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Battery energy storage systems (BESS) play a pivotal role in future powersystems as they contribute to achiev-ing the net-zero carbon emissionobjectives. The BESS systems, predominantly employing lithium-ion batteries,have been exten-sively deployed. The degradation of these batteriessignificantly affects system efficiency. Deep neural networks can accuratelyquantify the battery degradation, however, the model complexity hinders theirapplications in energy scheduling for various power systems at differentscales. To address this issue, this paper pre-sents a novel approach,introducing a linearized sparse neural network-based battery degradation model(SNNBD), specifically tailored to quantify battery degradation based on thescheduled BESS daily operational profiles. By leveraging sparse neuralnetworks, this approach achieves accurate degradation predic-tion whilesubstantially reducing the complexity associated with a dense neural networkmodel. The computational burden of inte-grating battery degradation intoday-ahead energy scheduling is thus substantially alleviated. Case studies,conducted on both microgrids and bulk power grids, demonstrated the efficiencyand suitability of the proposed SNNBD-integrated scheduling model that caneffectively address battery degradation concerns while optimizing day-aheadenergy scheduling operations.

Computational Enhancement for Day-Ahead Energy Scheduling with Sparse Neural Network-based Battery Degradation Model