摘要
针对可再生能源发电的不稳定性与电网调度问题,研究首先引进可再生能源发电并网技术,在此基础上设计了智能电网调度系统,并引进多目标粒子群算法处理可再生能源并网调度问题,最后采用遗传算法的变异策略进行优化,得到改进多目标粒子群算法。研究结果表明,由线性微分递减策略与非对称学习因子组成的参数优化策略可明显提升研究方法的计算效率,仅需迭代95次就可找到最优解。在实际应用中,与标准粒子群算法相比,研究方法的可再生能源发电可提升46.37%。上述结果说明研究方法能维持可再生能源供应的稳定性,并保证电网运行的安全性与经济性。
After introducing renewable energy generating grid connection technology,the research created an intelligent grid scheduling system to address the volatility of renewable energy generation and grid scheduling difficulties.The multi-objective particle swarm algorithm was introduced to handle the renewable energy grid connection scheduling problem.Finally,the genetic algorithm mutation strategy was used for optimization,resulting in an improved multi-objective particle swarm algorithm.The research results indicate that the parameter optimization strategy composed of linear differential descent strategy and asymmetric learning factor can significantly improve the computational efficiency of the research method,and only 95 iterations are needed to find the optimal solution.In practical applications,compared with the standard particle swarm algorithm,the research method can improve renewable energy generation by 46.37%.The above results indicate that the research method can maintain the stability of renewable energy supply and ensure the safety and economy of power grid operation.
作者
益西措姆
央宗
刘晓明
谭景明
夏强
YIXicuomu;YANG Zong;LIU Xiaoming;TAN Jingming;XIA Qiang(Nali Power Supply Company,State Grid Tibet Electric Power Co.,Ltd.,Nali,Xizang,China,859000,China;Economic and Technological Research Institute of State Grid Tibet Electric Power Co.,Ltd.,Lhasa,Xizang,China,850000,China)
出处
《自动化与仪器仪表》
2025年第9期294-298,共5页
Automation & Instrumentation
关键词
测控技术
并网技术
可再生能源
电网调度
多目标优化算法
measurement and control technology
grid connected technology
renewable energy
power grid dispatching
multi objective optimization algorithm
