Optimal design of wind farm layout using a biogeographical based optimization algorithm

Pouraltafi-kheljan, Soheil; Azimi, Amir Reza; Mohammadi‐Ivatloo, Behnam; Rasouli, Mohammad

doi:10.1016/j.jclepro.2018.07.217

Cited by 21 publications

(4 citation statements)

References 28 publications

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“…Hanieh Mohammadi et al [58] assessed the voltage stability by using multi objective BBO. Soheil Pouraltafi et al [59] designed layout of wind farm using BBO. Shankar Thawkar and Ranjana Ingolikar [60] tried to diagnosis the problem of breast cancer using BBO.…”

Section: Where: T=current Iteration T=maximum Iterationmentioning

confidence: 99%

Impact of Divergence in BBO on Efficient Energy Strategy of Demand Side Management

Sharma

Saxena²,

Palwalia

2022

Technol Econ Smart Grids Sustain Energy

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Demand Response (DR) keeps up an important character in the growth of electricity generation with transmission and distribution in the phase of advanced grid automation. For future smart grid, basically two types of Demand Side Management strategies have been used with a number of shiftable or controllable devices. In this research article, two new variants of Biogeography Based Optimization (BBO) algorithm named as Iteration Control BBO and Pandemic BBO are proposed for solving the minimization problem and definitions of these variants have been made by change in migration control � ⃗ a vector. These two variants are indirectly support the principle of No Free Lunch theorem. The beauty of these variants is that they are reported the impact of divergence in BBO on efficient energy strategy of DSM. The simulation results reported in this paper claims that the used DSM techniques acquire financial savings, while lowering and shifting peak load.

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Section: Where: T=current Iteration T=maximum Iterationmentioning

confidence: 99%

Impact of Divergence in BBO on Efficient Energy Strategy of Demand Side Management

Sharma

Saxena²,

Palwalia

2022

Technol Econ Smart Grids Sustain Energy

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“…To test the effectiveness of the proposed boundary constraint model, simulation of four commercial wind farms are performed, which verifies the significance of irregular-shaped wind farm micro-siting optimization problem. 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.…”

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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“…Due to the vast searching area of the layout optimization problem, a stochastic optimization algorithm (genetic algorithm, GA) is employed for an effective and efficient search of the solution domains. Following the pioneer work, a large number of publications emerged which are mainly focused on the inclusion of realistic wind farm optimization models, 8,9 optimization algorithms, 10,11 wind farm design methods 12,13 to the studies. Among which, Shorbagy 14 proposed a mixed genetic algorithm with local search technique, while 15 proposed a new optimization method to test the effectiveness of different approaches on the layout optimization of the ideal 2 km × 2 km wind farm.…”

Section: Introductionmentioning

confidence: 99%

Increase a real wind farm productivity through optimizing wind turbines layout

Wang

Zhang

Chen

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part A:

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In this paper, a 3D wake model considering both axial and radial wind speed variations along the incoming wind direction, and the non-flat topography effect on the wind flow, is developed for the layout optimization of a real onshore wind farm. The effectiveness of two methods (i.e., the discretization method that divides the wind scenario into small intervals and the Monte Carlo method which randomly generates the discrete wind speed samples), for evaluating the overall wind farm power production under the wind speed variation of Weibull distribution is investigated for the comparative study. It is found that the Monte Carlo power evaluation method achieves a better outcome than the traditional discretization method, with more total power output and less computational cost. Through the layout optimization with a total of 15 wind turbines installed, the individual wind turbine yields more than 533 kW power out of 536 kW theoretical wake-free power. As the installed wind turbine number increases, the maximum discrepancy of the actual wind turbine power output with respect to the theoretical power increases from 3 kW (with 15 wind turbines) to 6 kW (with 20 wind turbines). Through the comparative study of different wind rotation scenarios, it is found that the maximum discrepancy of individual actual wind turbine power outputs is 8 kW and 13 kW for a rotational angle of [Formula: see text] and [Formula: see text], respectively. Under the scenario of [Formula: see text] wind rotation scenario, the maximum individual power output deficit with 20 installed wind turbines is around 20 kW and the discrepancy of the total power output by comparing to the baseline wind model is maximally 1% proving the robustness of the optimization results with respect to the wind scenario variation.

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Optimal design of wind farm layout using a biogeographical based optimization algorithm

Cited by 21 publications

References 28 publications

Impact of Divergence in BBO on Efficient Energy Strategy of Demand Side Management

Impact of Divergence in BBO on Efficient Energy Strategy of Demand Side Management

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

Increase a real wind farm productivity through optimizing wind turbines layout

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