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

TL;DR

This paper introduces a linearized neural network-based battery degradation model integrated into a security-constrained unit commitment framework, enabling efficient and cost-effective scheduling of battery energy storage systems considering degradation.

Contribution

It proposes a novel linearization method for the neural network battery degradation model, allowing its integration into SCUC for improved scheduling accuracy and efficiency.

Findings

L-BD-SCUC effectively minimizes total operational and degradation costs.

The linearization enables solving complex models efficiently.

Case studies validate the model's practical applicability.

Abstract

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.