Incorporating Extreme Weather Risks into Energy System Modeling

Abstract:

Electric grid planning is supported by energy system optimization models which project future power plant installations in order to meet demand at the lowest possible cost. To date, these models have generally not incorporated the costs of damage to the electric power grid resulting from extreme weather events such as wind or flooding damage from hurricanes or fires caused by drought. In this talk, we present an extended energy system optimization model that incorporates hurricane risks and demonstrate its utility in the context of Puerto Rico, an island territory of the United States that had its energy system crippled by Hurricane Maria, which passed over the capital San Juan in 2017. The modeling framework uses stochastic optimization to minimize electricity costs across a scenario tree representing possible combinations of storms twenty-five years into the future. The scenario tree is based on historical hurricane data and projected increases in storm intensity due to climate change. Hurricane wind speeds are related to infrastructure damage using fragility curves. We use the model to assess the potential to change grid architecture, fuel mix, and grid hardening measures considering hurricane impacts as well as climate mitigation policies. We modeled both centralized and distributed grid architectures as well as the option to harden the grid. When hurricane trends are included, 2040 electricity cost projections increase by 19% based on historical hurricane frequencies and by 50% for increased hurricane frequencies resulting from climate change. Transitioning to an electric grid powered by a blend of natural gas and renewables reduces emissions and costs appreciably independent of climate mitigation policies. The framework presented here can be adapted to other types of extreme weather, enabling energy planners to explicitly consider extreme weather risks before making large infrastructure investments.