion planning by modeling both existing and potential new transmission lines. It utilizes co-optimization techniques to assess generation and transmission investments.Plexos incorporates a DC load flow model, which approximates power flows in the transmission network and respects Kirchhoff's laws. | For the transmission capacity, PLEXOS allows users to analyze whether to build new AC/DC transmission lines and/or retire existing transmission lines based on economic and technical considerations. Specifically for the expansion option, PLEXOS allows for expanding existing transmission interfaces and PLEXOS performs the expansion model in MW and include cost factors such as Expansion cost ($MM/MW); Maximum expansion capacity (MW); Weighted Average Cost of Capital (WACC) for discounting expansion investments. | Price Analysis: Market assessment include: Projecting pool prices under various scenarios of load growth and new entry Nodal Pricing: calculated locational marginal price able to show the shadow price of the capacity | 32 years 20 years for LTCE | Plexos represent the short term/operational reserve including spinning and non spinning reserve. For the Long-term reserve, Plexos evaluates the capacity expansion and retirement schedule in order to identify sufficient reserve margins to meet future demand and contingency scenarios. | Capacity Model | The capacity expansion problem in PLEXOS is formulated as a Mixed-Integer Linear Program (MILP), which ensures integer-based decisions for generator additions and retirements | PLEXOS handles uncertainties of fuel price by using Stochastic Optimization which simulates different fuel price scenarios (high, medium, low) to assess each price model on dispatch, generation costs, and investment decisions. It assigns probability distributions to fuel prices, which allows for risk-adjusted decision-making. PLEXOS also allows users to run scenarios where fuel prices vary based on historical trends, market projections, or user-defined probability distributions. These scenarios help model the effect of extreme price fluctuations on system operations and capacity expansion. Last but not least, the model incorporates Monte Carlo analysis to simulate random variations in fuel costs, outages, and renewable energy output | Plexos represent the environmental regulations, it can simulate compliance with state RPS, and maintain system reliability (compliance with the reliability standard) by modeling resource adequacy and integrating renewable energy sources. | Enhanced emissions modeling with plant level emission rates and prices for CO2, SO2 and NOx enabling thorough analysis to achieve emission reduction targets. This includes specific CO2 emission rates and prices for plants participating in the RGGI (Regional GHG initiative) program and uses the CSAPR (Cross State Air Pollution) categories for SO2 and NOx prices. | https://www.energyexemplar.com/plexos | |||
18 | Power System Optimizer (Polaris) | PSO serves as an industrial-grade engine for power system optimization and market simulation. It is designed to manage multiple aspects of power systems, including system expansion, security-constrained unit commitment and economic dispatch (SCUC and SCED), optimized energy conversion (Power-to-X or P2X), resource adequacy, and co-optimized gas-electric operations. | PSO is a production cost market simulator that optimizes energy and ancillary services dispatch across multiple timeframes, from years to minutes. It models intra-hour dynamics, simulates uncertainties in load and generation using probabilistic inputs or historical data, supports custom ancillary services, performs stochastic optimization, values uncertainty through reserve deployment, and simulates energy storage based on efficiency. | PSO's versatility allows it to be applied to various geographic regions. The ENELYTIX® platform, which integrates PSO, offers ready-to-run datasets covering all three interconnections serving the United States and Canada. Additionally, datasets are available for European countries, including all European Union nations, Great Britain, Switzerland, and Norway. | Utilities, Independent System Operators (ISOs), Regional Transmission Organizations (RTOs), policymakers, researchers, and consulting firms. | Obtained through a license. | Yes, The Power System Optimizer (PSO), also known as "System Optimizer," is explicitly categorized by the U.S. Department of Energy as a Capacity Expansion Model. According to a report by the Department, the System Optimizer is listed among utility-scale capacity expansion models. This classification confirms that PSO inherently includes endogenous generation capacity expansion, meaning it optimizes both the timing and type of new generation capacity additions based on evolving system requirements and constraints. | No, the model doesn’t provide any endogenous strìorage capacity options | The PSO model provides two transmission modeling methods. The first is a detailed nodal approach, ensuring power flow feasibility by strictly adhering to Kirchhoff’s laws, similar to real-world operational practices. This method calculates individual transmission line flows, enforcing constraints and losses accurately. Alternatively, PSO supports a simplified zonal ("pipe-and-bubble") method, where users can explicitly define transfer limits and interface capacities between regions without strictly applying Kirchhoff’s laws. This dual approach allows users flexibility depending on their modeling objectives and requirements. | Yes, the PSO model inherently supports endogenous transmission capacity expansion within its optimization framework. This means PSO simultaneously optimizes transmission investment decisions along with generation resources, accounting for economic costs, system constraints, and environmental compliance. When operating in expansion planning mode, PSO treats transmission upgrades and new line additions as internal decision variables. It evaluates both existing infrastructure and potential new investments, ensuring grid reliability and meeting defined network constraints. Unlike simpler models requiring predetermined transmission inputs, PSO autonomously identifies optimal transmission expansion pathways based on detailed system and economic parameters. | PSO can simulate various market prices, including hourly spot prices (LMPs), wholesale prices, capacity market prices, and ancillary service prices. The model provides these price projections by accurately reflecting real-time or day-ahead market conditions, resource adequacy requirements, and market-specific designs. This comprehensive capability makes PSO useful for detailed evaluations of market scenarios, revenues, costs, and resource viability. | PSO supports both short-term operational modeling and long-term forecasts, typically spanning multi-decade horizons (20–30 years). | The PSO model simulates short-term reserves required for operational reliability: It optimizes energy and ancillary services jointly. Long-term reserves (e.g., for capacity planning or adequacy) are not part of this analysis. | Production Cost Model | PSO uses MIP to jointly optimize unit commitment and economic dispatch, including start-up/shut-down decisions and minimum run times. This helps mimic real-world ISO/RTO operations closely. | PSO can model uncertainty and intra-hour dynamics (forecast errors, fast-ramping needs, etc.) | No | By enabling more efficient dispatch and increased use of lower-cost (often cleaner) power, such as wind/solar from SPP to WEIS, the model reduces the need for dirtier local generation. But again, there's no explicit environmental modeling, such as carbon pricing or emissions caps. | https://psopt.com/ | |||
19 | PROMOD | PROMOD is a production cost simulation model that performs hourly dispatch and security-constrained unit commitment for detailed electricity market forecasting. It focuses on nodal price formation, congestion analysis, and financial transmission rights valuation. | PROMOD is designed to model market dynamics under different conditions. It simulates hourly dispatch, economic dispatch with transmission constraints, and calculates locational marginal prices (LMPs) to inform decision-making in deregulated markets. | Primarily used in the USA, especially in regional transmission organizations (RTOs) such as PJM, ERCOT, and MISO. However, it has been adapted for use in international markets. | PROMOD is used by market operators, transmission planners, Independent System Operators (ISOs), utilities, and consultants to assess market trends, congestion risks, and generation investment decisions. | Proprietary, licensed by Hitachi Energy. PROMOD is commercial software that requires a paid subscription. | No – PROMOD is a production cost simulation model and does not endogenously build new generation capacity. It focuses on operating an existing fleet. In fact, production cost models explicitly do not invest in new generation as part of their functionality. | No – Because PROMOD does not perform capacity expansion, it cannot decide to build new storage resources. Any storage (e.g. batteries or pumped hydro) must be predefined in the input fleet. PROMOD will simulate the operation of storage if present, but it will not invest in additional storage capacity on its own. | PROMOD explicitly models transmission congestion and nodal price differences. It adheres to Kirchhoff’s Laws by incorporating DC load flow equations, ensuring physical feasibility of power transfers while enforcing transmission constraints. | No – Similarly, PROMOD does not endogenously add new transmission lines. It can simulate power flows on an existing transmission network, but it will not plan or optimize new transmission builds. | PROMOD simulates market clearing prices at a nodal level, generating locational marginal prices (LMPs) based on security-constrained economic dispatch. | PROMOD is mainly used for short- to mid-term forecasts, typically between 1 to 10 years, with a focus on hourly or sub-hourly dispatch simulation. | Primarily short-term reserves. PROMOD explicitly models security-constrained unit commitment (SCUC) and economic dispatch (SCED), meaning it accounts for short-term reserve requirements such as spinning reserves, non-spinning reserves, and contingency reserves. It is frequently used to evaluate operational reliability over hourly to daily timescales. | | PROMOD is formulated as a security-constrained unit commitment and dispatch model, which translates to a mixed-integer optimization problem. It simulates hourly generation dispatch with unit commitment decisions (on/off status of units), typically solved via MILP. | PROMOD runs are generally deterministic for a given input scenario, but the model can capture operational uncertainties through multiple scenario runs or chronicle variability. In practice, planners address uncertainty by running PROMOD under various futures (fuel price cases, load forecasts, outage scenarios). The model itself encompasses variability in inputs (e.g. hourly renewable output profiles, load shapes) rather than stochastic optimization. | PROMOD allows emissions costs to be included in economic dispatch but does not optimize compliance strategies. | PROMOD can incorporate environmental regulations by including carbon prices and emissions constraints in the dispatch model. | https://www.hitachienergy.com/products-and-solutions/energy-portfolio-management/enterprise/promod | |||
20 | PyPSA | Focused on electricity networks with detailed power flow modeling; supports multi sector coupling (electricity, transport, heat, industry); enables renewable energy integration analysis. | Stimulates and optimizes modern energy systems with features including conventional generators, renewable generation, energy storage, sector coupling, and transmission networks. | EU and USA. | Academic researchers, energy system planners, and policy analysts. | Open-source and freely available, with user guidelineguides accessible at pypsa.readthedocs.io. It is Python-based and supported by an active user community. | Yes, optimizes generation capacity expansion using linear or mixed-integer linear programming; accounts for renewable variability and network constraints; supports both greenfield optimization and brownfield expansion of existing systems; allows for co-optimization of generation, storage, and transmission; scales well with large networks and long time series for investment planning. | Yes, comprehensive endogenous storage capacity optimization through linear or mixed-integer programming. Co-optimizes storage deployment alongside generation and transmission. Supports multiple storage technologies (batteries, pumped hydro, hydrogen, compressed air, thermal storage) with various technical parameters. Enables detailed temporal and spatial resolution to capture full storage value. Accounts for sector coupling across electricity, heat, and gas vectors. | Models AC and DC transmission networks with power flow constraints based on Kirchhoff's Laws; allows detailed line capacity constraints; supports user-defined flow constraints | Supports transmission expansion planning with constraints on available corridors and maximum capacities; co-optimizes with generation expansion; includes costs of transmission infrastructure in objective function; can model both AC and HVDC expansion options. | Calculates optimal system costs and shadow prices reflecting marginal costs; doesn't explicitly forecast market prices but optimizes system costs | Through 2050 (annual resolution) | No information found. | Capacity Model | Primarily uses linear programming (LP) for large-scale problems; supports mixed-integer linear programming (MILP) for unit commitment constraints; implemented in Python using Pyomo optimization interface; connects to various solvers including GLPK, CPLEX, Gurobi, CBC; structured in modular fashion for flexibility | Supports deterministic scenario analysis for key uncertainties; can implement stochastic optimization with appropriate extensions; allows sensitivity analysis across key parameters; recent versions exploring integration with machine learning for uncertainty quantification; Monte Carlo simulations possible through scripting | Open-source compliance with transparent methodology; doesn't explicitly address regulatory compliance | Detailed modeling of renewable energy potentials; integration of variable renewable sources; sector coupling for decarbonization pathways; emissions constraints can be included | https://docs.pypsa.org/latest/user-guide/user-guide/ | |||
21 | ReEDS 2.0 (Owner is NREL) | From NREL site: The ReEDS model informs a range of electricity sector research questions. These include clean energy policy, renewable energy integration, technology innovation, and other forward-looking generation and transmission infrastructure issues. | ReEDS 2.0 is the NREL's flagship power sector model for capacity and generation planning in North America. | North America (focused on the 12 US power grids) | Power sector employees and policy makers. | Open-source, GitHub | Yes ; this is ReEDS’ core capability; optimizes new builds based on costs, constraints, and policy. | Yes; models storage technologies with operational and investment decisions. | This model supports high-fidelity modeling of theoretical transmission lines, requiring user input of net transmission. | Yes; co-optimizes generation and transmission expansion, allowing new transmission investments between regions. | Incorporates fuel prices and projected wholesale price of energy | Longterm (15-40 yrs) | Long-term | | ReEDS 2.0 uses linear programming (LP) to minimize total system costs over multi-decade planning horizons, typically through 2050. The model co-optimizes generation, storage, and transmission investment decisions while satisfying a wide range of policy, reliability, and technical constraints. It uses a spatially resolved structure with around 130 balancing areas and transmission zones, allowing for regional transmission limits and renewable resource availability. Temporal granularity is achieved using representative time slices (typically 17 per year), capturing diurnal and seasonal variability. | ReEDS handles uncertainty primarily through user-defined scenario analysis. Analysts can define and run multiple deterministic scenarios that reflect different assumptions for fuel prices, technology costs, policy regimes, demand growth, and renewable deployment. While ReEDS does not include built-in stochastic programming or probabilistic modeling, uncertainty is indirectly captured by comparing outputs across diverse scenario sets. | No | Carbon Constraints function for dictating when certain fossile fuel plants must be retired. | https://docs.nrel.gov/docs/fy21osti/78195.pdf | |||
22 | REPEAT | Policy assessment with economy-wide coverage; RIO component specializes in electricity capacity expansion with high temporal resolution and sector coupling. | Provides environmental and economic evaluation of federal energy and climate policies with spatially-explicit modeling; RIO focuses on temporal challenges of renewable integration. | USA | Policymakers, stakeholders, researchers, and government agencies. | Availability for external users not specified. | REPEAT focuses on policy-driven capacity expansion evaluation with highly spatially-explicit infrastructure modeling; assesses federal policy impacts on renewable deployment and technology adoption; provides detailed geographic visualization of infrastructure buildout; quantifies investment requirements and deployment rates under policy scenarios; evaluates comparative impacts across policies | Limited endogenous storage modeling primarily through RIO integration; handles both traditional and emerging storage technologies with focus on balancing renewable generation variability; evaluates storage deployment for managing periods of under/over-generation within a temporally-resolved framework;considers economy-wide energy storage solutions. | Yes. Employs spatially-explicit transmission modeling with high geographic resolution; focuses on mapping critical transmission corridors for renewable integration; models transmission constraints as part of infrastructure planning; identifies potential congestion points; includes detailed geographic visualization of required transmission expansion. | Includes transmission expansion needed for renewable integration based on Net-Zero America methodology; focuses on high-level transmission corridors rather than detailed network topology; addresses transmission bottlenecks in renewable-heavy scenarios; maps potential transmission corridors with geographic constraints. | Not explicitly detailed, but likely models wholesale electricity prices as part of economic evaluation | Not explicitly mentioned | Not explicitly mentioned, but likely considers reliability constraints given focus on renewable under-generation challenges | Capacity Model | Detailed formulation not specified in source material; likely employs optimization approaches for capacity expansion planning; RIO component described as "highly temporally resolved" suggesting sophisticated mathematical treatment of time dynamics; probably integrates GIS-based spatial analysis with traditional energy system optimization | Not explicitly detailed in source material, but most likely employs scenario analysis for key uncertainties | Evaluates policy impacts and implicitly addresses regulatory compliance scenarios | Detailed environmental impact assessment of policies; spatially-explicit emissions modeling; renewable integration analysis | https://repeatproject.org/docs/REPEAT_Climate_Progress_and_the_117th_Congress.pdf?utm_source=chatgpt.com | |||
23 | RPM (Owner is NREL) | National level capacity expansion model | Optimize generation, transmission, and energy storage investments to meet long-term energy policy goals | USA | Utilities, ISOs, RTOs | Free and open for download on Github | Yes | Yes | Yes | Yes | "Future capital costs and fuel costs are inherently uncertain, and RPM allows users to set these prices to facilitate scenario analysis. The cost projections used here are intended to demonstrate model capabilities and features, and they do not represent forecasts of future price and performance potential." | Up to 20 years | RPM uses operating reserve constraints to ensure sufficient contingency and frequency regulation reserves are available for each model hour. | Capacity Model | RPM formulation is written in the General Algebraic Modeling System (GAMS) platform and utilizes both nodal and zonal structures for system modeling | Scenario exploration and Analysis | Allows for policy constraints | Allows for constraints for annual renewable portfolio standard and emission requirements | https://docs.nrel.gov/docs/fy13osti/56723.pdf | |||
24 | Strategist | Strategist is a long-term electric power planning model primarily used for integrated resource planning. It simulates utility systems over multi-year horizons, focusing on generation resources and demand to meet load at least cost. It evaluates capacity expansion options and performs production cost simulations to estimate system operation costs and emissions over time. | The primary goal of Strategist is to identify the least-cost resource plan for meeting future electricity demand under given constraints. It serves as a decision-support tool for utility planners, evaluating thousands of possible expansion portfolios to determine which mix of new resources minimizes costs while reliably serving load. | Strategist is commonly used in North America, especially in the United States, for electric utility resource planning. Many investor-owned utilities and regulatory commissions use it for Integrated Resource Planning (IRP) processes. | Electric utilities, regulatory agencies, and consulting firms involved in long-term resource planning and policy analysis. Used by utilities for internal planning and by public utility commissions to review IRPs. | Strategist is a proprietary, commercially licensed software originally developed by Ventyx (now part of Hitachi Energy). It is not open-source and requires a vendor license for access. | Yes – Strategist is an integrated resource planning tool that endogenously selects new generation (and demand-side) additions. Its PROVIEW module performs automatic expansion planning by evaluating countless capacity mix options. Strategist/PROVIEW produces a least-cost plan for meeting future load and reserve requirements. It systematically searches through combinations of supply and demand resources to find the plan with minimum present-value cost that satisfies reliability constraints. | Yes – Strategist can include energy storage options as part of the supply-side expansion alternatives. The model will treat a storage project (e.g. a battery farm or pumped storage unit) like any other resource: if it’s defined in the database with cost and performance characteristics, PROVIEW can choose to build it. | Strategist does not explicitly model transmission network constraints or power flow in detail. It simplifies transmission by treating the system as a single zone or a few zones without enforcing Kirchhoff’s Laws, meaning it doesn’t simulate line-by-line flows or voltage constraints. | No – Strategist does not optimize transmission expansion. In IRP practice, transmission projects are handled outside of the Strategist modeling. The tool focuses on generation and demand-side resources. For example, when utilities use Strategist, they input only supply-side (plants, renewables, etc.) and demand-side alternatives for the model to consider – new transmission lines are not part of the decision variables. | Strategist is not primarily a market price forecasting model. It focuses on evaluating resource costs and capacity expansion needs rather than predicting competitive market-clearing prices. Users input external market price forecasts for economy purchases or sales. | Strategist is designed for long-term planning, typically modeling multi-decade timeframes from 20 to 30 years or more. Some IRPs use it for 40-year studies to fully assess long-lived infrastructure decisions. | Strategist primarily models long-term planning reserves rather than short-term operational reserves. It ensures adequate capacity margins over the years but does not model real-time reserve scheduling, spinning reserves, or ancillary services in detail. | Capacity Model | Strategist uses a dynamic programming approach for capacity expansion (as opposed to linear programming). The PROVIEW optimization employs dynamic programming to evaluate and rank resource plans. This method enumerates possible build sequences and finds the least-cost plan, rather than solving a single mixed-integer program. | Strategist is a deterministic model. It does not internally model uncertainties or probability distributions – all inputs (load growth, fuel prices, etc.) are fixed per scenario. Planners handle uncertainty by running multiple scenarios or sensitivity cases and then comparing results. In practice, a utility using Strategist will define different scenario input sets (high fuel cost, low load, etc.), run the model for each, and then examine the robustness of the expansion plans. There is no built-in stochastic optimization in Strategist’s algorithm (aside from deterministic what-if portfolio stress tests). | Strategists can model environmental regulations and policy constraints such as carbon pricing, emission limits, renewable portfolio standards (RPS), and other regulatory requirements by integrating these constraints into the capacity expansion optimization. | Strategist tracks and reports emissions (CO₂, NO, SO₂) from the generation fleet and allows users to model carbon pricing, renewable mandates, and emission caps in the optimization. | ||||
25 | Switch | Co-optimization of generation, storage, and transmission. The scope of the SWITCH model is long term planning of electric power systems with a focus on investment and operational decisions needed to meet future electricity demand at lowest cost while achieving policy goals. | Plans least-cost power system development. | US (National). The SWITCH model is global in applicability since it has been applied in regions such as the United States Chile and China and can be adapted for any location with appropriate data. | Grid planners, and renewable energy developers. | Open-source | Yes, Switch determines least-cost generation investment over time, making endogenous decisions on capacity additions. | Yes ,Switch supports endogenous investment in storage technologies, considering technical and economic constraints. | Models interregional transmission flows and expansion constraints. | Yes, Switch can endogenously decide on new transmission investments if candidate lines and associated costs are defined. | Projects spot and capacity prices | Investment periods spanning years to decades | Explicitly models grid flexibility and reserves | Capacity Model | Varies depending on the tool used | Switch handles uncertainty through scenario-based analysis and user-defined input variations. It allows testing different policy, technology, and demand assumptions but does not include built-in probabilistic or stochastic modeling. | Yes | Models renewable energy mandates, carbon pricing | https://switch-model.org | |||
26 | Synapse | From Synapse energy: Synapse performs operational and planning modeling analyses of electric power systems using industry-standard models such as EnCompass, Strategist, Market Analytics, PROMOD, and PLEXOS to evaluate long-term energy plans, assess the environmental and economic impacts of policy initiatives, and review utility system modeling. Our services include identifying the appropriate set of models to inform clients’ analyses; performing modeling studies and analyzing the results; and reviewing, critiquing, and re-running utilities' and project developers' modeling studies and output files. | From Synapse Energy: We started in our early days providing really good electric sector modeling and modeling review to non-utility clients who lacked access. We still do that; but we've expanded to better answer our clients' questions. Our experts use a combination of models and tools--some built by us--to get at the information our clients need. | USA; Customizable for individual clients | Energy investors | Obtained through a license from Energy Economics Inc. | Yes but limited to scenario-based modeling rather than fully optimized expansion; more of a policy analysis tool. | Yes; however, models based on user input and assumptions and linked tools. | Includes simplified modeling of system-level transfer limits based on user inputs. | Not natively supported. | Benefit Cost Analysis for long-term investments | Longer term forecasting focusing on regulatory changes and costs to adhere to environmental compliance | Both | Production Cost Model | Model designed for transparency, accessibility, and policy relevance rather than computational complexity. The models are built in spreadsheet or light-code environments and are scenario-driven rather than optimization-based, highly customized for various users and clients. | Uncertainty is handled via robust scenario analysis and stakeholder-informed parameter variation. Users develop multiple plausible futures to reflect uncertainty in policy adoption, technology development, market conditions, and climate goals. | No | Decarbonization and emissions impact modeling | https://www.synapse-energy.com/services/electric-system-modeling | |||
27 | TEMOA (Tools for Energy Model Optimization and Analysis) | TEMOA is a long-term energy system optimization model that is used for strategic planning. It evaluates energy supply pathways and the impact of policies such as carbon pricing, renewable energy targets, and technology cost reductions. | TEMOA is intended to analyze future energy systems under various economic and policy scenarios. It provides insights into the long-term cost-optimal mix of energy technologies while allowing sensitivity and uncertainty analysis. | Global applicability. TEMOA can be applied to any country or region, provided the necessary data is available. It has been used in the U.S., Europe, and other national energy system studies. | Academia, research institutions, and policymakers for scenario-based energy policy analysis, especially in studies on decarbonization and energy transitions. | Open-source, available under the GNU GPL v2 license. TEMOA can be freely downloaded and modified by users. | Yes – TEMOA is fundamentally a capacity expansion model. It optimizes the deployment of new energy technologies (generation, etc.) over the planning horizon to meet demand at minimum cost. In other words, it will endogenously decide how much of each generation type to build. | Yes –TEMOA can endogenously invest in storage technologies as part of the optimal mix. The model is technology-agnostic: any energy supply or storage tech included in the input data (with costs, efficiencies, etc.) will be considered in the capacity expansion decision. | TEMOA offers flexible spatial representation, allowing users to configure the model either as a single-region (copper-plate) system—treating the entire area without internal transmission constraints—or as a multi-region (zonal) system, explicitly accounting for inter-regional transmission limitations. | No – TEMOA does not explicitly model transmission expansion in its standard form. It is typically a single-region or multi-region energy system model focusing on generation and resource supply. There is no built-in optimization of transmission network builds comparable to power flow models. TEMOA was “strongly influenced by the well-documented MARKAL/TIMES model generators”, which generally omit detailed transmission expansion.) | TEMOA does not explicitly project energy market prices but instead estimates the system-wide marginal cost of energy under different policy scenarios. | TEMOA is designed for long-term energy planning and can model time horizons extending from present day to 2050 or beyond, depending on user input. | A mix of long-term and aggregated reserves. TEMOA does not model short-term operational reserves but can include constraints for long-term capacity adequacy (e.g., ensuring that sufficient firm capacity is built in a given planning horizon). Users can impose reserve margin constraints at a system-wide level but without the granularity of operational reserve dispatch. | | TEMOA’s model formulation is linear programming-based. It uses linear optimization to minimize costs, similar to MARKAL/TIMES. By default, capacity and operation decisions are continuous variables in a linear program. (The framework is built on Pyomo and can leverage linear, mixed-integer, or even non-linear solvers if configured but its standard energy system formulation is linear). | TEMOA supports explicit uncertainty analysis through multi-stage stochastic optimization. It was designed to facilitate rigorous uncertainty exploration – for example, one can formulate stochastic scenario trees in TEMOA. The developers note that from the start, TEMOA was built to run large numbers of scenarios or include stochastic programming capabilities. In practice, users can run deterministic scenarios or invoke the stochastic module (via PySP) to optimize under uncertainty. | TEMOA allows for policy constraints, such as carbon pricing and emission caps, to be integrated into optimization scenarios | TEMOA supports renewable energy mandates, carbon pricing mechanisms, and emissions constraints, making it useful for evaluating long-term decarbonization pathways | https://github.com/TemoaProject/temoa | |||
28 | TIMES (The Integrated MARKAL-EFOM System) | Long-term energy scenario planning, balancing supply and demand. It is a flexible model generator that can be applied at global, regional, national, or local scales to evaluate energy policies, technology choices, and system evolution. | Minimizes system cost while meeting constraints. It is designed to evaluate the impacts of policies, technology developments, and resource availability on the future configuration and costs of energy systems. | US (National). TIMES is an economic model generator for local, national, multi-regional, or global energy systems. | Government agencies, and energy planners. | Available under IEA-ETSAP. | Yes, TIMES optimizes generation capacity investment decisions endogenously based on least-cost planning. | TIMES allows endogenous investment in storage technologies, considering costs, efficiency, and operational constraints. | TIMES can include regional transmission flows and constraints, depending on model structure and data availability | Yes (if modeled) While not always activated by default, TIMES can be configured to optimize transmission expansion endogenously when inter-regional trade and line costs are defined. | Models energy market equilibrium and costs | Medium to long-term (decades) | Balances supply and demand but limited in reserves | | Varies depending on the tool used | TIMES handles uncertainty primarily through scenario analysis, allowing users to explore different assumptions about technologies, policies, and economic drivers. While it doesn’t include built-in probabilistic modeling, users can test sensitivities by varying input parameters across scenarios. | Yes | Models CO2 and emissions policies | https://iea-etsap.org/index.php/etsaptool/model-generators/markal https://github.com/etsap-TIMES/TIMES_model | |||
29 | US-REGEN (EPRI is owner) | "Links a detailed electric sector capacity planning and fuels supply model with a technologically detailed consumer choice model of end-use service and energy demands" | "The U.S. Regional Economy, Greenhouse Gas, and Energy (US-REGEN) model is an energy-economy model developed and maintained by EPRI’s Energy Systems and Climate Analysis (ESCA) group. The model provides a customizable platform for policy analysis, technology assessment, and strategy that is informed by decades of EPRI research on energy modeling and technology analysis." | US Split into 16 sub regions. Can consider multiple sub regions down to states or even portions of states | Utilities, analysts, policymakers, researchers, and consulting firms. | Open source version for download on Github | Yes | Yes | Yes | Yes | Detailed price projections bases on hsitorical pricing and general good-faith assumptions | Up to 35 years | Capacity Model | "The model is deterministic; that is, it does not include uncertainty, so that a given set of inputs and assumptions will produce the same outputs. This has the advantage of demonstrating the least-cost deployment mix of generation subject to a scenario-dependent range of technological and policy constraints and specified input assumptions. Apart from the impacts of uncertainty and imperfect information, economic theory suggests that it is reasonable to expect a least-cost outcome from a competitive market, which in some respects the wholesale generation of electric power has become." | v2025 | US-REGEN Documentation | |||||||
30 | Wis:DOM (Vibrant Clean Energy is owner) | "The WIS:dom® (Weather-Informed energy Systems: for design, operations and markets) optimization planning model is the state-of-the-art energy model developed by Vibrant Clean Energy, LLC (VCE®)." | "WIS:dom®-P simultaneously co-optimizes the capacity expansion requirements (generation, transmission, and storage) and the dispatch requirements (production cost, power flow, reserves, ramping, and reliability) for the entire electric (energy) grid of interest. ": | Scalable from campuses to countries. Primarily focused on USA and sub regions | Utilities, regulators, and policymakers. | Paid for License | Yes, mutiple consideration for capacity expansion including maximum and minimum installed capacity and ramp-up/ramp-down times. | Yes, robust control of discharge and charging rates to support system capacity and gain profitibilty | Yes, WIS:dom®-P is that it resolves the transmission topology and power flow for the electricity system being modeled | Yes, "The construction of new transmission lines can happen in two ways: the model augments an existing right�of-way (ROW) as well as expanding the substations if required, or the model builds a completely new transmission line and connects two existing substations (and augments them if necessary). The costs are different for performing the two different approaches." | Utilizes user inputs, no clear method for projections | Datasets possible up to 175 years | Computes Planning reserve margins | Capacity Model | Linear is Standard. Mixed-integer and non-linear formulations are available with no increase in accuracy. | Focuses on reduction of uncertaintly in input with various inout models. Does not directly address uncertainly in output in public data. | Renewable Focused | Model focuses on weather contraints and trends more than other models. NOAA High Resoltion Rapud Repfrash weather forcasting and historical data are used to predict renewable energy production. | https://vibrantcleanenergy.com/wp-content/uploads/2020/08/WISdomP-Model_Description(August2020).pdf https://vibrantcleanenergy.com/products/wisdom-p/ | |||
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