Optimal Power Management for Large-Scale Battery Energy Storage Systems via Bayesian Inference
Amir Farakhor, Iman Askari, Di Wu, Yebin Wang, Huazhen Fang
TL;DR
This work tackles optimal power management (OPM) for large-scale BESS under uncertain output demand $P_{out}(t)$. It introduces power-sharing ratios (PSR) and casts the NMPC-based OPM as a Bayesian inference problem solved by ensemble Kalman inversion (EnKI), achieving substantial computational savings. The approach reduces the decision space from $n$ cell-level variables to a 3-parameter vector $\boldsymbol{\theta}\in\mathbb{R}^3$, and couples a high-level NMPC with a low-level PI controller to ensure real-time operation and power balance. Validation includes simulations on up to $n=200$ cells and experiments on a 20-cell prototype, showing computation-time reductions exceeding 90% and robust performance under demand uncertainty. The framework enables scalable, uncertainty-aware OPM for grid-scale BESS with practical, hardware-in-the-loop applicability.
Abstract
Large-scale battery energy storage systems (BESS) have found ever-increasing use across industry and society to accelerate clean energy transition and improve energy supply reliability and resilience. However, their optimal power management poses significant challenges: the underlying high-dimensional nonlinear nonconvex optimization lacks computational tractability in real-world implementation, and the uncertainty of the exogenous power demand makes exact optimization difficult. This paper presents a new solution framework to address these bottlenecks. The solution pivots on introducing power-sharing ratios to specify each cell's power quota from the output power demand. To find the optimal power-sharing ratios, we formulate a nonlinear model predictive control (NMPC) problem to achieve power-loss-minimizing BESS operation while complying with safety, cell balancing, and power supply-demand constraints. We then propose a parameterized control policy for the power-sharing ratios, which utilizes only three parameters, to reduce the computational demand in solving the NMPC problem. This policy parameterization allows us to translate the NMPC problem into a Bayesian inference problem for the sake of 1) computational tractability, and 2) overcoming the nonconvexity of the optimization problem. We leverage the ensemble Kalman inversion technique to solve the parameter estimation problem. Concurrently, a low-level control loop is developed to seamlessly integrate our proposed approach with the BESS to ensure practical implementation. This low-level controller receives the optimal power-sharing ratios, generates output power references for the cells, and maintains a balance between power supply and demand despite uncertainty in output power. We conduct extensive simulations and experiments on a 20-cell prototype to validate the proposed approach.