Electric Power ›› 2025, Vol. 58 ›› Issue (1): 185-195.DOI: 10.11930/j.issn.1004-9649.202404034

• New Energy and Energy Storage • Previous Articles     Next Articles

Capacity Allocation and Operation Optimization Model of Household Photovoltaic-Storage System Based on MPC

Guomin QI1(), Tianye LI1, Hong YU1, Bolun LU1, Baozhong MA1, Wenxin ZHANG2(), Entong WU2, Xianyao XIAO2   

  1. 1. East Inner Mongolia Power Exchange Center Company Ltd., Hohhot 010010, China
    2. School of Economics and Management, North China Electric Power University, Beijing 102206, China
  • Received:2024-04-07 Accepted:2024-07-06 Online:2025-01-23 Published:2025-01-28
  • Supported by:
    This work is supported by Science and Technology Project of State Grid Inner Mongolia East Power Co., Ltd. (Research on Interactive Market Trading Mechanism of Diversified Power Supplies to Match the New Power System, No.526601230006).

Abstract:

Distributed photovoltaic (PV) is crucial to meeting the "double carbon" objective and establishing new power system with new energy sources as the main body. This research examines home PV-storage system construction and operation under the present stepped-peak-valley tariff system. Firstly, the household PV-energy storage system structure and tariff system are introduced. Secondly, the optimal capacity allocation model of PV and storage is established to minimize the investment cost and annual operation and maintenance cost of the household PV-storage system. Then, and a two-layer rolling optimization operation algorithm based on model predictive control is proposed considering the stepped-peak-valley tariff's effect on users' long-term energy use strategy. Among them, the upper model maximizes annual integrated return using a stepwise tariff-based annual rolling optimization, and the lower model is a daily rolling optimization based on a peak-valley tariff with the objective of minimizing daily operating costs. In the lower model, the operation scheme of PV-energy storage is based on the optimization results of the upper model, corrects the system state deviation caused by uncertainty factors. Finally, the algorithm's simulation findings suggest that it can delay high electricity tariff utilization and increase residential customer revenue.

Key words: household photovoltaic-energy storage system, stepped-peak-valley tariff system, capacity allocation, model predictive control, operation optimization