GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

With the increasing demand for electrical services,wind farm layout optimization has been one of the biggest challenges that we have to deal with.Despite the promising performance of the heuristic algorithm on the route network design problem,the expressive capability and search performance of the algorithm on multi-objective problems remain unexplored.In this paper,the wind farm layout optimization problem is defined.Then,a multi-objective algorithm based on Graph Neural Network(GNN)and Variable Neighborhood Search(VNS)algorithm is proposed.GNN provides the basis representations for the following search algorithm so that the expressiveness and search accuracy of the algorithm can be improved.The multi-objective VNS algorithm is put forward by combining it with the multi-objective optimization algorithm to solve the problem with multiple objectives.The proposed algorithm is applied to the 18-node simulation example to evaluate the feasibility and practicality of the developed optimization strategy.The experiment on the simulation example shows that the proposed algorithm yields a reduction of 6.1% in Point of Common Coupling(PCC)over the current state-of-the-art algorithm,which means that the proposed algorithm designs a layout that improves the quality of the power supply by 6.1%at the same cost.The ablation experiments show that the proposed algorithm improves the power quality by more than 8.6% and 7.8% compared to both the original VNS algorithm and the multi-objective VNS algorithm.

A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization

Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream.Wind farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind farm.Nevertheless,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion efficiency.Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios.Thus,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO problems.CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms.The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and robustness.To be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local.It improves the solution quality.The parameter and search pattern of chaotic local search are also analyzed for WFLO problems.

Optimal Placement of Wind Turbines in Wind Farm Layout Using Particle Swarm Optimization

An optimal geographical location of wind turbines can ensure the optimum total energy output of a wind farm.This study introduces a new solution to the optimization of wind farm layout(WFLO)problem based on a three-step strategy and particle swarm optimization as the main method.The proposed strategy is applied to a certain WFLO to generate highly efficient optimal output power.Three case scenarios are considered to formulate the non-wake and wake effects at various levels.The required wind turbine positions within the wind farm are determined by the particle swarm optimization method.The rule of thumb,which determines the wind turbine spacing,is thoroughly considered.The MATLAB simulation results verify the proposed three-step strategy.Moreover,the results are compared with those of existing research works,and it shows that the proposed optimization strategy yields a better solution in terms of total output power generation and efficiency with a minimized objective function.The efficiencies of the three case studies considered herein increase by 0.65%,1.95%,and 1.74%,respectively.Finally,the simulation results indicate that the proposed method is robust in WFLO design because it further minimizes the objective function.

Bi-objective Layout Optimization for Multiple Wind Farms Considering Sequential Fluctuation of Wind Power Using Uniform Design

The fluctuation of wind power brings great challenges to the secure,stable,and cost-efficient operation of the power system.Because of the time-correlation of wind speed and the wake effect of wind turbines,the layout of wind farm has a significant impact on the wind power sequential fluctuation.In order to reduce the fluctuation of wind power and improve the operation security with lower operating cost,a bi-objective layout optimization model for multiple wind farms considering the sequential fluctuation of wind power is proposed in this paper.The goal is to determine the optimal installed capacity of wind farms and the location of wind turbines.The proposed model maximizes the energy production and minimizes the fluctuation of wind power simultaneously.To improve the accuracy of wind speed estimation and hence the power calculation,the timeshifting of wind speed between the wind tower and turbines’locations is also considered.A uniform design based two-stage genetic algorithm is developed for the solution of the proposed model.Case studies demonstrate the effectiveness of this proposed model.