基于神经网络的考虑电池退化的微网能量优化调度
Microgrid Optimal Energy Scheduling Considering Neural Network based Battery Degradation
摘要：
电池储能系统（BESS）可以有效缓解可再生能源发电量变化的不确定性。电池的退化是无法预防的，并且很难建模和预测，例如最流行的锂离子电池（LiB）。在本文中，我们提出了一种数据驱动的方法来预测电池退化每一个给定的预定电池操作配置文件。特别地，提出了一种基于神经网络的电池退化（NNBD）模型来量化电池退化与主要电池退化因素的输入。将NNBD模型与微电网日前调度（MDS）相结合，利用提出的基于电池使用周期的处理（CBUP）方法，可以建立一种能够精确考虑等效电池退化成本的基于电池退化的MDS（BDMDS）模型。由于提出的NNBD模型是高度非线性和非凸的，BDMDS将非常难以求解。针对这一问题，本文提出了一种神经网络优化解耦启发式算法（NNODH），有效地解决了这一神经网络嵌入式优化问题。仿真结果表明，所提出的NNODH算法能够获得包括正常运行成本和电池退化成本在内的总成本最低的最优解.
英文摘要：
Battery energy storage system (BESS) can effec-tively mitigate the uncertainty of variable renewable generation. Degradation is unpreventable and hard to model and predict for batteries such as the most popular Lithium-ion battery (LiB). In this paper, we propose a data driven method to predict the bat-tery degradation per a given scheduled battery operational pro-file. Particularly, a neural network based battery degradation (NNBD) model is proposed to quantify the battery degradation with inputs of major battery degradation factors. When incorpo-rating the proposed NNBD model into microgrid day-ahead scheduling (MDS), we can establish a battery degradation based MDS (BDMDS) model that can consider the equivalent battery degradation cost precisely with the proposed cycle based battery usage processing (CBUP) method for the NNBD model. Since the proposed NNBD model is highly non-linear and non-convex, BDMDS would be very hard to solve. To address this issue, a neural network and optimization decoupled heuristic (NNODH) algorithm is proposed in this paper to effectively solve this neural network embedded optimization problem. Simulation results demonstrate that the proposed NNODH algorithm is able to ob-tain the optimal solution with lowest total cost including normal operation cost and battery degradation cost.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
