论文标题
通过基于神经网络的电池降解模型来解决安全受限的单位承诺的另一种方法
An Alternative Method for Solving Security-Constrained Unit Commitment with Neural Network Based Battery Degradation Model
论文作者
论文摘要
电池储能系统（BESS）可以有效地减轻可变生成的不确定性，并提供灵活的辅助服务。但是，降解是可充电电池（例如最广泛使用的锂离子电池）的关键问题。基于神经网络的电池降解（NNBD）模型可以准确量化电池降解。当将NNBD模型纳入安全受限的单元承诺（SCUC）时，我们可以建立基于电池降解的SCUC（BD-SCUC）模型，该模型可以精确地考虑等效的电池降解成本。但是，由于NNBD模型的高非线性，BD-SCUC可能无法直接解决。为了解决此问题，通过将每个神经元的非线性激活函数转换为线性约束，将NNBD模型线性化，这使BD-SCUC能够成为线性化的BD-SCUC（L-BD-SCUC）模型。案例研究表明，可以有效地解决提出的L-BD-SCUC模型，用于多个BESS总线电力系统日前的调度问题，总成本最低，包括等效的退化成本和正常运行成本。
Battery energy storage system (BESS) can effectively mitigate the uncertainty of variable renewable generation and provide flexible ancillary services. However, degradation is a key concern for rechargeable batteries such as the most widely used Lithium-ion battery. A neural network based battery degradation (NNBD) model can accurately quantify the battery degradation. When incorporating the NNBD model into security-constrained unit commitment (SCUC), we can establish a battery degradation based SCUC (BD-SCUC) model that can consider the equivalent battery degradation cost precisely. However, the BD-SCUC may not be solved directly due to high non-linearity of the NNBD model. To address this issue, the NNBD model is linearized by converting the nonlinear activation function at each neuron into linear constraints, which enables BD-SCUC to become a linearized BD-SCUC (L-BD-SCUC) model. Case studies demonstrate the proposed L-BD-SCUC model can be efficiently solved for multiple BESS buses power system day-ahead scheduling problems with the lowest total cost including the equivalent degradation cost and normal operation cost.
