Spatial dependence in wind and optimal wind power allocation: A copula-based analysis

The investment decision on the placement of wind turbines is, neglecting legal formalities, mainly driven by the aim to maximize the expected annual energy production of single turbines. The result is a concentration of wind farms at locations with high average wind speed. While this strategy may be optimal for single investors maximizing their own return on investment, the resulting overall allocation of wind turbines may be unfavorable for energy suppliers and the economy because of large fluctuations in the overall wind power output. This paper investigates to what extent optimal allocation of wind farms in Germany can reduce these fluctuations. We analyze stochastic dependencies of wind speed for a large data set of German on- and offshore weather stations and find that these dependencies turn out to be highly nonlinear but constant over time. Using copula theory we determine the value at risk of energy production for given allocation sets of wind farms and derive optimal allocation plans. We find that the optimized allocation of wind farms may substantially stabilize the overall wind energy supply on daily as well as hourly frequency.     

Probabilistic performance assessment of wind energy conversionsystems

This paper describes the development of a general probabilistic model of an autonomous wind energy conversion system (WECS) composed of several wind turbines (wind farm) connected to a load and a battery storage. The proposed technique allows the simulation of wind farms containing identical or different wind turbines types and considers a bidirectional flow of power in and out of the battery. The model is based upon a simple procedure to estimate the joint probability distribution function of the total available wind power and that of the turbines operating modes due to hardware failure. A methodology is also developed to use the proposed model to determine an upper limit on the size of the battery storage required for a given number of turbines to satisfy the load with a certain expected energy not supplied (EENS). The model can also be used to evaluate the energy purchased from or injected to the grid in the case of grid-connected systems     

Scenarios for offshore wind power production for Central California Call Areas

In response to the growing interest in offshore wind energy development in California, the U.S. Bureau of Ocean Energy Management delineated three Call Areas for potential leasing. This study provides a comprehensive characterization and comparison of offshore wind power potential within the two Central California Call Areas (Diablo Canyon and Morro Bay) using 12- and 15-MW turbines under different inter-turbine spacing and wind farm size scenarios. Our analysis shows similar daily and seasonal patterns of wind power produced within the Call Areas, which peak in spring and during evening hours. Per-turbine power production is higher in the Morro Bay Call Area due to slightly higher hub-height wind speeds, whereas total power production is higher in the Diablo Canyon Call Area due to its larger size. Turbine type had a negligible impact on average power production per-unit-area because while larger turbines produce more power, they require greater inter-turbine spacing. Combined power production from the two fully built out Call Areas could equal nearly a quarter of California's current annual electrical energy production. A commercial-scale wind farm with a realized power output of 960 MW would require a footprint of at least half of the Morro Bay Call Area or at least a quarter of the Diablo Canyon Call Area. These results provide guidance on offshore wind development over the Central California Coast, and the framework demonstrated here could be applied to other wind data sets in other regions.     

A framework for data integration of offshore wind farms

Operation and maintenance play an important role in maximizing the yield and minimizing the downtime of wind turbines, especially offshore wind farms where access can be difficult due to harsh weather conditions for long periods. It contributes up to 25–30% to the cost of energy generation. Improved operation and maintenance (O&M) practices are likely to reduce the cost of wind energy and increase safety. In order to optimize the O&M, the importance of data exchange and knowledge sharing within the offshore wind industry must be realized. With more data available, it is possible to make better decisions, and thereby improve the recovery rates and reduce the operational costs. This article describes the development of a framework for data integration to optimize the remote operations of offshore wind farms.     

Beyond NIMBYism: towards an integrated framework for understanding public perceptions of wind energy

It is widely recognised that public acceptability often poses a barrier towards renewable energy development. This article reviews existing research on public perceptions of wind energy, where opposition is typically characterized by the NIMBY (not in my back yard) concept. The objectives of the article are to provide a critical assessment of past research and an integrated, multidimensional framework to guide future work. Six distinct strands of research are identified, summarized and critiqued: public support for switching from conventional energy sources to wind energy; aspects of turbines associated with negative perceptions; the impact of physical proximity to turbines; acceptance over time of wind farms; NIMBYism as an explanation for negative perceptions; and, finally, the impact of local involvement on perceptions. Research across these strands is characterized by opinion poll studies of general beliefs and case studies of perceptions of specific developments. In both cases, research is fragmented and has failed to adequately explain, rather than merely describe, perceptual processes. The article argues for more theoretically informed empirical research, grounded in social science concepts and methods. A multidimensional framework is proposed that goes beyond the NIMBY label and integrates previous findings with social and environmental psychological theory. Copyright © 2004 John Wiley & Sons, Ltd.     

Renewable energy in a market-based economy: How to estimate its potential and choose the right incentives

A model to explain and predict market-driven investment in renewable energy capital is proposed. The model is suitable for application to the biomass, wind, solar and ocean-derived energy industries. It basically assumes that, given a set of prices and a specific technology, the marginal efficiency of capital invested in these industries only depends on the productivity of the project's site and on its energy transport distance. As suggested by traditional investment theory, the model supposes that only those projects offering marginal efficiencies of capital above the current available rate of interest would be implemented, thus demarcating a region in the productivity–energy transport distance space where all the economically viable projects should lie. By relating this region to the geographic space available for development, total potential investment can be deduced. By using cash flows defined in variable energy transport distance and mean wind speed, a case study for the Chilean wind energy industry is presented. The use of the model to analyse the effect of alternative support schemes for wind energy in Chile is briefly demonstrated. It is concluded that for increasing the area economically available for the development of new wind farms, a research and development support scheme aimed at reducing investment cost of wind turbines by 25% is equivalent to a 20% price subsidy on energy.     

Dynamic models of wind farms with fixed speed wind turbines

The increasing wind power penetration on power systems requires the development of adequate wind farms models for representing the dynamic behaviour of wind farms on power systems. The behaviour of a wind farm can be represented by a detailed model including the modelling of all wind turbines and the wind farm electrical network. But this detailed model presents a high order model if a wind farm with high number of wind turbines is modelled and therefore the simulation time is long. The development of equivalent wind farm models enables the model order and the computation time to be reduced when the impact of wind farms on power systems is studied. In this paper, equivalent models of wind farms with fixed speed wind turbines are proposed by aggregating wind turbines into an equivalent wind turbine that operates on an equivalent wind farm electrical network. Two equivalent wind turbines have been developed: one for aggregated wind turbines with similar winds, and another for aggregated wind turbines under any incoming wind, even with different incoming winds.The proposed equivalent models provide high accuracy for representing the dynamic response of wind farm on power system simulations with an important reduction of model order and simulation time compare to that of the complete wind farm modelled by the detailed model.     

Micrositing under practical constraints addressing the energy‐noise‐cost trade‐off

Wind energy is running well ahead of its peers to deal with the demand–supply and environmental crisis due to fossil fuels. However, continuous exploitation of land led wind farms built in close proximity of dwellings of human beings, restricted zones, causing adverse effect on health and the environment. In this work, using a widely used model for wake and acoustic model (ISO‐9613‐2), the optimal number and locations of turbines in a farm has been determined while meeting several conflicting objectives such as noise propagation, energy, and cost. An index‐based decomposition and repair strategy (iDRS) using different indices for grid locations and performing repair on chromosomes to enhance the performance of convergence has been proposed as solution methodology. Comparing with a well‐established case study, the methodology is applied next to another realistic case, where the effects of the presence of practical constraints on the optimal layout are demonstrated. A designer can select a layout from several choices from the obtained Pareto set of solutions based on the permissible noise limits, cost obligations, and the extent of harnessed energy.     

Wind energy development and its environmental impact: A review

Wind energy, commonly recognized to be a clean and environmentally friendly renewable energy resource that can reduce our dependency on fossil fuels, has developed rapidly in recent years. Its mature technology and comparatively low cost make it promising as an important primary energy source in the future. However, there are potential environmental impacts due to the installation and operation of the wind turbines that cannot be ignored. This paper aims to provide an overview of world wind energy scenarios, the current status of wind turbine development, development trends of offshore wind farms, and the environmental and climatic impact of wind farms. The wake effect of wind turbines and modeling studies regarding this effect are also reviewed.     

Comparative simulation of wind park design and siting in Algeria

In this paper, five typical regions of Algeria where wind is strong enough are selected. These regions usually intended for traditional agriculture are, centred around the towns of Guelma, El Oued, Tindouf, Touggourt and Tamanrasset. To make wind energy conversion available as an alternative energy source for the populations living in such countries, nine types of small and medium wind turbines constructed by American and European manufacturers are studied for their suitability. To account for the wind variations with height, four possible heights of the pylon holding the turbines are considered: 10, 20, 40 and 60 m. In each of the five locations and at each pylon height, wind energy converted by the turbines, is cumulated over the year and computed. Depending on the site and their size, most of these turbines are found to produce about 1000–10,000 MWh of electricity per year at 60 m of altitude and can easily satisfy the electricity need in irrigation and its household applications in rustic and arid regions. A quick glance of the results of the above computation shows that the choice of pylons of 20 m height yields a trade-off between the production of electrical energy and the requirements of economy. Owing to the sporadic wind variations, wind energy conversion systems can only be used as an auxiliary source. In particular, these systems can advantageously be coupled to stand-alone photovoltaic conversion systems in remote locations or connected to the electric mains in urban zones.     

Public perceptions of wind energy developments: Case studies from New Zealand

Although the public generally hold positive attitudes towards wind energy, proposals for the construction of new wind farms are often met with strong resistance. In New Zealand, where the government has recently introduced ambitious policy targets for renewable energy generation, negative perceptions of wind farms are increasingly evident and have the potential to prevent the achievement of these targets. This research sets out to examine what influences social resistance to wind farms in New Zealand. Drawing from public submissions on three wind farm proposals, a framework developed by Devine-Wright [Devine-Wright, P., 2005a. Beyond NIMBYism: towards an integrated Framework for Understanding Public Perceptions of Wind Energy. Wind Energy 8, 125–139.] was used as the basis for identification of factors affecting public perceptions of wind farms. The research found firstly that there was no apparent relationship between the proximity of submitters to a proposed wind farm and their likelihood of having a negative perception of the proposal. A wide range of factors written in submissions appeared to have affected the submitter's decision to support or oppose the wind farm proposal. Some of these were consistent with Devine-Wright's findings, but ten further factors were added to the framework to adequately cover the aspects raised in submissions. The findings have implications for the achievement of New Zealand's energy policy aspirations.     

Estimation of wind energy production in various sites in Australia for different wind turbine classes: A comparative technical and economic assessment

This study examines the effect of different wind turbine classes on the electricity production of wind farms in three areas of Australia, which present low, low to medium, and medium to high wind potential: Gingin, Armidale, and Gold Coast Seaway. Wind turbine classes determine the suitability of installing a wind turbine in a particulate site. Wind turbine data from six different manufacturers have been used. For each manufacturer, at lest two wind turbines with identical rated power (in the range of 1.5 MW–3 MW) and different wind turbine classes (IEC I, IEC II and/or IEC III) are compared. The results show the superiority of wind turbines that are designed for lower wind speeds (higher IEC class) in all three locations, in terms of energy production. This improvement is higher for the locations with lower and medium wind potential (Gingin and Armidale), and varies from 5% to 55%. Moreover, this study investigates the economical feasibility of a 30 MW wind farm, for all combinations of site locations and wind turbine models.     

Utilization of machine‐learning algorithms for wind turbine site suitability modeling in Iowa,USA

Because of the current shift away from fossil fuels and toward renewable energy sources, it is necessary to plan for the installation of new infrastructure to meet the demand for clean energy. Traditional methods for determining wind turbine site suitability suffer from the selection of arbitrary criteria and model parameters by experts, which may lead to a degree of uncertainty in the models produced. An alternative empirically based methodology for building a wind turbine siting model for the state of Iowa is presented in the study. We employ ‘ecological niche’ principles traditionally utilized to model species allocation to develop a new multicriteria, spatially explicit framework for wind turbine placement. Using information on suitability conditions at existing turbine locations, we incorporate seven variables (wind speed, elevation, slope, land cover, distance of infrastructure and settlements, and population density) into two machine‐learning algorithms [maximum entropy method (Maxent) and Genetic Algorithm for Rule Set Prediction (GARP)] to model suitable areas for installation of wind turbines. The performance of this method is tested at the statewide level and a six‐county region in western Iowa. Maxent and GARP identified areas in the Northwest and North Central regions of Iowa as the optimum location for new wind turbines. Information on variable contributions from Maxent illuminates the relative importance of environmental variables and its scale‐dependent nature. It also allows validating existing assumptions about the relationship between variables and wind turbine suitability. The resultant models demonstrate high levels of accuracy and suggest that the presented approach is a possible methodology for developing wind turbine siting applications. Copyright © 2014 John Wiley & Sons, Ltd.     

Turbulent times: tourists’ attitudes towards wind turbines in the Southern Highlands in Iceland

As a response to the threat of climate change, many nations are increasing their use of renewable energy, including wind energy. Large wind farms often conflict with other land uses, particularly tourism, which is a growing industry worldwide. In Iceland, tourism has recently become the largest export sector, with majority of tourists travelling to the country to experience its nature. This paper examines tourists’ opinions and perceptions of wind power development in the Southern Highlands of Iceland and compares how number, size and proximity of wind turbines, and the landscape in which they are situated, influence tourists’ perceptions. The study is based on an on-site questionnaire survey conducted in 2015. The results indicate that one-third of the travellers would be less likely to visit the Southern Highlands if a proposed wind farm were built, and two-thirds think that wind turbines would decrease the area’s attractiveness.     

Residents' perceptions of wind turbines: An analysis of two townships in Michigan

Wind energy development has become a ‘hot topic’ across Michigan as this state seeks to achieve 10% of energy delivered to consumers from renewable sources (Huron County Planning Commission, 2005). The focus of this effort to generate renewable energy has centered around wind energy. Wind turbines have been constructed at numerous locations across the state. The lower peninsulas' eastern counties near Lake Huron and Saginaw Bay were designated by the Wind Energy Resource Zone board as one such area of strong sustained wind in the state. Turbines have been constructed in ‘pockets’ across this ‘thumb’ region, yet half a decade after the first turbines were constructed, negative perceptions are still attributed to wind turbines. This paper examines residents of wind farm locations as a whole and independently as groups (those in opposition and in support of development) to identify what, if any similarities and differences, exist between the residents' perceptions. Qualitative analysis on stated negative perceptions unveiled common issues with residents: increased price of electricity with wind energy, noise from the turbine rotation and uncertainty surrounding the long term effects of wind turbines. These areas of concern seem to persist years after construction was completed.     

Developing and applying a GIS-assisted approach to locating wind farms in the UK

An increase in public awareness regarding the negative impact of traditional power-generating methods, especially coal and oil-fired power stations, on the environment has created a demand for developing and using environmentally friendly renewable energy. Wind power is a popular and safe form of renewable energy. It can be economically viable, does not produce any physical pollution and can contribute radically to the reduction in air pollution. In the UK, the demand for wind energy is mounting. However, achieving the goal set by the EU will require a substantial expansion. Planning and environmental restrictions and conflicts would inevitably accompany this growth. A questionnaire targeting relevant public and private sectors in the UK has revealed the lack of coherent national criteria for locating wind farms. Using information from the questionnaire and the available published literature, simple Geographical Information System (GIS)-assisted wind farm location criteria were developed for the UK. A GIS (IDRISI) was employed to apply these criteria using two different methods to combine information layers for a site in Lancashire. The first considered all the layers as being equally important and gave them equal weight. The second grouped the layers and graded them according to perceived importance. The outputs were composed of classes from 0 to 10, where 0 represents ideal locations and 10 represents unsuitable locations. Using the second method has resulted in a slight increase in the geographical extent for the most suitable sites. These maps can be used to assist in the decision-making process when locating wind farm sites.     

Wind farms connected to weak grids in India

This article describes the mutual influence between the wind turbines and the power quality in the Indian power systems. The Indian power systems are weak in general and the wind energy development has been very fast and concentrated in a few rural areas where the existing transmission and distribution grids are very weak. Therefore the mutual influence between wind turbines and power quality is particularly strong in India. The wind farms influence power quality aspects such as steady state voltage, power factor, flicker and harmonic and interharmonic distortion. The power quality of the grid influences the power performance and safety of the wind turbines and the lifetime of mechanical and electrical components. The findings presented are general for wind farms connected to weak grids, but the Indian case is pronounced concerning weakness of grid as well as wind energy penetration level. Copyright © 2001 John Wiley & Sons, Ltd.     

The flow in the induction and entrance regions of lab-scale wind farms

With the increasing demand for wind energy, it is important to be able to understand and predict the available wind resources. To that end, the present wind tunnel study addresses the flow in the induction and entrance region of wind farms through particle image velocimetry, with focus on differences between actuator disks and two-bladed rotating wind turbine models. Both staggered and aligned farm layouts are examined for three different incoming wind directions. For each layout, 69 disks or turbines are used, and the field of view ranges from 12 rotor diameters upstream of the farms to 8 diameters downstream of the first row. The results show that the induction, or blockage effect, is higher for the disks, even though the thrust (or drag) coefficient is the same. In contrast, the wake is stronger downstream of the turbines. The orientation and layout of the farm do not have a major impact on the results. Modal decomposition of the flow shows that the flow structure similarity between the disk and turbines improves downstream of the second row of wake generating objects, indicating that the substitution of wind turbines by actuator disks is more appropriate for wind farms than for the investigation of single wakes.     

Estimation of turbulence intensity using rotor effective wind speed in Lillgrund and Horns Rev-I offshore wind farms

Turbulence characteristics of the wind farm inflow have a significant impact on the energy production and the lifetime of a wind farm. The common approach is to use the meteorological mast measurements to estimate the turbulence intensity (TI) but they are not always available and the turbulence varies over the extent of the wind farm. This paper describes a method to estimate the TI at individual turbine locations by using the rotor effective wind speed calculated via high frequency turbine data.The method is applied to Lillgrund and Horns Rev-I offshore wind farms and the results are compared with TI derived from the meteorological mast, nacelle mounted anemometer on the turbines and estimation based on the standard deviation of power. The results show that the proposed TI estimation method is in the best agreement with the meteorological mast. Therefore, the rotor effective wind speed is shown to be applicable for the TI assessment in real-time wind farm calculations under different operational conditions. Furthermore, the TI in the wake is seen to follow the same trend with the estimated wake deficit which enables to quantify the turbulence in terms of the wake loss locally inside the wind farm.     

Modeling noise and lease soft costs improves wind farm design and cost-of-energy predictions

The Department of Energy uses the metric Cost-of-Energy to assess the financial viability of wind farms. Non-hardware costs, termed soft costs, make up approximately 21% of total cost for a land-based farm, yet are only represented with general assumptions in models of Cost-of-Energy. This work replaces these assumptions with a probabilistic model of the costs of land lease and noise disturbance compensation, which is incorporated into a wind-farm-layout-optimization-under-uncertainty model. These realistic representations are applied to an Iowa land area with real land boundaries and house locations to accentuate the challenges of accommodating landowners. The paper also investigates and removes a common but unnecessary term that overestimates cost-savings from installing multiple turbines. These three contributions combine to produce COE estimates in-line with industry data, replacing “soft” assumptions with specific parameters, identify noise and risk concerns prohibitive to the development of profitable wind farm. The model predicts COEs remarkably close to real-world costs. Wind energy policy-makers can use this model to promote new areas of soft-cost-focused research.