Integrated Reactive Power Optimisation for Power Grids Containing Large-Scale Wind Power Based on Improved HHO Algorithm

• Published in: Sustainability Vol. 15; no. 17; p. 12962
• Main Authors: Zhao, Jie, Zhang, Mingcheng, Zhao, Biao, Du, Xiao, Zhang, Huaixun, Shang, Lei, Wang, Chenhao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023
• Subjects: Air-turbines; Algorithms; Analysis; Energy; Green technology; Growth factors; Hawks; Integer programming; Laws, regulations and rules; Linear programming; Mathematical optimization; Ocean energy resources; Sustainability; Turbines; Wind farms; Wind power; China
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su151712962

Large-scale wind power grid integration will greatly change the system current distribution, making it difficult for the reactive power regulator to adjust to the optimal state. In this paper, an integrated reactive power optimisation method based on the improved Harris Hawk (HHO) algorithm is proposed. Firstly, a reactive power regulation model is constructed to solve the reactive power regulation interval of wind turbines, and the reactive power margin of wind turbines is used to participate in the system’s reactive power optimisation. Finally, a reactive power compensation capacity allocation optimisation model considering nodal voltage deviation, line loss and equipment investment cost, is established, and a reactive power optimisation scheme is obtained using the Harris Hawk optimisation algorithm on the basis of considering the constraints of the wind turbine reactive power output interval. The improved HHO algorithm is used to solve the reactive power optimisation scheme considering the constraints of tidal power, machine end voltage, a conventional generator and wind farm reactive power. In the simulation, the effects of the improved Harris Hawk optimisation algorithm and the particle swarm optimisation algorithm are compared, and the experimental results prove that compared to the particle swarm algorithm, the optimisation result of the improved Harris Hawk optimisation algorithm reduces the average loss of the system by 42.6% and reduces the average voltage deviation by 30.3%, which confirms that the improved Harris Hawk intelligent optimisation algorithm is effective in proving its superiority and solving the multi-objective model for reactive power optimisation.