Abstract

In high renewables-integrated power systems, irrespective to their sizes, energy storage is commonly included and utilized to mitigate fluctuations from both the load and renewable power generation, ensuring system reliability, among which battery energy storage system (BESS) are experiencing fast-growth in recent years. The BESS systems, predominantly employing lithi-um-ion batteries, have been extensively deployed. The degrada-tion of these batteries significantly affects system efficiency. Deep neural networks can accurately quantify the battery degrada-tion; however, the model complexity hinders their applications in energy scheduling for various power systems at different scales. To address this issue, this paper presents a novel approach, in-troducing a linearized sparse neural network-based battery deg-radation model (SNNBD), specifically tailored to quantify bat-tery degradation based on the scheduled BESS operational pro-files. This approach achieves accurate degradation prediction while substantially reducing the complexity associated with a dense neural network model. The computational burden of day-ahead energy scheduling when integrating battery degradation can thus be substantially alleviated. Case studies, conducted on both small-scale microgrids and large-scale bulk power grids, demonstrated the efficiency and suitability of the proposed opti-mal energy scheduling model that can effectively address battery degradation concerns while optimizing day-ahead energy sched-uling operations.

Index Terms

Battery degradation modeling, Bulk power grids, Day-ahead scheduling, Energy management, Machine learning, Microgrids, Optimization, Sparse neural network

Cite this paper:

Cunzhi Zhao and Xingpeng Li, “Optimal Power Scheduling for High Renewables-Integrated Energy Systems with Battery Storage”, IEEE Transactions on Sustainable Energy, Dec. 2025.