标题： 基于集成卡尔曼反演的电池储能系统的最优功率管理 显示英文标题

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

摘要： 电池储能系统（BESS）的最佳功率管理对于其安全和高效运行至关重要。 数值优化技术常用于解决最佳功率管理问题。 然而，由于计算复杂性，这些技术在大规模BESS中往往无法提供实时解决方案。 为了解决这个问题，本文提出了一种计算效率更高的方法。 我们引入了一组新的决策变量，称为每个电池的功率共享比例，表示它们从输出功率需求中分配的功率份额。 然后，我们制定一个最佳功率管理问题，以最小化系统范围内的功率损耗，同时确保符合安全、平衡和供电需求匹配的约束条件。 为了高效解决这个问题，设计并利用了一个参数化的控制策略，将最佳功率管理问题转化为参数估计问题。 然后，我们实现了集成卡尔曼反演来估计最优参数集。 由于1）决策参数的维度大大降低，以及2）对最佳功率管理问题的估计处理，所提出的方法显著降低了计算需求。 最后，我们进行了大量仿真以验证所提方法的有效性。 结果表明，与探索的数值优化技术相比，该方法在精度和计算时间方面表现出色。 显示英文摘要

摘要： 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.