Optimization of wind turbines siting in a wind farm using genetic algorithm based local search

Abdelsalam, Ali M.; El-Shorbagy, M. A.

doi:10.1016/j.renene.2018.02.083

Cited by 80 publications

(30 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a broader sense, research on ideal distances between turbines in wind farms [19] and the optimisation of wind farm placement [20,21] are also related to the subject of this paper. However, they are only concerned with the classification of a specific sub-step of the presented methodology, namely the selection of possible sites.…”

Section: Introductionmentioning

confidence: 99%

Nature conservation as a driver in wind energy scenarios

2019

View full text Add to dashboard Cite

Background: Germany has set ambitious goals for the reduction of greenhouse gases. The decarbonisation of the energy system has been in focus. An important means to achieve this is the increased utilisation of wind energy. The growth of wind power entails changes not only in the electrical system but also in the landscape and environment. Prospectively, scenarios will have to consider a wide range of aspects, not only economics and technology but also nature conservation and social affairs. The authors are participating in the research study "Szenarien für den Ausbau der erneuerbaren Energien aus Naturschutzsicht", funded by the Federal Agency for Nature Conservation, which examines the possibilities of integrating nature conservation into the development of scenarios. Methods: For aspects of nature conservation to be taken into account in scenario development, a multi-stage methodology has been developed to assess the conflict risk of wind energy and nature conservation throughout Germany. To ensure comparability of the scenarios, all of them are based on the same general framework consisting of fixed excluded areas, the same method of detail allocation and the same overall expected energy output. The "nature conservation" driver is integrated in the form of a nationwide comparative assessment of risk levels. The mapping of spatially differentiated risk levels for wind energy has been achieved in a GIS-based and discursive process. Results: The results show that nature conservation can be addressed properly in scenario-building. Here, the method of multi-criterion scenario-building itself, with its focus on including nature conservation as one of several drivers determining the spatial distribution of wind turbines, is a major result. The authors have developed specific scenarios that mainly address questions of landscape and nature conservation. Out of the four generic scenarios presented for the year 2035, two have nature conservation as their main driver, whereas the other two consider energy-economic drivers only. Examining these scenarios provides insight into the influence of each driver. For example, adding nature conservation as the main driver (highest priority) reduces the specific conflict risk by 26.1%, while at the same time only a relatively small increase in wind turbines is required (+12.5% in numbers, +2.3% in installed power capacity). Conclusion: The methods developed here provide a driver for allocating wind power plants to reduce conflicts in high-risk areas. Furthermore, using the same spatial distribution of risk levels makes it possible to subsequently rate the scenarios from a conservation perspective. The method developed here provides the means to analyse trade-offs between relevant drivers. The "nature conservation" scenarios show a relatively small additional demand for wind turbines but a greater amount of avoided conflict risk.

show abstract

Section: Introductionmentioning

confidence: 99%

Nature conservation as a driver in wind energy scenarios

2019

View full text Add to dashboard Cite

show abstract

“…SOGA is a metaheuristic algorithm and it is performed while mimicking the natural selection process. It searches problems by relying on bio-inspired operators, including mutation, crossover, and selection [37]. The setting of genetic algorithm (GA) parameters that are used for the optimization using different wind farm design methods in this paper are summarized in Table 3.…”

Section: Objective Function Evaluation Methodsmentioning

confidence: 99%

Comparative Study of Wind Turbine Placement Methods for Flat Wind Farm Layout Optimization with Irregular Boundary

Wang

2019

Applied Sciences

View full text Add to dashboard Cite

For the exploitation of wind energy, planning/designing a wind farm plays a crucial role in the development of wind farm project, which must be implemented at an early stage, and has a vast influence on the stages of operation and control for wind farm development. As a step of the wind farm planning/designing, optimizing the wind turbine placements is an effective tool in increasing the power production of a wind farm leading to an increased financial return. In this paper, the optimization of an offshore wind farm with an irregular boundary is carried out to investigate the effectiveness of grid and coordinate wind farm design methods. In the study of the grid method, the effect of grid density on the layout optimization results is explored with 20 × 30 and 40 × 60 grid cells, and the means of coping with the irregular wind farm boundary using different wind farm design methods are developed in this paper. The results show that, depending on the number of installed wind turbines, a power output increase from 1% to 1.5% is achieved by increasing the grid density from 20 × 30 to 40 × 60. However, the computational time is more than doubled, rising from 23 h to 47 h with 40 wind turbines being optimized from the coarse grid cells to the densified grid cells. In comparison, the coordinate method is the best option for achieving the largest power increase of 1.5% to 2% (relative to the coarse 20 × 30 grid method), while the least computational time (21 h with 40 wind turbines optimized) is spent.

show abstract

“…Said equation is expressed as the sum of penalties multiplied by a penalty factor (F p = 1.5e3, found by trial and error), which enables the sum of Penn with f1, thus penalizing the solutions that violate any constraint. Finally, the identification and interpretation of each one of the constraints that define the problem resulted in the set of penalties employed in equation 11, which is composed of equations (12) to (17). Such penalties are associated with each one of the restrictions previously mentioned and defined by the maximum value function, where, after each constraint is analyzed, the function will take the maximum value as a result, i.e.…”

Section: Objective Functionmentioning

confidence: 99%

“…For example, by means of a GA, some authors studied machining optimization through Computer Numerical Control (CNC) and geometry variations [11]. Another study [12] used a GA technique to optimize wind turbines for wind farms in order to find the rotor that would produce most power. Since optimization techniques, especially metaheuristic ones [13], are widely used to find a solution to different nonlinear problems [14], this work proposes the minimization of the weight of a drive shaft with an abrupt change in the cross section.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Transmission Shafts: a Continuous Genetic Algorithm Approach

Rodriguez-Cabal

Grisales-Noreña

Marín

et al. 2019

Stat., optim. inf. comput.

View full text Add to dashboard Cite

This paper presents an analysis of the optimal design of transmission shafts by adopting the approach of a novel continuous genetic algorithm. The optimization case study is formulated as a single-objective optimization problem whose objective function is the minimization of the total weight that results from the sum of all the sections in the shaft. Additionally, mechanical stresses and constructive characteristics are considered constraints in this case. The proposed optimization model corresponds to a nonlinear non-convex optimization problem which is numerically solved with a continuous variant of genetic algorithms. SKYCIV®and Autodesk Inventor®were used to verify the quality and robustness of the numerical results in this paper by means of simulation tools and analysis. The results obtained demonstrates that the methodology proposed reduce the complexity and improving the results obtained in comparison to conventional mechanical design.

show abstract

Optimization of wind turbines siting in a wind farm using genetic algorithm based local search

Cited by 80 publications

References 18 publications

Nature conservation as a driver in wind energy scenarios

Nature conservation as a driver in wind energy scenarios

Comparative Study of Wind Turbine Placement Methods for Flat Wind Farm Layout Optimization with Irregular Boundary

Optimal Design of Transmission Shafts: a Continuous Genetic Algorithm Approach

Contact Info

Product

Resources

About