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Battery energy storage system (BESS) can effectively mitigate the uncertaintyof variable renewable generation and provide flexible ancillary services.However, degradation is a key concern for rechargeable batteries such as themost widely used Lithium-ion battery. A neural network based batterydegradation (NNBD) model can accurately quantify the battery degradation. Whenincorporating the NNBD model into security-constrained unit commitment (SCUC),we can establish a battery degradation based SCUC (BD-SCUC) model that canconsider the equivalent battery degradation cost precisely. However, theBD-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 nonlinearactivation function at each neuron into linear constraints, which enablesBD-SCUC to become a linearized BD-SCUC (L-BD-SCUC) model. Case studiesdemonstrate the proposed L-BD-SCUC model can be efficiently solved for multipleBESS buses power system day-ahead scheduling problems with the lowest totalcost including the equivalent degradation cost and normal operation cost.

An Alternative Method for Solving Security-Constrained Unit Commitment with Neural Network Based Battery Degradation Model