Optimizing Day-Ahead and Real-Time Dispatch of Grid-Scale Battery Storage Using Linear and Stochastic Programming

Abstract

Power systems in the U.S. are evolving rapidly. For the first time in two decades, electricity demand is growing. At the same time, wind and solar have become the cheapest and fastest-growing sources of new electricity generation. To support the flexibility and reliability of this changing grid, large-scale battery storage systems are being deployed across the country. These batteries store excess renewable energy during the day and discharge it when demand peaks in the evening. Battery operators must make decisions about when to charge and when to discharge the system both a day ahead and in real time. In this thesis, I develop methods for making dispatch (charge/discharge) decisions under uncertainty about grid conditions. I begin by creating a battery optimizer using a linear programming method. I conduct two studies with the optimizer using historical data from electricity markets in the U.S. The first study simulates a battery in Texas from 2015 to 2025. I find that on average it earns over half of its yearly revenue on just 27 high-value days—mostly hot days in the summer. The second study compares how batteries with different durations (the time it takes to discharge at max power) perform in various regions of the U.S. Regions with high price volatility like Texas benefit most from batteries. Systems with significant renewable generation also benefit, particularly from batteries with 3- to 4-hour durations. In the final chapter, I develop a new optimizer that uses a stochastic programming framework to better account for price uncertainty. Using the stochastic programming optimizer, batteries generated 16% higher revenues on average than using the linear programming optimizer. This improvement highlights the importance of giving the battery system flexibility to react to changing grid conditions in real time.