Optimized Economic Operation Strategy for Distributed Energy Storage With Multi-Profit Mode

• Main Authors: Peng Peng, Yongqi Li, Dinglin Li, Yuda Guan, Ping Yang, Zhenkai Hu, Zhuoli Zhao, Dong Liu
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Distributed energy storage; demand management; demand response; peak-valley spread arbitrage; multi-profit model
• Online Access: https://ieeexplore.ieee.org/document/9306819/

Distributed energy storage (DES) on the user side has two commercial modes including peak load shaving and demand management as main profit modes to gain profits, and the capital recovery generally takes 8-9 years. In order to further improve the return rate on the investment of distributed energy storage, this paper proposes an optimized economic operation strategy of distributed energy storage with multi-profit mode operation. Considering three profit modes of distributed energy storage including demand management, peak-valley spread arbitrage and participating in demand response, a multi-profit model of distributed energy storage is established, and the proposed optimal operation strategy formulates three stages of the energy storage operation, namely month-ahead, day-ahead, and in-day. In the month-ahead optimization stage, the demand charge threshold of the next month is optimized to minimize the electricity cost. In the day-ahead optimization stage, under the constraint of demand charge threshold and with the goal of maximizing returns, the distributed energy storage is controlled to participate in peak-valley spread arbitrage and demand response, and the optimized output curve for the next day is calculated. In the in-day optimization stage, based on the optimized output curve, taking real-time demand response into account, the real-time charge-discharge power of energy storage is adjusted dynamically with the goal of minimizing income loss, thus to realize adaptive adjustment of distributed energy storage and eliminate the risk of income loss. Simulation results of distributed energy storage for typical industrial large users show that the proposed strategy can effectively improve the economic benefits of energy storage.