Multi-objective wind farm layout optimization using evolutionary computations

Abstract

<p class="Abstract">The usage of fossil fuels is actually not good for living nature and in future, this limited source of energy will vanish. Therefore, we need to go with the clean and renewable source of energy such as wind power, solar energy etc. In this paper, we are concentrating in wind power through optimizing the wind turbine placement in wind farm. The area-of-convex hull, maximize ‘output power’ and minimum spanning tree distance are our main objective topics, due to their effect in wind farm design. An implementation of modified version of the wind turbine (WT) placement model is uses to estimate the yields of the (wind farm) WF layouts and for simplifying the behavior of wind field, in this paper we uses a simple wake approach. Moreover, to resolve the multi-objective problem here we proposed (Modified Genetic Algorithm) MGA, which is considerably better than the (Genetic Algorithm) GA and for evaluate the performance of MGA we use the multi-objective (EA) evolutionary algorithms such as; Genetic algorithm (GA) and SPEA2 and, produce different number of WT layouts. These methodologies are consider with various ‘problematic specific operators’ that are present in this paper.</p>

A bi‐level programming model and differential evolution for optimizing offshore wind farm layout

“…Due to the complexity of models in both echelons, the random key genetic algorithm and the PSO algorithm are separately applied to obtain the optimal solutions in the first and second echelons. Moreover, the convex hull area, the maximized output power, and the minimum spanning tree distance are the objective functions in the study carried out by Shekar et al, 9 and an improved genetic algorithm is proposed to solve the multi‐objective problem. Experimental results show that the proposed algorithm performs better than other algorithms in solving three scenarios.…”