Renewable Energy Sources Scheduling Approach for Windfarm Layout Optimization by Using Ant Lion Optimization Algorithm

Abstract

The increasing penetration of RES and the intermittent nature of various distributed power generation (DG) resources have created uncertainty in variable power production and power systems. The overall energy output of a wind farm may be optimized by strategically positioning wind turbines. This paper proposes a three-step strategy to dealing with the difficult-to-control problem of wind farm layout optimization. To construct the non-wake and wake impacts at various levels, three case scenarios are studied. The proposed strategy is used to a particular Ant-Lion Optimization Algorithm (ALOA) as a novel approach to producing highly efficient optimal output power, as well as case studies using actual wind data assessing potential turbine site. Finally, the simulation results demonstrate that the suggested approach is robust in ALOA design because it further reduces the objective function on the best new outcomes to implement all network restrictions acquired via the analysis.

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.…”

“…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).…”