Location Of Wind Turbines Research Articles

Determining the size and location of variable speed wind turbines for reducing power losses and improving voltage profile

Due to the voltage level and current of feeders in distribution systems, power loss is high in such networks. With the proliferation of distributed generations, it has become more important to determine their location and rating. These goals can be obtained according to different purposes such as improving the voltage profile. Unlike fixed speed wind turbines, the variable speed type can produce or absorb the reactive power. Hence, this paper specifies the placement and sizing of variable speed wind turbines based on a multi-objective function and according to the reactive power management. In the first step, the variable speed wind turbine is modeled by using the practical curve and Levenberg–Marquardt algorithm and then, the objective functions are optimized simultaneously to achieve the locations and sizes. Also, the impact of wind speed uncertainty on the functions is evaluated. To validate the results, a comparison between variable speed and fixed speed wind turbines is also presented. The proposed model is implemented on an IEEE 33-bus standard test system and the results show a significant (42%) decrease in power loss by adding variable speed wind turbines. However, the investment cost is also increased. Moreover, with considering the uncertainty, power loss and investment cost are increased by about 8% and 15%, respectively. However, the robustness of the system is enhanced against the wind speed fluctuations.

Investigation of Black-Starting and Islanding Capabilities of a Battery Energy Storage System Supplying a Microgrid Consisting of Wind Turbines, Impedance- and Motor-Loads

Microgrids are small scale electrical power systems that comprise distributed energy resources (DER), loads, and storage devices. The integration of DER into the electrical power system basically allows the clustering of small parts of the main grid into Microgrids. Due to the increasing amount of renewable energy, which is integrated into the main grid, high power fluctuations are expected to become common in the next years. This carries the risk of blackouts to be also more likely in the future. Microgrids hold the potential of increasing reliability of supply, since they are capable of providing a backup supply during a blackout of the main grid. This paper investigates the black-starting and islanding capabilities of a battery energy storage system (BESS) in order to provide a possible backup supply for a small part of the main grid. Based on field tests in a real Microgrid, the backup supply of a residential medium voltage grid is tested. Whereas local wind turbines within this grid section are integrated into this Microgrid during the field test, the supply of households is reproduced by artificial loads consisting of impedance- and motor loads, since a supply of real households carries a high risk of safety issues and open questions regarding legal responsibility. To operate other DER during the island operation of such a Microgrid, control mechanisms have to ensure the power capabilities and energy reserves of the BESS to be respected. Since the operation during a backup supply of such a Microgrid requires a simple implementation, this paper presents a simple master–slave control approach, which influences the power output of other DER based on frequency characteristics without the need for further communication. Besides the operation of other DER, the capability to handle load changes during island operation while ensuring acceptable power quality is crucial for such a Microgrid. With the help of artificial loads, significant load changes of the residential grid section are reproduced and their influence on power quality is investigated during the field tests. Besides these load changes, the implementation and behavior of the master–slave control approach presented in this paper is tested. To prepare these field tests, simulations in Matlab/Simulink are performed to select appropriate sizes for the artificial loads and to estimate the expected behavior during the field tests. The field tests prove that a backup supply of a grid section during a blackout of the main grid by a BESS is possible. By creating the possibility of operating other DER during this backup supply, based on the master–slave control approach presented in this paper, the maximum duration for this backup supply can be increased.

Identifying areas of wetland and wind turbine overlap in the south-central Great Plains of North America

The North American Great Plains contains > 80,000 wetlands (playas) that are habitat resources for birds. This region has also undergone recent expansion of wind energy development. Areas of overlap between playas and wind energy structures are potential wildlife hazards. We identified areas where conservation values of playas may be compromised by proximity to wind turbines. We mapped where playas co-occur with wind-energy structures in a 902,765 km2 area encompassing portions of six U.S. states. At a smaller focal level (59 ~458 km2 Christmas Bird Count [CBC] circles), we developed a habitat quality index based on playa area, density, and inundation frequency, and spatially associated values with locations of wind turbines; we then examined CBC data for three focal species from 1984-2015. Over 38% of playas were within 8 km of at least one structure, and 1.3% were within 100 m; ~90% of turbines/structures were within 8 km of a playa (2.7% within 100 m). Six CBC circles had high-quality habitat overlapping with high density of wind turbines. Another seven were high quality with few structures, representing valuable areas for future conservation efforts. However, lack of consistent time-series data constrained our ability to detect effects of wind-energy infrastructure installation on birds. Mapping high-quality habitat areas coincident with wind turbines is a way of identifying overlapping areas of wildlife habitat and sources of mortality without relying on population surveys that may be problematic or lacking altogether. Establishment of innovative protocols will be critical for detecting effects of wind-energy installation, a growing economic sector.

Using waste heat of high capacity wind turbines in a novel combined heating, cooling, and power system

In this paper, a new combined cooling, heating and power system was proposed to investigate the utilization of the waste heat of 12 high capacity wind turbines, each has a capacity of 7500 kW. This system includes a modified organic Rankine cycle consisting of a regenerator and feedwater heater, an absorption refrigeration cycle, and heat exchangers. It is investigated theoretically to assess power, cooling, and hot water production. There is a large number of wind data during a year in the location of these high capacity wind turbines (Noordoostpolder; Netherlands); therefore, to follow a logical procedure for each month and reduce calculations, the hourly average wind speed of 2 days of each season are selected. For the 15th day of the first and last months of each season, the hourly average wind speeds at 12 p.m. are considered. Energy analysis was performed for all chosen hourly average wind speeds; however, to reduce exergy calculations, the average wind speed of 12.25 m/s was selected. At this average wind speed, results indicated that the hourly produced net power, hot water at 65 °C, and cooling at −10 °C are 196.2 kWh, 35.5 m3/h, and 147.7 kWh, respectively. The highest exergy destruction rate belongs to the heat exchanger applied for recovering waste heat from high capacity wind turbine and equals to 74.82% of the total exergy destruction rate. The energy and exergy efficiencies of the proposed system were obtained as 45.32% and 38.61%, respectively. Finally, a simple economic analysis was carried out, accordingly, the payback period was calculated as 1.9 years and it resulted that the proposed system is cost-efficient.

A data-driven evolutionary algorithm for wind farm layout optimization

The wind farm layout model is to optimize the location of wind turbines to maximize the power output of the wind farm. Due to the complexity of the wind farm layout problem, the computation of objective function costs lots of time. To reduce the high computational cost while maintaining the solution performance, a data-driven evolutionary algorithm is proposed. An adaptive differential evolution algorithm (ADE) is proposed as the solver of the wind farm layout model. The adaption mechanism of ADE benefits the automatic adjustment of parameters in the mutation and crossover operators to achieve the optimal solution. The general regression neural network (GRNN) algorithm builds the data-driven surrogate model. The data-driven surrogate model is trained and updated using the data generated by the evolutionary algorithm throughout the evolution process. Through the data-driven surrogate model, the objective function is fast approximated and the bad candidate solutions are identified. The algorithm efficiency is greatly improved by fast filtering the bad candidate solutions. The ADE-GRNN is compared to other three conventional optimization methods based on two different wind scenarios. The results show the super-performance of ADE-GRNN in complex situations in terms of power output and execution time.

Hybrid neurofuzzy wind power forecast and wind turbine location for embedded generation

Hybrid neurofuzzy wind power forecast and wind turbine location for embedded generation

Cable Connection Optimization for Onshore Wind Farms Considering Restricted Area and Topography

For onshore wind farms where all wind turbine (WT) locations are fixed, it is necessary to design corresponding cable connections to collect electricity generated by each WT. The cable connection system should ensure the collection of electricity successfully and reduce the cable cost optimally. Cables are prohibited from crossing restricted land areas, such as reserves, oil wells, and rocky areas. Therefore, in this article, a restricted area is added to the planning area of onshore wind farms. To obtain more accurate final optimized results, we consider topographic factors of onshore wind farms in the optimization process. The algorithm used for cable connection optimization is the dynamic minimum spanning tree. Results indicate the nonnegligible effects of the restricted area and topographic factors on the cable connection topology. The total cable cost savings can exceed 1.8% compared with the reference cable connection topology, in which the topographic factors and restricted area are not considered.

Wind-hydrogen storage in distribution network expansion planning considering investment deferral and uncertainty

With respect to the recent developments of hydrogen storage system (HSS), it is relevant to model these storage units in the network expansion planning. Also, most of the available expansion planning tools consider constant locations and sizing for renewable resources and only study the impacts of renewables on the model. It seems that considering variable location and capacity for renewable energies and finding their optimal levels may result in more flexible model. With regard to these issues, this paper presents distribution network expansion planning incorporating wind power and hydrogen storage. The optimal site and size of wind and hydrogen systems are denoted. The stochastic optimization programming is addressed to minimize the plan budgets. The purpose is to defer the investment and operating budgets. The uncertainty modeling is developed to handle the load-wind errors. The achievements demonstrate that the model finds optimal location, sizing, operation pattern, and setting for wind turbines and HSSs while the planning cost is deferred and minimized.

Assessing the spatial distribution of avian collision risks at wind turbine structures in Brandenburg, Germany

AbstractThe risk of collision with wind turbines remains a critical issue for bird conservation. Undoubtedly, for the continued development of wind farms to increase the energy capacity, wind farm locations must be carefully chosen going forward. This can be achieved not only by avoiding areas with higher bird densities but also by avoiding installations at sensitive distances from their ecologically important land‐use types. Through analyses of the Euclidean distances to the different land‐use types, we utilized the random forest (RF) machine learning algorithm to model the distance‐based impacts of wind turbine locations on detected bird collisions for the frequently‐hit groups of birds at WTs. Although, the predicted areas with potential collision risk in total had a small but highly dispersed expanse of ~2,130 km2 across the vast 29,479 km2 area of the federal state. Our results further segregated these assessed areas based on their different probabilities of collision thresholds (between 0 and 1) to only detect the areas with collision probabilities <.05, which were interpreted as the actual “no risk areas”. These “no risk areas” summed to a total of merely 754 km2 of the land space in Brandenburg, suggesting that any further planned additions of wind energy farms in the state that is, the proposed wind turbines, to be suitably positioned only in these safer areas. Additionally, the study also enabled the identification of any existing wind turbines already installed in the remaining less safe 28,725 km2 area of the state. These areas are also essential to be include in the collision detection surveys and bird population dynamic studies. This would further our understanding regarding the deleterious consequences of collisions at the population levels of birds, eventually helping in the formulation of adequate mitigation measures.

Hazards of wind turbines on avifauna - a preliminary appraisal within the Indian context

Wind farms are substantial sources of renewable energy in India; however, their spread across the country potentially present new hazards to local and migratory birds. This study explored the risk of electrocution and collision of birds with wind turbines close to eco-sensitive zones in India, including Bakkhali, a UNESCO World Heritage site. Geographic information system and remote sensing technology were used. The results indicate vulnerability of local bird species such as barn owl, Indian Scops Owl, Blue Rock Pigeon, Asian Koel, House Crow, Common Sandpiper, Common Snipe, Ruddy Shelduck, Lesser Whistling Duck, Cattle Egret, Great Egret, and Pond Herons, as well as migratory species such as Bar-headed Goose, Red-crested Pochard, and American Black Duck. Modification of wind turbine design and location were considered determinant factors to reduce risk of bird collisions.

Defining the degree of flood hazard using a hydrodynamic approach, a case study: Wind turbines field at west of Suez Gulf

There is a big shortage of drinking water in the western Suez Gulf in Egypt. Fortunately, wind energy is available to cope with the required power for desalination. In previous works the area was subjected to Multi-Criteria Analysis process using a DEM implemented into a GIS environment. For each watershed, a Weighted Standardized Risk Factor (WSRF) was deduced based on the correlation factor for all the main morphological parameters which were obtained from the “HEC-HMS” model results. Former thought of defining the safe and unsafe watersheds based on global scale and dependent only on (WSRF), is nowadays criticized as a watershed with high value for (WSRF) could have safe specific areas for the wind turbines’ establishment. A 2D hydrodynamic model was applied on a Macro Scale to deduce inundation maps then were overlaid with wind speeds maps to define the most appropriate locations for wind turbines.

Pronghorn Winter Resource Selection Before and After Wind Energy Development in South-Central Wyoming

Few studies have evaluated the response of ungulate populations to wind energy development. Recent demand for wind-generated electricity coupled with a tendency for wind-energy facilities to be sited within suitable pronghorn (Antilocapra americana) winter range make this a critical issue for conservation of this icon of western North America. We evaluated pronghorn response to wind energy development at the winter home range scale, as well as within individual winter home ranges using data collected from 47 adult female pronghorn equipped with Global Positioning System transmitters. At both scales, we developed separate resource selection models for pronghorn before (winter 2010) and after (winters 2011 and 2012) development of the Dunlap Ranch wind energy facility in south-central Wyoming to evaluate the potential impacts of wind energy infrastructure on pronghorn winter resource selection. In general, pronghorn winter resource selection was correlated with greater sagebrush (Artemisia spp.) cover, lower snow depth, and lower slopes before and after wind energy development at both scales. At the larger scale, pronghorn selected home ranges closer to wind turbines during all winters. Within home ranges, pronghorn selected areas closer to future locations of wind turbines at Dunlap Ranch during 2010 before turbine erection. However, we found evidence that pronghorn avoided wind turbines in winters after development within their winter home ranges. This relationship was most evident during winter 2011, which coincided with the most severe winter of our study. Long-term replicated studies will be necessary to make inferences for pronghorn populations exposed to wind energy development in different environments and scales than we evaluated. Nonetheless, in the absence of additional information on how ungulates respond to wind energy development, our finding that pronghorn avoided wind turbines within their winter home ranges has important implications for future wind development projects, particularly in areas known to fulfill important seasonal requirements of pronghorn populations.

Digitalization of scanning lidar measurement campaign planning

Abstract. By using multiple wind measurements when designing wind farms, it is possible to decrease the uncertainty of wind farm energy assessments since the extrapolation distance between measurements and wind turbine locations is reduced. A WindScanner system consisting of two synchronized scanning lidars potentially represents a cost-effective solution for multipoint measurements, especially in complex terrain. However, the system limitations and limitations imposed by the wind farm site are detrimental to the installation of scanning lidars and the number and location of the measurement points. To simplify the process of finding suitable measurement positions and associated installation locations for the WindScanner system, we have devised a campaign planning workflow. The workflow consists of four phases. In the first phase, based on a preliminary wind farm layout, we generate optimum measurement positions using a greedy algorithm and a measurement “representative radius”. In the second phase, we create several Geographical Information System (GIS) layers such as exclusion zones, line-of-sight (LOS) blockage and lidar range constraint maps. These GIS layers are then used in the third phase to find optimum positions of the WindScanner systems with respect to the measurement positions considering the WindScanner measurement uncertainty and logistical constraints. In the fourth phase, we optimize and generate a trajectory through the measurement positions by applying the traveling salesman problem (TSP) on these positions. The described workflow has been digitalized into a Python package named campaign-planning-tool, which gives users an effective way to design measurement campaigns with WindScanner systems. In this study, the Python package has been tested on three different sites characterized by different terrain complexity and wind farm dimensions and layouts. With minimal effort, the Python package can optimize measurement positions and suggest possible lidar installation locations for carrying out resource assessment campaigns.

A numerical study on wind dams: A novel approach to enhance wind potential using natural barriers

A natural barrier, such as a hill or a small mountain, might be the location of a great potential for wind energy production. The expression ‘wind dam’ refers to a newly proposed concept which aims to take advantage from the existing geographical natural barriers, in order to generate a high speed wind zone for installation of wind turbines. Previous studies have claimed that excavating a tunnel in a hill, connecting its high pressure zone to the low pressure zone, provides not only a suitable location for wind turbines, but also minimizes the negative environmental and visual impacts of the installed wind turbines. In the current study, the effects of various geometrical parameters on the air flow pattern inside such a tunnel are investigated numerically. The effects of the tunnel inclination angle, converging/diverging angle, entrance shape, tunnel width, incident wind velocity and its profile on the air flow increment inside the tunnel are examined in details. Finding the best shape for the tunnel, the effects of adding a wind turbine inside the tunnel are also analyzed. Results show that the best locations for the beginning and ending of the tunnel are almost the upwind base and the summit, respectively. For a tunnel without any wind turbines mounted in it, the maximum velocity increment is 1.5U∞ which occurs in a 2-degrees diverging tunnel with an initial width of 0.04H, where H is the hill height and U∞ is the incident wind velocity. Investigating the incident wind profile effects showed that the velocity increment in the tunnel is higher at the sites with lower roughness heights. It is also found that a relatively uniform velocity inside the tunnel can be obtained by modifying the inlet shape of the tunnel. Considering the effects of adding a wind turbine inside the tunnel revealed that this turbine can generate at least 50% more energy compared to a bare wind turbine, for the loading coefficients between 0 and 1. However, for the loading coefficients larger than 1.0, the wind dam concept works worse than a bare wind turbine.

Probabilistic analysis of offshore geotechnical site investigation in a homogeneous stiff clay deposit

Unlike most onshore geotechnical projects, conducting offshore site investigations is often difficult and technically challenging due to the harsh ocean environment. Hence, an important cost consideration is minimizing the number of soundings for an offshore project while ensuring reliable and safe designs. This study examines the possibility of reducing the number of soundings at site by using practical numerical tools to predict cone tip resistance (qt ) at unsampled locations. Prediction errors were quantified within a probabilistic framework. Two prediction approaches, i.e. 2D linear regression and Kriging, were adopted. Site investigation data from an existing offshore wind farm with homogeneous stiff clay deposit were collected and analyzed. Result shows that in a homogeneous soil deposit, it is possible to use as little as 20% of the available data to predict the remaining 80% qt profiles with small error. Monopile deformation under typical design load was also calculated using shear strength derived from the qt profile at each wind turbine location. The small spatial variation of monopile deformation suggests that a reduction in the number of geotechnical soundings for sites with uniform soil conditions would not significantly impact the foundation design.

Siting and sizing of wind farms taking into account stochastic nature of generation

The article deals with the problem of the negative impact of wind farms stochastic generation on power grid. One of the ways to reduce the stochasticity of the wind farms generation is their geographically distributed siting. A technique for sizing and distributed siting of wind farms from the standpoint of the influence on the variability of the total generated power is proposed. Modeling of wind power generation with hourly detailing is carried out using the developed Wind-MCA software based on data from archives of long-term observations of ground-based weather stations. The optimal distribution of wind turbines in potential locations is based on a genetic algorithm. The objective function is the coefficient of variation of the power generated by all wind farms in the sites under consideration, depending on the number of wind turbines in their composition. The genetic algorithm is implemented using the built-in MATLAB function. The proposed technique is applied to assess the capacity options and sites for wind farms in the Zabaykalsky Krai. The solution providing the minimum value of the coefficient of variation of the wind farms generated power and high value of the wind farms capacity utilization factor has been obtained.

Wind Farm Layout Optimization Considering Obstacles Using a Binary Most Valuable Player Algorithm

Wind farms are developed and implemented in many places around the globe. Designing a wind farm is becoming more and more complex especially with the recent trend towards large farms. Finding the optimal locations of wind turbines inside a wind farm to reduce energy cost is a highly challenging task, as it requires the handling of conflicting criteria and depending on the number of turbines considered it can turn to a large scale-optimization problem. Therefore, the aim of this paper is to place efficiently wind turbines inside a given area considering all constraints. This problem formulated as an optimization problem is referred to as the wind farm layout optimization (WFLO) problem. This real-world problem is nonlinear and difficult to solve using classical optimization algorithms and it has to take into consideration wind scenarios, power curve and wake effects. For this purpose, a binary version of the most valuable player algorithm (MVPA) called BMVPA is developed and implemented. Furthermore, ten scenarios were investigated using different wind speeds, terrain sizes with and without obstacles. For the same terrain but including obstacles, it was found that the energy cost increased due to the presence of obstacles that could limit the search space and consequently reduces the number of available options. The empirical results obtained using BMVPA were compared with those obtained using other well-known algorithms like the binary particle swarm optimization and genetic algorithm. BMVPA showed better results in solving the WFLO problem than the comparative algorithms. The optimum design of the wind farm obtained will allow an efficient and economic exploitation of wind resource.

An Applicable Method to Improve Transient and Dynamic Performance of Power System Equipped With DFIG-Based Wind Turbines

The improvement of synchronous generators (SGs) stability is one of the most important issues in power systems. To this aim, doubly fed induction generator (DFIG) can be helpful. The main purpose of this paper is to improve both transient and dynamic performance of SG by using local DFIG-based wind turbines. Firstly, a transient controller (TC) is designed in controlling block of DFIG which changes its operating mode from generator to motor regime. Secondly, two damping controllers which are named electromagnetic torque band damping controller (ETBDC) and reactive power band damping controller (RPBDC) are suggested to improve dynamic stability of SG. Each damping controller includes three feedbacks namely, SG speed changes, SG electromagnetic torque and DFIG electromagnetic torque. Moreover, genetic algorithm (GA) is used to adjust these controlling parameters. The performance of TC is examined by comparison of system transient indexes such as critical clearing time (CCT), SG accelerating energy and DFIG inertia energy with and without using the proposed TC in a two-area test network. Similarly, system dynamic performance indexes such as overshoot, settling time, eigenvalue and damping torque of SG are compared with and without using two dynamic controllers. The obtained results show the effectiveness of proposed controllers.

Observation-based estimates of land availability for wind power: a case study for Czechia

BackgroundThe availability of land for the installation of wind power turbines is restricted by numerous factors. Besides climatic conditions, the deployment of wind energy is limited by technical, social, economic, and environmental factors. Typically, assessments of land availability for wind power use legal and technical criteria to estimate the potential for wind power expansion. In contrast, we use observed characteristics of wind power generation sites existing in Austria and Denmark to estimate its potential expansion in Czechia. We combined data on wind turbine locations with data on land use, wind speeds, human impact on land, and nature conservation areas.ResultsOur analysis shows that the density of wind power in Austria is variable, but higher on average (4.79 MW km−2) than in Denmark (1.76 MW km−2). Austrian wind turbines have been installed in areas where the human impact on land is mostly higher than the Austrian average, while in Denmark, no difference is observed. Regarding the land use composite, the share of agricultural land on sites with wind turbines is on average much higher (86%), while the share of forest is much lower (7%) in both countries. We identified a maximum potential area in Czechia of 543 km2 with Austrian and 421 km2 with Danish characteristics. When conservatively assuming observed historical power densities, this area translates to 2295 MW and 741 MW of installed wind power capacity, respectively. These results are a magnitude of order lower than the potentials found in existing studies. In a sensitivity analysis, we have examined that the availability of potential sites depends mainly on the population density, the human impact on land, prevailing wind speeds, and the height above sea level.ConclusionsWe estimated available land area for potential wind turbine installations in Czechia using our newly developed methodology based on observed site characteristics of today’s wind power infrastructure in Austria and Denmark. Available land area indicated possible overestimation of wind power capacities proposed in the recent studies on the renewable energy transition. Hence, more rigorous consideration of land availability is required for assessments of potential wind power expansion.

In search of flexible and robust wind farm layouts considering wind state uncertainty

Variations in wind states are generally ignored while designing the wind farm layouts. As a result, given the highly uncertain nature of wind the ability of the wind farms to produce power on a sustained basis gets severely affected in the long run. In this study, a robust optimization methodology has been proposed and successfully adopted to design wind farm layouts (optimum number and location of wind turbines) that are immune to varying wind state conditions. Assuming different realizations of wind state uncertainty distributions of wind speeds and directions, the proposed methodology provides uncertainty resilient robust solutions for the conservative worst case and the optimistic best case situations by adopting maximum and minimum extent of variations in uncertain parameters, respectively. Additionally, the flexibility introduced in the formulation enables a designer to create solution anywhere between these two extremes. This set of robust solutions provide with a realistic envelope for the farm performance parameters under uncertainty helping meeting the guaranteed service levels. It has been observed that the turbine layout corresponds to the worst case can produce an average of ∼49% more power than the guaranteed value and provide with 100% success rate on meeting the service levels under changing wind conditions. Representing the wind farm by uniform set of coarse grids initially, need based grids of variable resolution along with novel repair operator are adopted to generate Pareto solutions for the multi-objective cost-power trade-off problem. The proposed grid based technique is validated against other techniques of no-grid and grid with fixed resolution and improvements of ∼9.0% in power production and ∼1.7% in the ratio of cost and power can be seen. Furthermore, improvements of 75.7% in produced power as compared to rigorously explored benchmark case study and 1–10% in cost to power ratio compared to other techniques presented in literature, show the merits of the proposed technique.