Open Access

ARTICLE

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

Yingchao Li^1,*, Jianbin Wang¹, Haibin Wang²

1 China Electric Power Research Institute, State Grid Inner Mongolia Eastern Electric Power Co., Ltd., Hohhot, 010010, China
2 School of Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

* Corresponding Author: Yingchao Li. Email:

Energy Engineering 2024, 121(4), 1049-1065. https://doi.org/10.32604/ee.2023.045228

Received 21 August 2023; Accepted 14 November 2023; Issue published 26 March 2024

Abstract

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.

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Keywords

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