Decarbonized energy system planning with high-resolution spatial representation of renewables lowers cost
Liying Qiu, Rahman Khorramfar, Saurabh Amin, Michael F. Howland
Decarbonized energy systems will heavily rely on wind, solar, and storage to satisfy time-varying energy demand, yet optimal siting of variable renewable energy (vRE) remains challenging for designing resource-adequate, low-cost power systems. This study performs km-resolution, spatially explicit energy system optimizations to understand how to harness vRE resource complementarity through high-resolution representation of vRE to reduce supply-demand mismatch in the system, especially on the sub-daily scale. The optimized siting is significantly impacted by the resolution used to generate meteorological inputs. Using downscaled meteorological data at km-scale yields lower cost compared with typical meteorological data at resolutions over 30 km, underscoring the value of high-resolution weather and climate data in planning energy systems. In tandem with km-scale meteorological input, spatially explicit energy modeling at 4–6 km for wind and 14–50 km for solar is required to achieve less than 0.5% error in cost estimation across New England (ISONE), Texas (ERCOT), and California (CAISO).
Qiu, Liying, Rahman Khorramfar, Saurabh Amin, and Michael F. Howland. “Decarbonized Energy System Planning with High-Resolution Spatial Representation of Renewables Lowers Cost.” Cell Reports Sustainability, December 6, 2024. https://doi.org/10.1016/j.crsus.2024.100263