2022/07/01 by Cunzhi Zhao, Zhao, Cunzhi, Xingpeng Li +1
Engineering · #Advanced Battery Technologies Research #Electric Vehicles and Infrastructure #FOS: Electrical engineering #Microgrid Control and Optimization #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2207.00650
openalex publication_date 2022/07/01 · openalex created_date 2022/07/15 · openalex updated_date 2026/07/28
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.