The importance of temporal resolution in modeling deep decarbonization of the electric power sector

Abstract

Abstract Power sector decarbonization is a central pillar of economy-wide emissions reductions. However, model complexity, especially temporal resolution, can materially impact power sector decarbonization pathways. Using a detailed electric sector capacity planning and dispatch model, this analysis explores impacts of temporal resolution on electric sector investments and costs and how these outcomes vary under different policy and technology assumptions. Results show that approaches to simplify temporal variability used in many integrated assessment and energy system models may not replicate fundamental relationships for power sector decarbonization or may exhibit large quantitative deviations from more detailed modeling, including abatement costs rising nonlinearly at higher decarbonization levels; variable renewables and batteries being accompanied by additional low-/zero-/negative-emissions resources, especially approaching 100% decarbonization; and carbon removal technologies altering electric sector costs and investments. Representative day approaches can preserve many of these properties with large reductions in computational complexity. Simplified temporal aggregation approaches tend to understate the value of broader technological portfolios, firm low-emitting technologies, wind generation, and energy storage resources and can overstate the value of solar generation. Approximation accuracy also depends on assumptions about technological cost and availability: differences across approaches are smaller when carbon removal is available and when renewables costs are lower. The analysis indicates that higher temporal resolution is increasingly important for policy analysis, electric sector planning, and technology valuation in scenarios with deeper decarbonization and higher variable renewables.