Climate change is increasingly impacting power system operations, not only through more frequent extreme weather events but also through shifts in routine weather patterns. Factors such as increased temperatures, droughts, changing wind patterns, and solar irradiance shifts can impact both power system production and transmission and electric load. The current power system was not designed to be resilient towards future climates. In this work, we aim to co-optimize power generation, storage, and transmission expansion in order to develop a climate-resilient system that is able to reliably meet future demands.
We analyze the impact of climate change on power systems via an adaptation of a capacity expansion planning model that seeks to minimize costs while ensuring power system resilience under a changing climate. We model the problem as a stochastic mixed-integer program, which we implement in Pyomo and solve using the stochastic programming library, mpi-sppy, along with Gurobi. We extend a synthetic but realistic, high-resolution, test case for California, the California Test System (CATS), to include parameters required for our capacity expansion planning problem and climate scenarios. Leveraging climate data from the U.S. Department of Energy’s Energy Exascale Earth System Model (E3SM), we map climate projections onto power system parameters, focusing on changes in temperature, wind speed, solar irradiance, and streamflow which affect both load and wind, solar, and hydro generator availability, respectively. We compare investment decisions based on present-day climate with future-climate and find that accounting for future climate scenarios significantly impacts generation, storage, and transmission investment decisions.
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