基于改进Pareto最优算法的海上风电场多目标微观选址规划  

Improved Pareto Optimality Based on Multi-Objective Micro-Siting Planning of Offshore Wind Farm

作　　者：谭茂强[1] 黄伟[2] 车文学 闫彬禹 谭任深 

机构地区：[1]中国能源建设集团广东省电力设计研究院,广州市510663 [2]华北电力大学电气与电子工程学院,北京市102206

出　　处：《电力建设》2017年第4期103-111,共9页Electric Power Construction

摘　　要：为了在海上风电场初步规划时可以兼顾考虑风机尾流影响、集电系统的经济性和可靠性,用风机层数表征集电系统可靠性,以风电场尾流折减系数、海缆投资、风机层数为优化目标,建立海上风电场多目标微观选址规划模型,并通过改进Pareto多目标优化算法求解最优解集,最后应用层次分析法进行最终优化决策。以某海上风电场的风资源分布状况为实例,验证了风机尾流、连接海缆及风机层数之间的相互制约关系,在规划时应予以综合考虑,同时证明了模型和方法的可行性,改进Pareto最优算法可以求出均匀分布的备选方案,层次分析法可以根据不同要求选择出对应的最优方案。To take the wind turbine＇s wake effect,the economy and reliability of the collector system into account when starting to plan an offshore wind farm,the reliability of the collector system should be characterized by the number of wind turbine＇s layers. With the wind turbine wake loss coefficient,the cost of submarine cables,and the number of wind turbine＇s layers as the optimization targets,this paper establishes the multi-objective micro-siting planning model of the offshore wind farm,and obtains the optimal solution set through improved Pareto multi-objective optimization algorithm,finally makes the final optimization decision by analytic hierarchy process. Taking the distribution of wind resources in an offshore wind farm as an example,it verifies the mutual restraint relationship between the wind turbine wake loss,the connection submarine cables and the number of wind turbine＇s layers,which should be taken into account in the planning progress. And the feasibility of the model and method is proved,evenly distributed alternative scheme can be obtained by improved Pareto optimization algorithm,analytic hierarchy process can choose the best plan based on the different requirements.

关 键 词：海上风电场 微观选址 PARETO最优 集电系统 

分 类 号：TM614[电气工程—电力系统及自动化]