Optimal Bidding Strategy of Battery Storage in Power Markets Considering Performance-Based Regulation and Battery Cycle Life

TLDR

Large‑scale battery storage is expected to become essential to the smart grid, yet while performance‑based regulation can boost profitability through fast ramping, frequent charge–discharge cycles risk reducing battery life. This study seeks to determine an optimal bidding strategy that balances profitability with battery life preservation. The authors embed a battery cycle‑life model into a profit‑maximization framework for day‑ahead energy, spinning reserve, and regulation markets, and propose a decomposed online calculation method to efficiently evaluate cycle life under varying operational strategies. The model enables investors to optimize bidding and operational schedules while accurately assessing economic viability, as confirmed by case‑study results.

Abstract

Large-scale battery storage will become an essential part of the future smart grid. This paper investigates the optimal bidding strategy for battery storage in power markets. Battery storage could increase its profitability by providing fast regulation service under a performance-based regulation mechanism, which better exploits a battery’s fast ramping capability. However, battery life might be decreased by frequent charge–discharge cycling, especially when providing fast regulation service. It is profitable for battery storage to extend its service life by limiting its operational strategy to some degree. Thus, we incorporate a battery cycle life model into a profit maximization model to determine the optimal bids in day-ahead energy, spinning reserve, and regulation markets. Then a decomposed online calculation method to compute cycle life under different operational strategies is proposed to reduce the complexity of the model. This novel bidding model would help investor-owned battery storages better decide their bidding and operational schedules and investors to estimate the battery storage’s economic viability. The validity of the proposed model is proven by case study results.