Optimal Power Management of Battery Energy Storage Systems via Ensemble Kalman Inversion

TL;DR

This paper introduces a computationally efficient method for real-time power management of battery energy storage systems using ensemble Kalman inversion, reducing complexity and improving speed over traditional optimization methods.

Contribution

The paper proposes a novel approach that transforms the power management problem into a parameter estimation task solved by ensemble Kalman inversion, enabling real-time operation.

Findings

Significantly reduces computational requirements.

Achieves comparable accuracy to traditional methods.

Demonstrates effectiveness through extensive simulations.

Abstract

Optimal power management of battery energy storage systems (BESS) is crucial for their safe and efficient operation. Numerical optimization techniques are frequently utilized to solve the optimal power management problems. However, these techniques often fall short of delivering real-time solutions for large-scale BESS due to their computational complexity. To address this issue, this paper proposes a computationally efficient approach. We introduce a new set of decision variables called power-sharing ratios corresponding to each cell, indicating their allocated power share from the output power demand. We then formulate an optimal power management problem to minimize the system-wide power losses while ensuring compliance with safety, balancing, and power supply-demand match constraints. To efficiently solve this problem, a parameterized control policy is designed and leveraged to transform the optimal power management problem into a parameter estimation problem. We then implement the ensemble Kalman inversion to estimate the optimal parameter set. The proposed approach significantly reduces computational requirements due to 1) the much lower dimensionality of the decision parameters and 2) the estimation treatment of the optimal power management problem. Finally, we conduct extensive simulations to validate the effectiveness of the proposed approach. The results show promise in accuracy and computation time compared with explored numerical optimization techniques.