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A review of 10 × 10 and 20 × 20 grid-type wind turbine placement problems solving by metaheuristics

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Abstract

Wind energy is the most important renewable energy source produced by wind turbines. The optimal placement of wind turbines is a challenging binary optimization problem. Maximizing production capacity and minimizing the number of turbines (accordingly minimizing the installation cost) are the main objectives. Metaheuristic algorithms can solve binary optimization problems with optimal or near-optimal solutions. In the literature, the wind turbine placement problem (WTPP) is solved by metaheuristic algorithms from 1994 to 2020. In this work, a literature review of solving 10 × 10 and 20 × 20 grid-type WTPPs with metaheuristic algorithms is conducted. Forty-six different papers were deeply discussed and presented all the experimental results to shed light on future studies for presenting more robust metaheuristics for solving WTPPs. The key results of this review are precaution against false comparisons; presenting the current experimental results; determining the comparison parameters; and demonstrating the benchmark problem clearly. Future directions were clearly spotlighted for practitioners and researchers and new research topics for potential studies are also presented.

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Data availability

The datasets used and/or analyzed are available on reasonable request.

Abbreviations

AAA:

Artificial algae algorithm

ABC:

Artificial bee colony

AL:

Along with the basic learner phase of teaching–learning-based optimization

BCGA:

Boolean code genetic algorithm

BinAAA:

Binary artificial algae algorithm

BinEHO:

Binary elephant herding optimization

BIWO:

Binary invasive weed optimization

BNCS:

Binary negatively correlated search

BPSO:

Binary particle swarm optimization

BPSO-RANDIW:

Binary particle swarm optimization random inertia weight factor

BPSO-TVAC:

Binary particle swarm optimization time-varying acceleration coefficients

BPSO-TVIW:

Binary particle swarm optimization time-varying inertia weight factor

BRCGA:

Binary real coded genetic algorithm

C1:

First case

C2:

Second case

C3:

Third case

CS:

Cuckoo search

CSWH:

Cuckoo search with heuristic

DE:

Differential evolution

DGA:

Distributed genetic algorithm

DGHS:

Discrete global-best harmony search

DPS:

Definite point selection

EA:

Evolutive algorithm

EHO:

Elephant herding optimization

FR:

Fixed runtime

GA:

Genetic algorithm

GRASP-VNS:

Greedy randomized adaptive search procedure-variable neighborhood search

HBDE:

Hybrid encoding differential evolution

HDGA:

Hybrid distributed genetic algorithm

HHO:

Harris hawks optimization

IWO:

Invasive weed optimization

JayaX:

Jaya-based binary optimization algorithm

LGA:

Lazy greedy algorithm

LGPSO:

Local search integrated gaussian particle swarm optimization

MaxFEs:

Maximum function evaluation numbers

MDE:

Modified differential evolution

MEGA:

Modified elitist genetic algorithm

MILP:

Mixed-integer linear program

MPGA:

Multi-population genetic algorithm

MPSO:

Modified particle swarm optimization

MTPG-Jaya:

Multi-team perturbation-guiding Jaya

N/A:

Not available

NoT:

Number of turbines

NP:

Nonpolynomial

NGHS:

Novel global harmony search

NSGA-II:

Non-dominated sorting genetic algorithm-II

PAL:

Combined PSO and ABC inspired search mechanism along with basic learner phase of teaching–learning-based optimization

PPSO:

Particle swarm optimization with penalty functions

PRGM:

Pseudo-random number generation method

PSO:

Particle swarm optimization

PVS:

Passing vehicle search

QAP-GA-ICPS:

Quadratic assignment problem-genetic algorithm with an initial candidate points selection

QIP:

Quadratic integer program

SA:

Simulated annealing

SAVE:

Space and variable decomposition-based evolutionary

SD:

Space decomposition

SDwR:

Space decomposition algorithm with repair

SGA:

Simple genetic algorithm

SSGA:

Spread sheet genetic algorithm

VBOA:

Viral based optimization algorithm

vRsDwR:

Variable resolution space decomposition with repair

vRsDwR NSGA II:

Variable resolution space decomposition with repair multi-objective non-dominated sorting genetic algorithm

VSD:

Variable and space decomposition method

VSDwR:

Variable and space decomposition method with repair

TLBO:

Teaching–learning-based optimization

TLBOe:

Enhanced teaching–learning-based optimization

WFOG:

Wind farm optimization using a genetic algorithm

WTPP:

Wind turbine placement problem

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Acknowledgements

The authors wish to thank Scientific Research Projects Coordinatorship at Selcuk University and The Scientific and Technological Research Council of Turkey for their institutional supports.

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  1. Department of Computer Engineering, Faculty of Technology, Selçuk University, Konya, Turkey

    Ahmet Cevahir Cinar

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Ahmet Cevahir Cinar: investigation, conceptualization, methodology, data curation, writing—original draft preparation, writing—reviewing and editing, supervision.

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Cinar, A.C. A review of 10 × 10 and 20 × 20 grid-type wind turbine placement problems solving by metaheuristics. Environ Sci Pollut Res 30, 11359–11377 (2023). https://doi.org/10.1007/s11356-022-24738-3

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