Background/Objectives
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 generator, storage, and transmission efficiencies, production, and lifetimes. Furthermore, some of these factors also impact electric load. The current power system was not designed to be resilient towards future climates. In this work, we aim to find cost-optimal solutions for expanding and hardening the power system to develop a climate-resilient system that is able to meet future load.
Approach/Activities
We analyze the impact of climate change on power systems via a novel climate-resilient capacity expansion planning model, which seeks to minimize costs while ensuring power system resilience and reliability under a changing climate. We model the problem as a stochastic mixed-integer program, which we implement in Pyomo and solve using mpi-sppy and Gurobi. We apply our model to a case study of California, focusing initially on changes in temperature, wind speed, solar irradiance, and load. Climate data from the CMIP6 model repository are leveraged.
Results/Lessons Learned
The results show that when climate impacts are accounted for, power system expansion primarily occurs through renewable resources with greater installed capacity, primarily in solar resources, when planning for 2045 as compared to the present day. Expansion of wind and solar resources occurs throughout California, whereas expansion of the transmission system is concentrated in a few key areas. The results for the stochastic program, run across 360 representative days, are significantly different from the individual scenario results, demonstrating the importance of solving the model as a stochastic program across a representative set of scenarios.