The objective of this study is to evaluate the effect of wind turbine spacing in large wind farm on the total energy loss of the wind farm, the power loss is due to the wake effect between wind turbines, on a site gathering several wind turbines, if the wind turbines are too close, the loss of power increases with the wake effect. This paper presents an investigation into optimal wind farm layout in 88 wind farm configurations of a hypothetical WF in Tarfaya, to search the optimal number of Wind Turbines (WTs), the wind farm are Installed on an area of 35 km2 (7000m×5000m), with the aim to maximize the electrical power generated by all WTs and grows the annual economic profitability of the WF, in order to approve the result of this investigation a new approach based on the division of the wind farm in the sub domain method is proposed, to search the optimal location of Wind Turbines by mean an RCGA (Real Coded Genetic Algorithm). This new proposed approach is promising in terms of the applicability in large wind farm. It is also more suitable when performing the wind farm layout assessment in WPP (Wind Power Project).
The objective of this paper is to investigate the ability of analytical wake models to estimate the wake effects between wind turbines (WTs). The interaction of multiple wakes reduces the total power output produced by a large offshore wind farm (LOFWF). This power loss is due to the effect of turbine spacing (WTS), if the WTs are too close, the power loss is very significant. Therefore, the optimization of turbine positions within the offshore wind farm requires an understanding of the interaction of wakes inside the wind farm. To better understand the wake effect, the Horns Rev 1 offshore wind farm has been studied with four wake models, Jensen, Larsen, Ishihara, and Frandsen. A comparative study of the wake models has been performed in several situations and configurations, single and multiple wakes are taken into consideration. Results from the Horns Rev1 offshore wind farm case have been evaluated and compared to observational data, and also with the previous studies. The power output of a row of WTs is sensitive to the wind direction. For example, if a row of ten turbines is aligned with the 270° wind direction, the full wake condition of WTs is reached and the power deficit limit predicted by Jensen model exceeds 70%. When a wind direction changes only of 10° (260° and 280°), the deficit limit reduces to 30%. The obtained results show that a significant power deficit occurs when the turbines are arranged in an aligned manner. The findings also showed that all four models gave acceptable predictions of the total power output. The comparison between the calculated and reported power output of Horns Revs 1 showed that the differences ranged from - 8.27 MW (12.49%) to 15.27 MW (23.06%) for the Larsen and Frandsen models, respectively.
This paper investigates optimal placement of wind turbines (WTs) within a large offshore wind farm (LOFWF). 88 wind farm (WF) configurations are invested to search the optimal layout for the Horns rev 1 offshore WF using twenty years of wind data knowing that wind characteristics are modeled from long term reanalysis data based on MERRA-2. The regular and the irregular placement of the Horns Rev 1 (HR1) offshore WF are investigated with Jensen wake model. Therefore, the objective is to assess the effect of wind turbine spacing (WTS) on the power output loss in a LOFWF and, also to find the best configuration that gives the maximum power with minimum investment cost. The use of the Biogeography based optimization (BBO), as a bio-inspired evolutionary approach, represents the advantage of being effective on strongly non-convex spaces such as a large offshore WF. Due to the iterative process applied to the initial population, and the multiplicity of the population, the BBO process limit the risk of getting stuck in a local optimum, by distributing the individuals in the whole solution space. The results obtained show that the wind data extracted from MERRA-2 can be applied reliably to any existing WF to simulate wind power production. The results also demonstrate that significant power losses occur when the turbines are arranged in a condensed manner and the significant power gains are not obtained from too large configurations, but rather from the best placement in configurations. The proposed approach shows promise in terms of applicability with MERRA-2 and is effectively suitable for arranging turbines and assessing wind resources in an offshore wind farm project (OFWFP).
This paper investigates the optimal layout of wind turbines (WTs) within a wind farm. Finding the best placement of WTs in a wind farm is a challenging process due to the existence of multiple wake effects. A biogeography based optimization (BBO) algorithm method is proposed to search for the optimal location of WTs in a wind farm (WF), to maximize the power produced by the WF and improve the annual economic performance of the WF. A wind turbine (WT) that operates in the wake of one or more other turbines is subject to lower flow and therefore produces less power. When designing a wind project, the arrangement of the turbines with each other in a wind farm is a very important factor. The best layout of a wind farm is to achieve the optimal placement of the turbines in relation to each other in a given area to maximize the efficiency of the whole wind farm and reduce its cost. A dense configuration would result in considerable power losses. Each turbine must have a sufficient distance from other turbines in the WF where the optimal number of turbines should be placed. The BBO approach is conducted on a 2 km x 2 km wind farm assuming a constant wind speed of 12 m/s with a fixed wind direction, for solving the wind farm layout optimization problem in two different configurations which include 26 and 30 WTs respectively. A comparison of the results obtained with the previous studies shows that the BBO is more efficient in terms of maximizing power output and economic profitability of the same wind farm model, which validates that BBO performs effectively in optimizing WTs placement within WF. BOO provides the greatest improvement in the optimal layout, for example, in the case of the layout for 30 WTs. The power output reaches 15,383 KW, the agreement between the ideal and the optimal layout is more favorable. The difference in output power is only 169 KW (1%). Knowing that the ideal layout is reached if all WTs receive a wind flow with a maximum wind speed of 12 m/s. Furthermore, a case study of wind turbine layout optimization using the BBO program on the Alta X wind farm has been performed under variable wind speed and variable wind direction. The results indicate that the optimized layout of the Alta X wind farm achieves a 12% increase in the power output for a similar cost when compared to the original layout of Alta X. It is also more appropriate for evaluating the wind farm layout in the Wind Power Project (WPP).
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