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FInal Project for AA 203: Optimal and Learning-Based Control: Real-Time, Multi-Service Operation of Grid-Scale Energy Storage using Model Predictive Control

License: MIT License

Python 100.00%
energy-storage-systems peak-shaving energy-arbitrage optimization gurobi gurobipy control-systems optimal-control

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Real-Time, Multi-Service Operation of Grid-Scale Energy Storage using Model Predictive Control

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.

Plot of cumulative reward with different MPC horizons, compared against a full-foresight optimization

Video link: https://youtu.be/Q9qfMK28X0g

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