Solar, Wind, and Storage: Optimizing for Least Cost Configurations of Renewable Energy Generation in the PJM Grid

Abstract

The integration of solar and wind power into the grid poses many challenges due to the intermittent nature of weather conditions. This thesis models the hourly generation, storage, and consumption of solar, offshore wind, onshore wind, and fossil fuel energy such that demand is met every hour. For a given fossil fuel penalty, the least cost renewable energy build-out is determined through the use of a finite-difference stochastic approximation algorithm. The algorithm optimizes over five decision variables: solar power, offshore wind, onshore wind, battery inverter power, and battery storage capacity. The relationship between fossil fuel penalties and energy outcomes is explored for four different scenarios. This thesis finds that as fossil fuel energy costs rise, onshore wind and lithium-titanate grid-level storage become costeffective for meeting demand.