FInal Project for AA 203: Optimal and Learning-Based Control
Abstract:
Energy storage systems (ESSs) are a critical part of the renewable-fueled, sustainable energy grid of the future. ESSs can increase their value by dispatching (charging and discharging) to the grid to provide multiple grid services. This paper presents a framework to non-simultaneously provide two separate grid services of energy arbitrage and peak shaving in real-time using model predictive control (MPC), demonstrating the trade-off between MPC horizon and computational power needed for real-time control.
Video link: https://youtu.be/Q9qfMK28X0g