Process and Policy Insights from an Intercomparison of Open Electricity System Capacity Expansion Models

TL;DR

This study tackles the challenge of divergent results across open-source electricity capacity expansion models by harmonizing inputs with the PowerGenome data tool and systematically testing multiple policy scenarios and model configurations. Using four models (TEMOA, Switch, GenX, USENSYS), it demonstrates that under harmonized inputs the models yield near-identical cost-minimizing outcomes for current policies and net-zero pathways, with residual differences driven mainly by configurations such as unit commitment and economic retirement. Key findings show that a carbon buyout price of $1000/tonne$ is a dominant driver of emissions reductions, while transmission constraints and CCS availability significantly shape technology mix and costs. The paper also provides practical guidelines for conducting intermodel comparisons, including data pipelines and transparent scenario/configuration definitions, to improve robustness and policy relevance for decarbonizing electricity systems.

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.

Figures (13)

• Figure 1: Results from each model under the base configuration of Net-zero and Current Policy scenarios. Subplots show total capacity (top-left) and generation (top-right) of selected resources, total transmission capacity (bottom-left), and annual operational system cost (bottom-right).
• Figure 2: Projected annual emissions within each planning period for the current policies and net-zero emissions scenarios. The color of the lines indicates current policies versus net-zero cases; the shape of the marker indicates CO$_2$ buyout price; dashed lines indicate limits on transmission expansion; and marker color indicates whether CCS is allowed. The base net-zero configuration is indicated by a thicker blue line.
• Figure 3: Tighter constraints on transmission expansion increase both system costs and emissions. Results are identical in all constraint child scenarios prior to 2030. Costs and emissions are annual values for each planning period.
• Figure 4: Trade-offs between expansions of transmission and capacity of CCS, solar, and wind resources in select modeled regions as estimated by GenX for 2050. Results reflect differences in resource capacity and transmission capacity between unconstrained and no (0%) transmission expansion. Transmission constraints increase resource capacity in the purple regions and decrease it in the green regions.
• Figure 5: Effects of allowing or disallowing CCS on total capacity, generation mix, emissions, transmission expansion, and costs under the Net-zero scenario. Capacity and transmission represent existing stock at the end of each planning period. Generation and costs are annual values within each planning period.