An Optimization Strategy for Unit Commitment in High Wind Power Penetration Power Systems Considering Demand Response and Frequency Stability Constraints

Minhui Qian, Jiachen Wang, Dejian Yang (), Hongqiao Yin and Jiansheng Zhang
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Minhui Qian: College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Jiachen Wang: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Dejian Yang: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Hongqiao Yin: School of Electrical Engineering, Southeast University, Nanjing 210003, China
Jiansheng Zhang: College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China

Energies, 2024, vol. 17, issue 22, 1-15

Abstract: To address the issue of accommodating large-scale wind power integration into the grid, a unit commitment model for power systems based on an improved binary particle swarm optimization algorithm is proposed, considering frequency constraints and demand response (DR). First, incentive-based DR and price-based DR are introduced to enhance the flexibility of the demand side. To ensure the system can provide frequency support, the unit commitment model incorporates constraints such as the rate of change of frequency, frequency nadir, steady-state frequency deviation, and fast frequency response. Next, for the unit commitment planning problem, the binary particle swarm optimization algorithm is employed to solve the mixed nonlinear programming model of unit commitment, thus obtaining the minimum operating cost. The results show that after considering DR, the load becomes smoother compared to the scenario without DR participation, the overall level of load power is lower, and the frequency meets the safety constraint requirements. The results indicate that a comparative analysis of unit commitment in power systems under different scenarios verifies that DR can promote rational allocation of electricity load by users, thereby improving the operational flexibility and economic efficiency of the power system. In addition, the frequency variation considering frequency safety constraints has also been significantly improved. The improved binary particle swarm optimization algorithm has promising application prospects in solving the accommodation problem brought by large-scale wind power integration.

Keywords: unit commitment; demand-side response; frequency security constraints; operational flexibility (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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