Wind Farm Layout Optimization Considering Obstacles Using a Binary Most Valuable Player Algorithm

Makbul A M Ramli,Houssem R E H Bouchekara

doi:10.1109/access.2020.3009046

Abstract

Wind farms are developed and implemented in many places around the globe. Designing a wind farm is becoming more and more complex especially with the recent trend towards large farms. Finding the optimal locations of wind turbines inside a wind farm to reduce energy cost is a highly challenging task, as it requires the handling of conflicting criteria and depending on the number of turbines considered it can turn to a large scale-optimization problem. Therefore, the aim of this paper is to place efficiently wind turbines inside a given area considering all constraints. This problem formulated as an optimization problem is referred to as the wind farm layout optimization (WFLO) problem. This real-world problem is nonlinear and difficult to solve using classical optimization algorithms and it has to take into consideration wind scenarios, power curve and wake effects. For this purpose, a binary version of the most valuable player algorithm (MVPA) called BMVPA is developed and implemented. Furthermore, ten scenarios were investigated using different wind speeds, terrain sizes with and without obstacles. For the same terrain but including obstacles, it was found that the energy cost increased due to the presence of obstacles that could limit the search space and consequently reduces the number of available options. The empirical results obtained using BMVPA were compared with those obtained using other well-known algorithms like the binary particle swarm optimization and genetic algorithm. BMVPA showed better results in solving the WFLO problem than the comparative algorithms. The optimum design of the wind farm obtained will allow an efficient and economic exploitation of wind resource.

Highlights

The development of wind energy technology has made considerable progress, marked by the continuous reduction of costs year on year
The Global World Energy Council (GWEC) reported that in 2017, in a tender in Mexico, the prices came down to USD 0.02/kWh, which was lower than the 2016 price of USD 0.03/kWh in a Moroccan tender
The associate editor coordinating the review of this manuscript and approving it for publication was Bijoy Chand Chand Chatterjee. These facts clearly show that wind power will continue to grow, and more markets are expected to realize it in the future

Summary

INTRODUCTION

The development of wind energy technology has made considerable progress, marked by the continuous reduction of costs year on year. The global optimization approach was performed to obtain the maximum power production with the minimum wake effects They investigated an offshore wind farm layout configuration in Germany and found that an increase in the annual power production can be obtained using the proposed method. Three multi-objective evolutionary algorithms were successfully managed as pre-defined low-level metaheuristics to optimize the optimal locations of wind turbines Results showed that this approach is found to be an optimal solution for a difficult WFLO problem. Solving the WFLO problem for different scenarios and terrain sizes and with or without obstacles When a wind turbine is affected by the wake of multiple wind turbines, the total velocity deficit Vel_defi is given as follows: N

POWER MODEL

BINARY MOST VALUABLE PLAYER ALGORITHM

IMPLEMENTATION OF BMVPA TO SOLVE THE WFLO PROBLEM

CONCLUSION