论文标题
微电网最佳能量调度考虑基于神经网络的电池降解
Microgrid Optimal Energy Scheduling Considering Neural Network based Battery Degradation
论文作者
论文摘要
电池储能系统（BES）可以有效地减轻可变生成的不确定性。降解是无法预防的，难以建模，并且可以预测诸如最受欢迎的锂离子电池（LIB）等电池。在本文中，我们提出了一种数据驱动的方法，以预测给定的预定电池操作的蝙蝠降解。特别是，提出了基于神经网络的电池降解（NNBD）模型，以用主要电池降解因子的输入来量化电池降解。当将提出的NNBD模型纳入微电磁模型调度（MDS）时，我们可以建立一个基于电池降解的MDS（BDMDS）模型，该模型可以精确地考虑使用NNBD模型的基于拟议的基于循环的电池电池使用方法（CBUP）方法，以精确考虑电池降解成本。由于所提出的NNBD模型是高度非线性的，而非凸线则很难解决。为了解决这个问题，本文提出了神经网络和优化解耦启发式（NNODH）算法，以有效解决此神经网络嵌入的优化问题。仿真结果表明，所提出的NNODH算法能够以最低的总成本（包括正常的运营成本和电池降解成本）遵守最佳解决方案。
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.
