Multi-objective optimization of clustered wind farms based on potential game approach

Abstract

With the rapid development of offshore wind power, the construction and operation of multiple adjacent wind farms (WFs) in the form of cluster has become a trend. However, the method for single WF planning can not address the WF cluster layout optimization in the complex relationship of competition or cooperation. This paper proposes a novel method to design clustered offshore WFs. The objectives to be optimized include the maximization of WF generated power and the minimization of wind turbine (WT) turbulence intensity. To solve this problem, a mathematical model based on the potential game theory is established. In this planning model, each WF in the cluster is assumed to be an individual stakeholder, the decision variables and the constraints are mapped to be the strategies and the strategy sets of the stakeholders, respectively, and the objectives are set as their payoffs. The non-dominated sorting genetic algorithm II (NSGA-II) is synthesized with the dependency structure matrix genetic algorithm II (DSMGA-II) to tackle the multi-objective optimization problem (MOOP) and the fuzzy-membership function (FMF) method is used to select the final solution from the Pareto optimal set. The best-strategy-response iterative algorithm is put forward to search for the Nash equilibrium (NE) of the game. Case studies on three clustered offshore WFs in Qidong, China, are carried out to validate the effectiveness of the proposed approach. Also, the simulation results indicate that the synthesized algorithm outperforms the NSGA-II in solving this optimization problem.