The Application of Water Cycle Optimization Algorithm for Optimal Placement of Wind Turbines in Wind Farms

Abstract

Wind farms (WFs) include an enormous number of wind turbines (WTs) in order to achieve high capacity. The interaction among WTs reduces the extracted amount of wind energy because wind speed decreases in the wake region. The optimal placement of WTs within a WF is therefore vital for achieving high performance. This permits as many WTs as possible to be installed inside a narrow region. In this work, the water cycle algorithm (WCA), a recently developed optimizer, was employed to identify the optimal distribution of WTs. Minimization of the total cost per kilowatt was the objective of the optimization process. Two different cases were considered: the first assumed constant wind speed with variable wind direction, while the second applied variable wind speed with variable wind direction. The results obtained through the WCA optimizer were compared with other algorithms, namely, salp swarm algorithm (SSA), satin bowerbird optimization (SBO), grey wolf optimizer (GWO), and differential evolution (DE), as well as other reported works. WCA gave the best solution compared to other reported and programmed algorithms, thus confirming the reliability and validity of WCA in optimally configuring turbines in a wind farm for both the studied cases.

Enhanced Chaotic Manta Ray Foraging Algorithm for Function Optimization and Optimal Wind Farm Layout Problem

“…The CMRFO5 achieved a power generation of 18337KW with 86.28% efficiency for a cost equal to 0.0015306, while MRFO produced a total power of 17880KW and 86.23% efficiency for a cost equal to 0.0015375. Along with these findings, it is observed that the cost per unit power is improved compared to those reported in the literature and the re-implemented approaches and has been decreased by 11.92% for Mosetti et al [2], 2.19% for Grady et al [4], 2.18% for Pookpunt et al [14], 0.10% for Taleb et al [30], 1% for Kalyan et al [31], 0.23% for Biswas et al [38], 0.46% for Hegazy et al [43], and 1.95%, 2.48%, 1.5% for AOA, SCA, and SSA, respectively. It is worth noting that the results are compared for unfixed WTs.…”

“…[30], Kalyan et al [31], Biswas et al [38], Hegazy et al [43], AOA, SCA, and SSA, respectively. According to the studies mentioned above, Mosetti reported higher efficiency of up to 94.65% with a lower total power output of 13588KW for 15 turbines.…”

“…The main objectives of all these studies are focused on the enhancement of the power output in wind farm layouts. In this regard, more computation intelligence approaches have been improved and introduced to solve this problem such as: evolutionary algorithm (EA) [19], monte carlo simulation [20], greedy algorithm [21], simulated annealing (SA) [22], sequential convex programming [23], random search algorithm (RSA) [24], [25], [26], [27], multi-Objective random search algorithm (MORSA) [28], ant colony (AC) [29], ant lion optimization (ALO) [30], sparrow search algorithm (SSA) [31], single-objective hybrid optimizer (SOHO) [32], binary invasive weed optimization (BIWO) [33], [34], differential evo-lution(DE) [35], Jaya algorithm [36], integer programming [37], success history based adaptive differential evolution (L-SHADE) [38], cuckoo search (CS) [39], [40], biogeographybased optimization (BBO) [41], multi-team perturbationguiding jaya (MTPG-Jaya) [42], water cycle optimization (WCO) [43], dynastic optimization algorithm (DOA) [44], binary most valuable player algorithm (BMVPA) [45], adaptive neuro-fuzzy inference system (ANFIS) [46], extended pattern search algorithm (EPS) [47]. In this present study, the optimal wind turbine layout was for the first time performed based on a modified new inspired evolutionary algorithm recently developed in 2020 by Zhao et al [48]; named manta ray foraging optimization (MRFO).…”

Optimal Energy Management Integrating Plug in Hybrid Vehicle Under Load and Renewable Uncertainties

“…WCA is one of the promising heuristic optimization techniques especially in power system area. WCA has been introduced to solve optimal microgrids integration considering emission cost, reliability and loss minimization [11][12][13][14]. Gravitational Search Algorithm is another heuristic-based optimization technique that has been implemented for scheduling, sizing and citing of DGs [15][16][17][18][19][20].…”

An Ensemble Framework to Investigate Wind Energy Sustainability Considering Climate Change Impacts

“…It was shown that innovative solutions dealing with turbines, such as a lubrication system for hollow roller bearings, can lead to increased energy efficiency and system output [17,18]. Different algorithms have been employed to find the optimal layout of wind farm and placement of wind turbines for different regions [19,20]. Furthermore, impacts of climate change on wind energy resources have been evaluated using different GCMs and also regional climate models for different areas all over the world.…”