The optimal configuration of turbines location in a wind farm using a Genetic Algorithm
Abstract
The placement of wind turbines is a key technology for wind farm configuration, but the automatic placement of turbines is always still a difficult problem. The objective of every wind farm designer is producing as maximum as possible of energy, with minimal cost of installation The improved wind and turbine models are formulated into an optimal control framework in terms of minimizing the cost per unit energy of the wind farm. In this study, a code Wind Farm Optimization using a Genetic Algorithm (WFOAG) is developed for optimizing the placement of wind turbines in wind farm to minimize the cost per unit power produced from the wind farm. A genetic algorithm is employed for the optimization. WFOAG is validated using the results from previous studies.
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Introduction
Today, the part of production from renewable energy sources has increased dramatically compared to fossil fuels. This is generally due to some factors such as the high and rising price of traditional fossil fuels, during this, great social and environmental concerns and institutional support undertake to reduce foreign fossil fuels.
Many countries have already invested in green energy and they will invest even more because of dwindling resources of fossil fuels, the commitment of the Kyoto Protocol and the obligations for all countries with regard to the protection of the environment. By focusing on the types of renewable energy, it is a well known fact that wind energy has increased the most. That is why the development of an efficient tool for the design and construction of wind farms has a special importance. The design of the wind farm involves several factors. These range from maximum desired installed capacity for the wind farm, site constraints, noise assessment for noise sensitive dwellings, visual impact and the total cost. The fundamental aim, while designing a wind farm, is to maximize the power production while reducing the total costs associated with the wind farm. ‘Micro-siting’ is the process of optimizing the layout of the wind farm. This process is facilitated by the use of wind farm design tools (WFDTs) which are commercially available.
In this work, wind turbine placement in a wind farm is optimized using an objective function that represents the cost per unit power produced by the wind farm for a particular wind distribution function. The wind distribution function, in general, represents a model of wind variations in speed and direction averaged over a year, or many years. A genetic algorithm is employed for optimizing the placement of the wind turbines. An analytical wake model is utilized for modeling wind turbine wakes in the wind farm.
Conclusion
The placement of wind turbines is an initial step in the wind farm design and forms the foundation for the efficient operation of the farm. At present, empirical schemes are commonly adopted. As the wind condition becomes complicated, systematical approaches such as genetic algorithms are needed to reach an optimal or suboptimal design. This paper further previous research on genetic algorithm placement by incorporating more appropriate models of wind speed distributions and turbine power curves. Simulation results indicate that the new genetic-algorithm scheme can improve wind farm performance, which expands more computation due to complicated models. It is of importance to study more realistic situations, for example, to use a more relevant wind turbines probably with pitch control systems, to use a cost model including cabling, to study relatively complex terrain layouts.