Wind Farm Clustering Optimization Method Using Gap Statistic

2020 ◽  
Author(s):  
Li Peng ◽  
Hang Su ◽  
Hongying Peng ◽  
Xiaomin Qiao ◽  
Pan Wu ◽  
...  
Keyword(s):  
Wind Farm ◽  
Optimization Method ◽  
Gap Statistic ◽  
Clustering Optimization

scholarly journals An Optimization Method for the Configuration of Inter Array Cables for Floating Offshore Wind Farm

2017 ◽  
Author(s):  
Yann Poirette ◽  
Martin Guiton ◽  
Guillaume Huwart ◽  
Delphine Sinoquet ◽  
Jean Marc Leroy
Keyword(s):  
Wind Farm ◽  
Optimization Problem ◽  
Initial Point ◽  
Trust Region ◽  
Optimization Method ◽  
Global Optimum ◽  
Offshore Wind ◽  
Finite Element Software ◽  
Constrained Problems ◽  
Floating Offshore Wind Turbines

IFP Energies nouvelles (IFPEN) is involved for many years in various projects for the development of floating offshore wind turbines. The commercial deployment of such technologies is planned for 2020. The present paper proposes a methodology for the numerical optimization of the inter array cable configuration. To illustrate the potential of such an optimization, results are presented for a case study with a specific floating foundation concept [1]. The optimization study performed aims to define the least expensive configuration satisfying mechanical constraints under extreme environmental conditions. The parameters to be optimized are the total length, the armoring, the stiffener geometry and the buoyancy modules. The insulated electrical conductors and overall sheath are not concerned by this optimization. The simulations are carried out using DeepLines™, a Finite Element software dedicated to simulate offshore floating structures in their marine environment. The optimization problem is solved using an IFPEN in-house tool, which integrates a state of the art derivative-free trust region optimization method extended to nonlinear constrained problems. The latter functionality is essential for this type of optimization problem where nonlinear constraints are introduced such as maximum tension, no compression, maximum curvature and elongation, and the aero-hydrodynamic simulation solver does not provide any gradient information. The optimization tool is able to find various local feasible extrema thanks to a multi-start approach, which leads to several solutions of the cable configuration. The sensitivity to the choice of the initial point is demonstrated, illustrating the complexity of the feasible domain and the resulting difficulty in finding the global optimum configuration.

scholarly journals Minimizing Energy Loss by Coupling Optimization of Connection Topology and Cable Cross-Section in Offshore Wind Farm

2019 ◽  
Vol 9 (18) ◽  
pp. 3722
Author(s):  
Wang ◽  
Wu ◽  
Han ◽  
Wang
Keyword(s):  
Power Systems ◽  
Power System ◽  
Energy Loss ◽  
Cross Section ◽  
Wind Farm ◽  
Optimization Method ◽  
Offshore Wind ◽  
Optimal Topology ◽  
Offshore Wind Farm ◽  
Resistance Level

The cable cross section of an offshore wind farm power system is conventionally determined on the basis of the maximum current carrying capacity. However, this criterion cannot be matched and optimized with connection topology, which may lead to a large overall resistance level in the topology, thereby causing severe energy loss. In this pursuit, the present work envisages the establishment of a coupling optimization method of connection topology and cable cross-section planning for the first time. Based on this method, the power system of a small discrete wind farm is optimized and its results are compared with the results of the traditional design methods. The results indicate that an optimal matching of connection topology and cable cross section can be achieved using the proposed method. Besides, the optimal topology obtained uses more branches, and the large cross-section cables are reasonably used on the large-current cable segments, thus dramatically reducing the energy loss and minimizing the total cost of the power system. The proposed method is very versatile and suitable for the optimization of power systems containing any number of wind turbines and substations. Moreover, it can be combined with any evolutionary algorithm.

Maximizing Energy Output of a Wind Farm Using Teaching–Learning-Based Optimization

2015 ◽  
Author(s):  
Jaydeep Patel ◽  
Vimal Savsani ◽  
Rajesh Patel
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Optimization Method ◽  
Energy Output ◽  
Wake Effect ◽  
Effective Form ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Variable Wind

World is facing a big problems for fossil fuel as it deals with the issues like availability, environmental effect like global warming etc, which forces us to explore new renewable sources of energy like solar, tidal, geothermal, wind etc. Among all the energies wind energy is the effective form of energy. As evaluated from the research, main cause for reduction of energy output in wind farm is the positioning of the wind turbine, as it is a function of wake loss. Present paper investigates an effective meta-heuristics optimization method known as Teaching–Learning-Based Optimization (TLBO), to optimize the positioning of the wind turbine in a wind farm. Two different scenarios of wind speed and its direction distribution across the wind site is considered like, (a) uniform wind speed of 12 m/s with uniform direction and (b) uniform wind speed of 12 m/s with variable wind direction. The results show that the implementation of TLBO is effective then other existing strategy, in terms of maximized expected power output and minimum wake effect of turbines by each other.

scholarly journals Energy Storage Sizing Optimization and Sensitivity Analysis Based on Wind Power Forecast Error Compensation

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4755 ◽  
Author(s):  
Xiaodong Yu ◽  
Xia Dong ◽  
Shaopeng Pang ◽  
Luanai Zhou ◽  
Hongzhi Zang
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Error Compensation ◽  
Wind Farm ◽  
Forecast Error ◽  
Wind Farms ◽  
Optimization Method ◽  
Future Market ◽  
Penalty Cost ◽  
Wind Power Forecast

To better track the planned output (forecast output), energy storage systems (ESS) are used by wind farms to compensate the forecast error of wind power and reduce the uncertainty of wind power output. When the error compensation degree is the same, the compensation interval is not unique, different compensation intervals need different ESS sizing. This paper focused on finding the optimal compensation interval not only satisfied the error compensation degree but also obtained the max profit of the wind farm. First, a mathematical model was proposed as well as a corresponding optimization method aiming at maximizing the profit of the wind farm. Second, the effect of the influencing factors (compensation degree, electricity price, ESS cost, and wind penalty cost) on the optimal result was fully analyzed and deeply discussed. Through the analysis, the complex relationship between the factors and the optimal results was found. Finally, the comparison between the proposed and traditional method was given, and the simulation results showed that the proposed method can provide a powerful decision-making basis for ESS planning in current and future market.

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