Optimizing the number and locations of turbines in a wind farm addressing energy-noise trade-off: A hybrid approach

•Concurrent resolution of turbine number and locations during micro-siting.•Effect of noise on energy-noise multi-objective optimization is demonstrated.•A hybrid algorithm is proposed utilizing probabilistic and deterministic methods.•∼24% improved performance is achieved over the benchmark case st...

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Veröffentlicht in:Energy conversion and management Jg. 132; S. 147 - 160
Hauptverfasser: Mittal, Prateek, Mitra, Kishalay, Kulkarni, Kedar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ltd 15.01.2017
Elsevier Science Ltd
Schlagworte:
algorithms
Alternative energy sources
Classical
Constraints
Decision making
energy
Energy conservation
Evolutionary
Evolutionary algorithms
Multiple objective analysis
Noise
Noise propagation
Optimization
Pareto
Tradeoffs
Turbines
Wind
Wind farms
Wind power
Wind power generation
Wind turbines
Pareto
Wind
Classical
Noise
Evolutionary
Optimization
ISSN:0196-8904, 1879-2227
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Zusammenfassung:•Concurrent resolution of turbine number and locations during micro-siting.•Effect of noise on energy-noise multi-objective optimization is demonstrated.•A hybrid algorithm is proposed utilizing probabilistic and deterministic methods.•∼24% improved performance is achieved over the benchmark case study.•∼29% enhanced efficiency over real-binary genetic algorithm alone can be observed. Micro-siting is an optimal way of placing turbines inside a wind farm while considering various design objectives and constraints. Using a well-established Jensen wake model and ISO-9613-2 noise calculation, this study performs a wind farm layout optimization based on a multi-objective trade-off between minimization of the noise propagation and maximization of the energy generation. A novel hybrid methodology is developed which is a combination of probabilistic real-binary coded multi-objective evolutionary algorithm and a newly proposed deterministic gradient based non-dominated normalized normal constraint method. Based on the Inverted Generational Distance metric, the performance of the proposed method is found to be better than the conventional normalized normal constraint method or the concerned evolutionary method alone. Moreover, in contrast to the previous studies, the generated non-dominated front is capable of providing a trade-off between various alternative energy-noise solutions, along with an additional information about the corresponding turbine numbers and their optimal location coordinates. As a result, the decision maker can choose from different competing wind turbine layouts based on existing noise and other standard regulations.
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ISSN:0196-8904
1879-2227
DOI:10.1016/j.enconman.2016.11.014