Optimization and Scheduling Method for Power Systems Considering Wind Power Forward/Reverse Peaking Scenarios

Hao Yu, Yibo Wang (), Chuang Liu, Shunjiang Wang, Chunyang Hao and Jian Xiong
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Hao Yu: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Yibo Wang: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Chuang Liu: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Shunjiang Wang: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Chunyang Hao: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Jian Xiong: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China

Energies, 2024, vol. 17, issue 5, 1-18

Abstract: With the promotion of the dual carbon target, the scale of the wind power grid connection will significantly increase. However, wind power has characteristics such as randomness and volatility, and its grid connection challenges the pressure of system peak shaving, making it increasingly difficult to regulate the power system. To solve the problem of wind power abandonment, the positive and negative peak shaving characteristics of wind power were first analyzed. Based on this, it is proposed that demand response resources and energy storage with adjustable characteristics are used as the new means of wind power consumption. Together with the thermal power units, they participate in the optimization and scheduling of the power grid, forming a coordinated and optimized operation mode of source load storage. With the goal of minimizing system operating costs, a two-stage economic scheduling model was formed for the day-ahead and intra-day periods. Finally, optimization software was used to solve the problem, and the simulation results showed the effectiveness of the proposed economic scheduling model, which can improve the system’s new energy consumption and reduce the system’s operating costs.

Keywords: demand response; wind power consumption; source-load coordination; energy storage (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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