Design of wind farm layout with non-uniform turbines using fitness difference based BBO

Bansal, Jagdish Chand; Farswan, Pushpa; Nagar, Atulya K.

doi:10.1016/j.engappai.2018.02.007

Cited by 18 publications

(7 citation statements)

References 30 publications

(48 reference statements)

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“…The authors in [43] propose a biogeography based optimization (BBO) algorithm to solve the wind farm layout optimization problem (WFLOP) using wake model. The authors in [44] propose modified BBO algorithm named Fitness Difference Based BBO (FD-BBO) to solve the WFLOP. The authors claim that FD-BBO an efficient and accurate algorithm and prove their claim using graphical and statistical analysis.…”

Section: ) Biologically Inspired Modeling Techniquesmentioning

confidence: 99%

Placement Optimization for Renewable Energy Sources: Ontology, Tools, and Wake Models

et al. 2020

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Due to intermittent nature of renewable energy sources (RESs) and their strong dependence on various environmental factors, it is imperative to carefully deploy these sources. A survey of recent works published in the area of optimal deployment of RESs is presented in this article. The existing works are categorized according to the type of energy source, objective function and model of operation. An optimal mix of hybrid-RESs along with energy storage system (ESS) is presented as solution to overcome the randomness and inconstancy of a single RES such as wind or solar power. We outline mathematical formulations for different objective functions, i.e., minimization of cost, maximization of power generation, maximization of the average cosine efficiency and minimization of the distribution losses. We present different wake models and simulation tools being used for the wind form layout optimization. These simulation tools are used to grade particular RESs according to technical and financial feasibility. This review paper depicts multifaceted coverage of the subject to provide the readers with state of the art developments in the area and can serve as a foundation for further research in the area. In addition, it can be used to find optimal mix of RESs for particular geographical area to satisfy the energy demands of that particular area. INDEX TERMS Renewable energy, placement optimization, positioning, wake models.

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Section: ) Biologically Inspired Modeling Techniquesmentioning

confidence: 99%

Placement Optimization for Renewable Energy Sources: Ontology, Tools, and Wake Models

et al. 2020

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“…In this study, the convergence speed is improved by modifying mutation operator and exploitation is maintained by keeping original migration [30]. In some of the previous studies like [32,33], BBO has proved itself as a good search algorithm and it has effectively solved complex problems with multiple targets and constraints. Thus the Basic BBO has been applied in handling several optimization problems.…”

Section: Review Of Previous Work On Bbo Algorithmmentioning

confidence: 99%

A Hybrid SCA Inspired BBO for Feature Selection Problems

Sindhu

Ngadiran

Yacob

et al. 2019

Mathematical Problems in Engineering

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Recent trend of research is to hybridize two and more metaheuristics algorithms to obtain superior solution in the field of optimization problems. This paper proposes a newly developed wrapper-based feature selection method based on the hybridization of Biogeography Based Optimization (BBO) and Sine Cosine Algorithm (SCA) for handling feature selection problems. The position update mechanism of SCA algorithm is introduced into the BBO algorithm to enhance the diversity among the habitats. In BBO, the mutation operator is got rid of and instead of it, a position update mechanism of SCA algorithm is applied after the migration operator, to enhance the global search ability of Basic BBO. This mechanism tends to produce the highly fit solutions in the upcoming iterations, which results in the improved diversity of habitats. The performance of this Improved BBO (IBBO) algorithm is investigated using fourteen benchmark datasets. Experimental results of IBBO are compared with eight other search algorithms. The results show that IBBO is able to outperform the other algorithms in majority of the datasets. Furthermore, the strength of IBBO is proved through various numerical experiments like statistical analysis, convergence curves, ranking methods, and test functions. The results of the simulation have revealed that IBBO has produced very competitive and promising results, compared to the other search algorithms.

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“…Only limited studies have been carried to assess wind power potential and its offshore application aspects. These include wind energy trend estimation using different techniques (Chaurasiya et al, 2018), wind farm layout design optimization (Bansal et al, 2018), and wind power distribution analysis across different regions (Alluri et al, 2017;Chaurasiya et al, 2019;Indhumathy et al, 2015;Kore & S, 2016;Mohsin & Rao, 2018;Sharma et al, 2019). The large initial cost associated with offshore wind energy technology poses a hindrance to its promotion and development, particularly in developing countries like India.…”

Section: Introductionmentioning

confidence: 99%

Techno-Economic Analysis and Planning for the Development of Large Scale Offshore Wind Farm in India

Riaz

Khan

2020

Int. J. Renew. Energy Dev.

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Despite India's great potential for offshore wind energy development, no offshore wind farm exists in the country. This study aims to plan a large scale offshore wind farm in the south coastal region of India. Seven potential sites were selected for the wind resource assessment study to choose the most suitable site for offshore wind farm development. An optimally matched wind turbine was also selected for each site using the respective power curves and wind speed characteristics. Weibull shape and scale parameters were estimated using WAsP, openwind, maximum likelihood (MLH), and least square regression (LSR) algorithms. The maximum energy-carrying wind speed and the most frequent wind speed were determined using these algorithmic methods. The correlation coefficient (R2) indicated the efficiency of these methods and showed that all four methods represented wind data at all sites accurately; however, openwind was slightly better than MLH, followed by LSR and WAsP methods. The coastal site, Zone-B with RE power 6.2 M152 wind turbine, was found to be the most suitable site for developing an offshore wind farm. Furthermore, the financial analysis that included preventive maintenance cost and carbon emission analysis was also done. Results show that it is feasible to develop a 430 MW wind farm in the region, zone B, by installing seventy RE power 6.2 M152 offshore wind turbines. The proposed wind farm would provide a unit price of Rs. 6.84 per kWh with a payback period of 5.9 years and, therefore, would be substantially profitable.

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Design of wind farm layout with non-uniform turbines using fitness difference based BBO

Cited by 18 publications

References 30 publications

Placement Optimization for Renewable Energy Sources: Ontology, Tools, and Wake Models

Placement Optimization for Renewable Energy Sources: Ontology, Tools, and Wake Models

A Hybrid SCA Inspired BBO for Feature Selection Problems

Techno-Economic Analysis and Planning for the Development of Large Scale Offshore Wind Farm in India

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