Process and policy insights from an intercomparison of open electricity system capacity expansion models

Abstract This study performs a detailed intercomparison of four open-source electricity capacity expansion models—Temoa, Switch, GenX, and USENSYS—to evaluate (1) how closely the results of these models align when inputs and configurations are harmonized, and (2) the degree to which varying model configurations affect outputs. We harmonize the inputs to each model using PowerGenome and use clearly defined scenarios (policy conditions) and configurations (model setup choices). This allows us to isolate how differences in model structure affect policy outcomes and investment decisions. Our framework allows each model to be tested on identical assumptions for policy, technology costs, and operational constraints, allowing us to focus on differences that arise from inherent model structures. Key findings highlight that, when harmonized, models produce very similar capacity portfolios under current policies and net-zero scenarios, with less than 1% difference in system costs for most configurations. This agreement among models allows us to focus on how configuration choices affect model results. For instance, configurations with unit commitment constraints or economic retirement yield different investments and system costs compared to simpler configurations. Our findings underscore the importance of aligning input data and transparently defining scenarios and configurations to provide robust policy insights.

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Publication Details

Journal
Environmental Research Energy
Published
2025-06-17
DOI
https://doi.org/10.1088/2753-3751/ade548
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
0.00

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Process and policy insights from an intercomparison of open electricity system capacity expansion models

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article en

Abstract

Abstract This study performs a detailed intercomparison of four open-source electricity capacity expansion models—Temoa, Switch, GenX, and USENSYS—to evaluate (1) how closely the results of these models align when inputs and configurations are harmonized, and (2) the degree to which varying model configurations affect outputs. We harmonize the inputs to each model using PowerGenome and use clearly defined scenarios (policy conditions) and configurations (model setup choices). This allows us to isolate how differences in model structure affect policy outcomes and investment decisions. Our framework allows each model to be tested on identical assumptions for policy, technology costs, and operational constraints, allowing us to focus on differences that arise from inherent model structures. Key findings highlight that, when harmonized, models produce very similar capacity portfolios under current policies and net-zero scenarios, with less than 1% difference in system costs for most configurations. This agreement among models allows us to focus on how configuration choices affect model results. For instance, configurations with unit commitment constraints or economic retirement yield different investments and system costs compared to simpler configurations. Our findings underscore the importance of aligning input data and transparently defining scenarios and configurations to provide robust policy insights.

Environmental Research EnergyVol. 2(4)
Environmental Defense Fund (US), University of Hawaii System (US), Technical University of Nova Scotia (CA), Princeton University (US), University of California San Diego (US), Optimal Solutions (United States) (US), Carnegie Mellon University (US)
Alfred P. Sloan Foundation
Openalex Percentile: Top 88%
Smart Grid Energy Management
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