Wind Farm Micro-siting Research Articles

Optimization of wind turbine micro-siting for reducing the sensitivity of power generation to wind direction

In the optimization of wind turbine micro-siting of wind farms, the major target is to maximize the total energy yield. But considering from the aspect of the power grid, the sensitivity of wind power generation to varying incoming wind direction is also an essential factor. However, most existing optimization approaches on wind turbine micro-siting are focused on increasing the total power yield only. In this paper, by employing computational fluid dynamics and the virtual particle model for the simulation of turbine wake flow, a sensitivity index is proposed to quantitatively evaluate the variation of power generation under varying wind direction. Typical turbine layouts obtained by existing power optimization approaches are evaluated for stability. Results indicate that regularly arranged turbine layouts are not suitable for stable power production. Based on solutions from the power optimization, a second-stage optimization using Particle Swarm Optimization algorithm is presented. The proposed optimization method adjusts the positions of the turbines locally, aiming at increasing the stability of wind farm power generation without damaging its advantage of high power yield. Case studies on flat terrain and complex terrain both demonstrate the effectiveness of the present local adjustment optimization method.

Automatic Contour-Based Road Network Design for Optimized Wind Farm Micrositing

Constructing the access roads between wind turbines requires a significant cost when a wind farm is built in hills or mountains. An optimized design of road network can substantially reduce construction costs and increase investment returns. In this paper, we consider a challenging problem of the road network design for a wind farm with complex topography. An automatic contour-based model is developed for road network design, and is incorporated into the optimization of wind farm micrositing to maximize investment returns of the wind farm. The directions of access roads are first deduced from a contour tree, the route projections are then designed by a gradient-bounded algorithm, and the most cost-effective road network is finally obtained by the minimum spanning tree algorithm. The topographic speedup, wind resource grid and Park wake model are incorporated to accurately evaluate the power production of the wind farm. Both the profit of wind energy sales and the cost of access roads are combined into a cost function called net present value, which is optimized using genetic algorithms. Simulation results illustrate that the length of access roads is decreased significantly while the locations of wind turbines are optimized. The proposed method simultaneously optimizes the turbine layout and road network of the wind farm, therefore achieving a more practical and profitable wind farm micrositing.

A Novel and Efficient Hybrid Optimization Approach for Wind Farm Micro-siting

Due to increasing penetration of wind energy in the recent times, wind farm owners tend to generate increasing amount of energy out of wind farms. In order to meet targets, many wind farms are operated with a layout of numerous turbines placed close to each other in a limited area leading to greater energy losses due to ‘wake effects’ instead of generating more power. To solve the probl em in the most optimal way, these turbines need to satisfy many other constraints such as topological constraints, minimum allowable capacity factors, inter-turbine distances etc. Existing methods to solve this complex turbine placement problem typically assume knowledge about the total number of turbines to be placed in the farm, which might be unrealistic. This study proposes a novel hybrid optimization methodology, a combination of evolutionary and classical optimization approaches, to simultaneously determine the optimum number of turbines to be placed in a wind farm along with their optimal locations. Application of the proposed method on a representative case study yields 43% higher Annual Energy Production (AEP) than the results found by one of the existing methods.

The lazy greedy algorithm for power optimization of wind turbine positioning on complex terrain

Wind farm micro-siting is to determine the optimal positions of wind turbines within the wind farm, with the target of maximizing total power output or profit. This paper studies the performance of the lazy greedy algorithm on optimization of wind turbine positions above complex terrain. Instead of the traditional linear models, computational fluid dynamics and virtual particle wake flow model are employed in the present study for a more accurate evaluation of wind energy distribution and wind power output of wind farm on complex terrain. The validity of the submodular property used by the lazy greedy algorithm is discussed for the wind farm micro-siting optimization problem. By conducting the numerical tests, results demonstrate that the combination of the lazy greedy algorithm and the virtual particle wake model is effective in optimizing wind turbine positioning on complex terrain, for it produces better solution in less time comparing to the previous bionic method.

Wind Farm Micro-Siting Optimization and Benefits Analysis

This paper puts forward a creative turbine micro-siting scheme breaking the 3D-5D rule, and calculates wake losses, power production and its economic benefits in traditional wind farm micro-siting scheme and creative wind farm micro-siting scheme by evaluating software WEPAS and MWVE. Results show that the micro-siting scheme breaking 3D-5D is more feasible and effective. It not only ensures the safe and stable operation of the wind turbines and technical parameters, but also the power production and the initial investment have been optimized.

Optimization of wind farm micro-siting for complex terrain using greedy algorithm

An optimization approach based on greedy algorithm for optimization of wind farm micro-siting is presented. The key of optimizing wind farm micro-siting is the fast and accurate evaluation of the wake flow interactions of wind turbines. The virtual particle model is employed for wake flow simulation of wind turbines, which makes the present method applicable for non-uniform flow fields on complex terrains. In previous bionic optimization method, within each step of the optimization process, only the power output of the turbine that is being located or relocated is considered. To aim at the overall power output of the wind farm comprehensively, a dependent region technique is introduced to improve the estimation of power output during the optimization procedure. With the technique, the wake flow influences can be reduced more efficiently during the optimization procedure. During the optimization process, the turbine that is being added will avoid being affected other turbines, and avoid affecting other turbine in the meantime. The results from the numerical calculations demonstrate that the present method is effective for wind farm micro-siting on complex terrain, and it produces better solutions in less time than the previous bionic method.

Post Evaluation of Wind Resource Assessment and Micro-Siting

The design energy productions deviate from the actual situation, which are affected by the accuracy of two significant factors - the wind resource assessment and wind farm micro-siting. A running wind farm in northern China was taken as the object in this investigation. The measured data obtained in operation phase and the relevant information in design phase were integrated and a post evaluation of wind resource assessment, micro-siting and generating capacity reduction factors of the wind farm in design phase was provided. The results indicate that the representative year wind regimes of the wind farm in design phase can basically reflect the wind conditions in the wind farm without the consideration of the trends of long-term changes in wind speed; micro-siting project in design phase is superior to that in practical; generating capacity reduction factors, overall on the high side, should be further optimized considering 20-year operation period.

Irregular-shape wind farm micro-siting optimization

Landscape constraints inevitably cause the irregularity of the shape or boundary of a wind farm, which was not fully considered in previous literature. In this paper, a single-boundary constraint model and a novel multi-boundary constraint model incorporated with ray intersection method are developed to quantify the irregular boundary constraint for wind farm micro-siting optimization. In order to obtain high-fidelity wind farm shape information, an edge detection algorithm is employed to extract wind farm contour data from digital maps, and an optimal polygonal approximation algorithm is applied to compress the contour data so as to make the computation of boundary constraints less time-consuming. Simulations of four commercial wind farms comprehensively demonstrate the effectiveness of the proposed boundary constraint models and the significance of irregular-shape wind farm micro-siting optimization.

OFFSHORE WIND FARM MACRO AND MICRO SITING PROTOCOL APPLICATION TO RHODE ISLAND

Since 2008, the Rhode Island (RI) Coastal Resources Management Council has been leading the development of an Ocean Special Area Management Plan (Ocean SAMP), in partnership with the University of Rhode Island, resulting in an extensive multidisciplinary analysis of the Rhode Island offshore environment and its suitability to site offshore wind farms. As part of SAMP, a comprehensive macro-siting optimization tool: the Wind Farm Siting Index (WIFSI), integrating technical, societal, and ecological constraints, was developed within the conceptual framework of ecosystem services. WIFSI uses multivariate statistical analyses (principal component and k-means cluster analyses) to define homogeneous regions, which integrate and balance ecological and societal constraints as part of a Cost/Benefit tool. More recently, a Wind Farm micro-Siting Optimization Tool was developed (WIFSO), which uses a genetic algorithm to derive the optimal layout of a wind farm sited within one of the macro-siting selected regions. In this work, we present an overview of the current state of development of the integrated macro- and micro- siting tools.

Wind farm micro-siting by Gaussian particle swarm optimization with local search strategy

The micro-siting of wind farms has recently attracted much attention due to the booming development of wind energy. The paper aims to maximize the electrical power extracted from a wind farm while satisfying the required distance between turbines for operation safety. The micro-siting problem is by nature a constrained optimization problem, in which the coupling of wake effects is strong and the number of position constraints between turbines is large. An improved Gaussian particle swarm optimization algorithm is proposed to optimize the positions of turbines in the continuous space. To prevent the premature of the algorithm, a local search strategy based on differential evolution is incorporated to search around the promising region achieved by the particle swarm optimization. A simple feasibility-based method is employed to compare the performance of different schemes. Comprehensive simulation results demonstrate that the micro-siting schemes obtained by the proposed algorithm increase the power generation of the wind farm. Moreover, the execution time of the algorithm is significantly reduced, which is important especially for large-scale wind farms.

Wind resource estimation and wind park design in El-Kef region, Tunisia

The aim of this study is to investigate the prospects of wind energy development in the El-Kef governorate, a north-western region of Tunisia. This work falls within the framework of a 15 MW wind farm project, undertaken by the local cement factory in order to cover a part of its energy needs. The collected data were analyzed and processed using the Weibull statistical method. The results of these analyses are used to estimate the wind power potential in the region and select the best fitted wind turbines to the site characteristics. Then, a wind farm micro-siting is proposed using the Windstation and 3DEM softwares. Finally, annual energy production of the wind farm was calculated and the project economic feasibility was evaluated. The economic analysis is based on the life-cycle costing methodology and it aims to estimate wind projects viability and profitability in the region.

Wake flow model of wind turbine using particle simulation

A particle model for calculating turbine wake flow during the optimization of wind farm micro-siting is presented. This model treats the wake flow as virtual particles generated by the turbine rotor. Based on the pre-calculated flow field of empty wind farm, the motions of the particles are simulated. The wake flow effect of velocity decrement is obtained from the density of the particles. On flat terrain, the proposed particle model fits the experimental data better than the previous linear model. On complex terrain, since the particle simulation is based on numerical result of flow field, the particle model can be coupled with optimization algorithm of generating turbine layout.

State of the Art and Trends in Wind Resource Assessment

Given the significant rise of the utilization of wind energy the accurate assessment of the wind potential is becoming increasingly important. Direct applications of wind assessment techniques include the creation of wind maps on a local scale (typically 5 20 km) and the micrositing of wind turbines, the estimation of vertical wind speed variations, prospecting on a regional scale (>100 km), estimation of the long-term wind resource at a given site, and forecasting. The measurement of wind speed and direction still widely relies on cup anemometers, though sonic anemometers are becoming increasingly popular. Moreover, remote sensing by Doppler techniques using the backscattering of either sonic beams (SODAR) or light (LIDAR) allowing for vertical profiling well beyond hub height are quickly moving into the mainstream. Local wind maps are based on the predicted modification of the regional wind flow pattern by the local atmospheric boundary layer which in turn depends on both topographic and roughness features and the measured wind rose obtained from one or several measurement towers within the boundaries of the planned development site. Initial models were based on linearized versions of the Navier-Stokes equations, whereas more recently full CFD models have been applied to wind farm micrositing. Linear models tend to perform well for terrain slopes lower than about 25% and have the advantage of short execution times. Long-term performance is frequently estimated from correlations with nearby reference stations with concurrent information and continuous time series over a period of at least 10 years. Simple methods consider only point-to-point linear correlations; more advanced methods like multiple regression techniques and methods based on the theory of distributions will be discussed. Both for early prospecting in regions where only scarce or unreliable reference information is available, wind flow modeling on a larger scale (mesoscale) is becoming increasingly popular.

Algorithms for Offshore Wind Farm Layout Optimization

Offshore wind energy is positioned to facilitate substantial growth in wind energy production, but further reductions in the cost of energy will strengthen its ability to compete directly with other energy generating technologies. One simple solution is the optimal use of current technologies. To this end, this study investigates the use of optimization algorithms for offshore wind farm micrositing. First, a discussion is given of five different types of optimization algorithms: gradient search, heuristic, pattern search, simulated annealing, and evolutionary algorithms. The relevance of each algorithm to wind turbine micrositing is then evaluated by considering two separate objectives: minimization of the levelized production cost and maximization of the energy production. The genetic and greedy heuristic algorithms are further evaluated through the use of design simulations. Finally, these algorithms are employed to optimize the layout of a potential, real-world offshore wind farm near Hull, Massachusetts.