Proposed Wind Farm Research Articles

Forecasting Wind Speed Using Clustering of Trend-Based Time Series Data

Accurate forecasting of wind speed is crucial for the efficient operation of wind energy systems. As a time-series concern, wind forecasting may help determine how much electricity a proposed wind farm might produce annually. The majority of forecasting techniques perform differently depending on seasonal and trends variation. For this reason, time series data frequently have seasonal and nonlinear trend components eliminated in order to simplify wind forecasting approach. The application function used to remove the seasonality and trend determines accuracy. The proposed method begins by identifying and extracting underlying trends from historical wind speed data, segmenting the time series into distinct trend-based components. This paper proposes a hybrid method for predicting time series. A method for clustering data has been designed that identifies clusters of time series data with similar trend components. Statistical procedures, such as generalized autoregressive and autoregressive integrated moving average scoring approaches, are used to each individual cluster after the appropriate clusters of related trend components have been identified. Ultimately, the components that are made are combined. The datasets collected from the NREL site. The experiment demonstrates that when compared to current statistical approaches, the cluster-based forecasting approach performs better. This research makes contribution towards the field of renewable energy forecasting by providing a robust and scalable method for wind speed prediction, which can be integrated into existing energy management systems for improving the efficiency and stability of wind energy generation. The research paper examines the trend features of time series data of wind employing the suggested hybrid technique on wind forecasting. Performance calculated in terms of RMSE and MAE shows that the proposed technique succeeds as compared to other state of the art techniques.

Assessing offshore wind plants for energy and green hydrogen production: A case study in the Mostaganem coastal region, Algeria

This study assesses the feasibility and strategic implications of establishing a 500 MW offshore wind farm utilizing fixed wind turbine technology along Algeria’s western coastal economic zone, with a focus on the Mostaganem region. The study recommends integrating 33 Vestas V-235 wind turbines, each boasting a 15 MW capacity to harness offshore wind resources for electricity and green hydrogen production. Utilizing the Wind Atlas Analysis and Application Program (WAsP), offshore wind potential is assessed based on meteorological data from a land-based station, overcoming the lack of in situ sea level measurements. Results show that the proposed wind farm could yield 1361.63 GWh/year of electricity at a cost of 0.89 $/kWh. Additionally, it would produce 28,571 tons of hydrogen at a cost of 2.12 $/kg. This initiative is anticipated to significantly reduce CO2 emissions, by approximately 595,450 tons/year, highlighting the effectiveness of offshore wind resources in promoting sustainable energy production and advancing green hydrogen as a viable energy carrier in this region.

A numerical simulation framework for wakes downstream of large wind farms based on equivalent roughness model

Accurate assessment of the wake effect of wind farm clusters is of great significance for the development of wind power bases, yet there is currently a deficiency in numerical simulation methods with high accuracy and simple modeling. To address this issue, a numerical simulation framework for large wind-farm wakes based on a wind farm parameterization model is proposed, and is then implemented based on the open-source CFD software OpenFOAM. In this framework, a newly proposed wind farm equivalent roughness model is adopted to parameterize wind farms and incorporated into the simulation via the wall stress model. To validate the method, the large-eddy simulation based on the actuator disk model resolving all the turbines in the wind farm is taken as a benchmark. Thirty two cases including spanwise infinite wind farms and finite wind farms with different streamwise turbine spacings, spanwise turbine spacings, wind turbine thrust coefficients, and roughness lengths of ground surface are set to analyze the sensitivity of the framework to different wind farm parameters. Results show that the proposed method predicts the velocity deficit and turbulence intensity downstream of the wind farm accurately. Compared to the classical wake superposition model, the prediction accuracy of the velocity deficit is improved by more than 30 %. Furthermore, without resolving the turbines using fine mesh, the framework shows a high potential to reduce computational costs compared with the turbine-resolved simulation.

The influence of seabed lease fees on offshore wind farm design

Governments that control seabed generally extract fees from the offshore wind sector, often tying these fees to the characteristics of the proposed wind farms. These fees could change the optimal design of offshore wind farms, thereby affecting the long-term development of the industry. We employ microeconomic theory and incentive-response analysis to explain the effects of different types of fees on the optimal design and characteristics of offshore wind farms. We find that fees that are structured to be proportional to wind farm area, nameplate capacity, production, and revenue may influence the optimal design of a wind farm from a wind farm developer's perspective. Fees based on area, production, or revenue may result in less area used by developers. Fees based on capacity, production, or revenue may result in the choice of turbines with lower specific power, while fees based on production or revenue may additionally encourage the use of smaller turbines. All four fee types may encourage developers to use fewer turbines per wind farm. If designed intentionally, policymakers could use seabed lease fees to guide the wind farm designs toward more socioeconomically optimal outcomes, including less area used, reduced environmental impacts, or better grid integration.

Whose Expertise and Whose Knowledge Matter? Influencing Wind Power Decisions in Norway

ABSTRACT Norway is committed to promoting energy from renewable sources, but wind power installations are often sited in areas where the environment remains relatively untouched. This has generated a debate about the acceptability of such installations in the country. Host municipalities in Norway hold an unofficial veto power over the outcomes of proposals for wind power developments, so the ways that local attitudes and beliefs are influenced could be crucial to the outcome of licensing decisions. This research explores how elected officials come to hear about proposed wind farm developments, whose knowledge and expertise they trust on the issue – as well as whose is discounted – and how this impacts the eventual perceptions that they adopt. We investigate the persuasive influence of narrative messaging on both supportive and oppositional stakeholders in a land-based wind power licensing process in Norway.

Developed analytic hierarchy process and multi criteria decision support system for wind farm site selection using GIS: A regional-scale application with environmental responsibility

As determined by empirical analysis, renewable energy sources account for less than 1.73 percent of the developing countries energy market. The western region of Iran, or province of Kermanshah, presents significant opportunities for power exports to neighboring country (Iraq). In this research, an appropriate location for wind farms in Western Iran is determined with the assistance of a combined method of Geographic Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) description. Electrical, techno-economic, environmental, and geo-infrastructure aspects, as well as their respective sub-criteria, were subjected to a comprehensive analysis and weighting in this study. The Analytical Hierarchy Process (AHP) was utilized to achieve this result. The aforementioned conclusions were reached through an extensive examination of insightful interviews conducted with esteemed experts in the domain of wind energy. Significant suitability for the potential installation of large-scale wind turbines or wind farms is evident in six critical areas, encompassing more than 20 % of the evaluated region, according to the findings of this paper. Conversely, it was deduced that precisely 80 % of the areas examined were deemed inappropriate for such endeavors. The assessment yielded four locations that were deemed suitable, denoted as A, B, C, and D. The southern, western, and central regions of the province contain these locations. The proposed total capacity is 216 MW, which is determined by the combined surface areas of these regions (19 km2, 55 km2, 64 km2, and 40 km2, respectively). An anticipated reduction of 1,262,471 tons in greenhouse gas emissions is anticipated as a result of the implementation and substitution of the proposed wind farms for the gas turbine power plants that presently predominate in Iran's thermal power plant sector. The method used in this article can be extended to all cases of other faculties to measure the potential of wind farm resources.

Approach Distances of Scottish Golden Eagles Aquila chrysaetos to Wind Turbines According to Blade Motion Status, Wind Speed, and Preferred Habitat

Understanding drivers underlying birds’ responses to operational wind turbines is essential for robust wind farm proposal assessments, especially for large raptors with life history traits engendering sensitivity to impacts from two potential adverse effects: fatality through collision with rotating turbine blades and functional habitat loss through avoidance of turbines. The balance between these two potential effects represents opposing extremes on a continuum and is influenced by several factors. Collisions have an obvious impact on survival, but the impacts of avoidance may be more insidious and potentially more significant for a population. It is reasonable to conclude that collisions are less likely when blades are motionless. Consequently, turbine shutdown systems (TSSs, “shutdown on demand” or “curtailment”), instigated as raptors approach operational turbines, may provide mitigation against collisions. By contrast, if avoidance is most likely, this could be independent of blade motion, and TSSs/curtailment would provide no mitigation against habitat loss. For birds tending to wariness of turbines, therefore, it is important to understand if it is conditional on blade motion. Scottish golden eagles show a strong propensity to avoid (be wary of) turbines, subject largely to the suitability of habitat at and surrounding turbine locations. A previous Scottish study found that approach distances to turbines by non-territorial eagles were unaffected by blade motion but were closer at higher wind speed. Here, we analyse movement data from a GPS-tagged territorial eagle and non-territorial eagles responding to the motion status (and wind speed) of turbines at another Scottish wind farm. Eagles’ approach distances to turbines were only weakly affected by blade motion but were closer at higher wind speed. We again found that habitat suitability in and around turbine locations was strongly influential on eagles’ approach distance to turbines. Our confirmation that blade motion had little effect on Scottish golden eagles’ wariness of turbines suggests that for eagles that are prone to avoid turbines, their wariness is a response to turbines per se, and not blades’ movement. In our study system, and others where avoidance is the predominant response, curtailment of turbines’ operation on birds’ close approaches, or making turbine blades more obvious, should, therefore, have little material influence on functional habitat loss impacts. If true, this has important implications for wind farm designs and any proposed mitigation.

Modelling the wind potential energy for metallurgical sector in Albania

The metallurgical industry, in the context of the global energy crisis and the new European green deal, needs urgent investments on energy and resource efficiency. The metallurgical sector, which includes the production of different metals is an energy-intensive industry that requires large amounts of energy for various processes such as smelting, refining, and casting. One of the largest consumptions of energy in Albania comes from the metallurgical sector during the production of iron, steel, chromium and aluminum which corresponds respectively to three private companies called “Kurum International Ltd”, “AlbChorme Ltd” and “Everest Ltd”. During the last three years, these companies have temporary interrupted the production process due to the higher electricity price that come from imports. Based on it, our research work presents the energy efficiency analysis in the Albanian metallurgical sector by focusing on the implementation of wind energy in the above mentioned private metallurgical companies, because adding new generation capacity from Renewable Energy Sources in a context of industrial energy communities, will contribute to improve the security of supply for this industry. The Wind Balkan Atlas, New European Wind Atlas, and Wind Atlas Analysis and Application Program (WAsP) has been used to select the appropriate areas and to develop the wind potential distribution maps, as well as to select the most suitable type of wind turbine based on capacity factors. Two areas were selected close to the metallurgical sectors in the regions of “Vajkal” in Bulqizë and “Selitë e Malit” in Tirana. It has been installed the power of 9 MW for each wind farm, with a capacity factor of 40% and 36.6% respectively, and with a total annual energy production of about 60 GWh/year, these wind farms will cover about 26% of the total annual consumption of companies. Clean Energy Management Software (RETScreen Expert) was used for the detailed economic analysis and environmental impact of proposed wind farms. The economic sensitivity analysis of the proposed wind farms showed that even for the highest installation cost value of 1350 €/kW, for discount rates 5, 7, and 11%, the LCoE values are within the statistically established range for wind farms in Europe.

Analysis of Clean Development Mechanism for Dernah Wind Farm (I) Project (Libya) by Using AM0019 Methodology

Since the Kyoto Protocol came into effect on Feb 16, 2005, the Clean Development Mechanism (CDM) has experienced significant global growth. This mechanism enables developing countries to actively engage in combating climate change by implementing projects aimed at reducing Greenhouse Gas Emissions (GGEs). In 2010, Libya established the Commission of the Designated National Authority (DNA) to oversee the implementation of the CDM. This move was made as part of Libya's efforts to develop a range of projects that qualify for CDM and contribute to reducing GGEs. The main motivation for conducting this study was the absence of Libya's involvement in global-level CDM projects, as evidenced by the United Nations Framework Convention on Climate Change (UNFCCC) annual reports. Additionally, the failure to recognize the significance of CDM in influencing decisions regarding investments in wind energy. This work aims to investigate the use of CDM in Dernah wind farm (I) project (Libya). The study used a suitable CDM methodology, AM0019 with the appropriate tool (03-V3), calculated as CO2 reductions and Certified Emission Reductions (CERs). The results of CDM analysis are as follows: CO2 reductions = 362,201.82 tCO2e/year and CERs for the first ten years of the age of the proposed wind farm (Dernah Wind Farm (ɪ)) (CERs10y) = 1,687,898,590 LD (320,838,371.8 €) for the first ten years, likewise CERs20y= 3,375,797,180 LD (641,676,743.5 €) during the entire life (20 years) of the proposed wind farm. Based on these results, it can be concluded that the project will be highly cost-effective, this will lead to lower electricity prices for consumers and higher profits for the project owners. Therefore, registering wind energy projects as CDM projects and earning CERs is the most practical way to promote wind energy. The findings of this study could be valuable for policy makers and project developers who are interested in CDM wind projects.

Carbon footprint and energy life cycle assessment of wind energy industry in Libya

The recent investigation has demonstrated that wind energy holds great potential as a viable and environmentally friendly energy source in Libya. The study employed a Life Cycle Assessment (LCA) methodology to evaluate various energy, economic, and environmental indicators for potential wind farm installations at multiple suitable locations across Libya. The assessment encompassed estimations of energy requirements and greenhouse gas (GHG) emissions associated with the conversion of wind energy into electricity throughout the entire life cycle of the proposed wind farms. In light of Libya not being a producer of wind energy converters, a novel approach was developed to define the system's boundaries. These boundaries included distinct subsystems, each corresponding to various stages within the life cycle of a wind energy system, encompassing factors such as shipping emissions from the manufacturer's location to Tripoli's marine ports, land transportation to the wind farm sites, energy and emissions associated with installation, operation, maintenance, and eventual disposal of wind turbines.Hourly climate data spanning a 25-year period from 1995 to 2020 were gathered from the SolarGis climate information site. The System Advisor Model (SAM) program was utilized to predict the energy yields of 100 MW capacity wind farms at 12 sites in Libya. Additionally, a novel eco-environmental indicator known as the Life Cycle Levelized Cost of Energy (LCLCOE) was introduced, which factors in all environmental damage costs throughout the entire lifespan of wind energy projects. The study's findings revealed that the Gamesa turbine, with a capital cost of $146,916,400 for a 100 MW capacity wind energy farm, exhibited the most favorable economic and environmental performance. GHG emission factors across all examined cities ranged from 32 to 70 gGHG/kWh, with carbon payback durations spanning from 4.5 to 12.3 months. The estimated energy payback period varied from 13 to 22 months, while the LCLCOE ranged from 4.8 to 8.4 ¢/kWh.

Centralized Offshore Hydrogen Production from Wind Farms in the Baltic Sea Area—A Study Case for Poland

In Poland, hydrogen production should be carried out using renewable energy sources, particularly wind energy (as this is the most efficient zero-emission technology available). According to hydrogen demand in Poland and to ensure stability as well as security of energy supply and also the realization of energy policy for the EU, it is necessary to use offshore wind energy for direct hydrogen production. In this study, a centralized offshore hydrogen production system in the Baltic Sea area was presented. The goal of our research was to explore the possibility of producing hydrogen using offshore wind energy. After analyzing wind conditions and calculating the capacity of the proposed wind farm, a 600 MW offshore hydrogen platform was designed along with a pipeline to transport hydrogen to onshore storage facilities. Taking into account Poland’s Baltic Sea area wind conditions with capacity factor between 45 and 50% and having obtained results with highest monthly average output of 3508.85 t of hydrogen, it should be assumed that green hydrogen production will reach profitability most quickly with electricity from offshore wind farms.

Wind Fields in Category 1–3 Tropical Cyclones Are Not Fully Represented in Wind Turbine Design Standards

AbstractOffshore wind energy deployment in the U.S. is expected to increase in the years to come, with proposed wind farm sites located in regions with high risk for tropical cyclones. Yet, the wind turbine design criteria outlined by the International Electrotechnical Commission for extreme events may not account for wind field characteristics unique to tropical cyclones. To evaluate if current design standards capture the extreme conditions of these storms, we perform idealized large‐eddy simulations of three tropical cyclones (one Category 1, one Category 2, and one Category 3 storms) using the Weather Research and Forecasting model. Wind conditions near the eyewall of Cat‐1, Cat‐2, and Cat‐3 storms can exceed current design standards for offshore wind turbines. Winds at 90 m can be faster than outlined in the design criteria for Class I and Class T turbines for 50‐year recurrence periods. Wind speed shear across the turbine rotor layer is also larger than assumed in design specifications. Moreover, vertical variations in wind direction across the turbine rotor layer are large for tropical cyclones of all intensity levels, suggesting design standards should include veer, which can amplify loads in wind turbines. The existence of hurricane wind field characteristics that are not represented in design standards does not imply that damage or failure will certainly occur. Engineering safety factors incorporated in the design of the turbine's blades and structural components may prevent damage from occurring.

WIND RESOURCE ASSESSMENT IN SOUTHERN ALBANIA: DATA COMPARISONS FROM THE BALKAN AND NEW EUROPEAN WIND ATLASES

This article describes a new method to more accurately evaluate the wind potential of 5 areas of interest in southern Albania. Using the WAsP simulation program, the authors were able to compare the wind data from the Balkan Wind Atlas (BWA) and the New European Wind Atlas (NEWA) to real wind measurements in Mamaj, Tepelenë, and showed that NEWA is closer to real wind conditions in Albania than BWA. Furthermore, by analyzing wind speed, power density and wind direction, the authors illustrate BWA and NEWA match closely in the five areas of interest in Albania, although BWA tends to overestimate wind potential more. The wind power class was evaluated and four from the five areas of interest were deemed appropriate for establishing wind farms. The authors analyzed eight different wind turbine types and identified the wind turbine with the highest capacity factor for the region to be SG 2.1-114 wind turbine. The potential electricity production from the proposed wind farms was evaluated using the RETScreen Expert program. This research allows for experts to more confidently use wind atlas data to evaluate wind potential in the region of Southern Albania. Finally, these results confirm the electricity potential from building wind farms in the areas of interest, which will contribute to the fulfillment of energy demand in the country and reduce imports in the energy sector.

Techno-economic assessment of wind farm for sustainable power generation in Northern coastal region of Arabian sea

Northern coastal region of Arabian sea has an abundant wind potential available throughout the year which can be utilized for meeting the energy demands in this region. This study presents a techno-economic evaluation of a 100MW wind farm at Gwadar region of Pakistan which is ideally located at the shores of Arabian sea and holds great strategic and economic significance. Several feasibility studies have been undertaken on this location; however, this analysis is carried out using on-site wind data on an hourly basis as well as the most recent financial factors along with optimized wind farm design configuration. Empirical relationships are presented for analyzing the long-term economic feasibility of wind farm. Results indicate that annual energy production of 29,7859 MWh is possible with an optimized design of wind farm that will help to reduce 12,648 equivalent tons of CO2 emissions. Moreover, a positive net present value, levelized cost of electricity around 9.08 ¢/kWh, and payback period of approximately 4 years justify the economic viability of the proposed wind farm design at Gwadar region. This study will serve as a valuable reference for similar wind profile site locations in India and Iran. Moreover, this study presents long-term economic feasibility with site specific analysis which can provide prospects for investment in sustainable energy development and increase its utilization in developing countries.

Institutional acceptance of wildlife mitigation technologies for wind energy: The case of Israel

While the existing literature on the acceptability of renewable energy focuses primarily on stated preferences of individual acceptance, this study focuses on a socio-political dimension of acceptance by examining revealed preferences as evidenced by stakeholder interactions with in a regulatory process. Specifically, we examine the acceptability of technology designed to mitigate harm to wildlife: a Shutdown On Demand (SOD) technology. Taking a longitudinal approach, we review planning protocols covering four years of negotiations over this new mitigating technology in the case of a proposed wind farm to be located along a major international corridor for migrating birds. We develop three separate indicators of institutional acceptance and demonstrate how exposure to different types of uncertainties influenced acceptance of the technology over time. We also show how various responses to these uncertainties can resolve gridlocks around institutional acceptance. The study offers insights into the development of negotiations strategies for different stakeholders over time. It also offers recommendations for policy designed to resolve issues inhibiting institutional acceptance, including data gathering and exchange, issue linkage strategies, and incremental regime building.

Boosting the annual profit of an offshore wind farm in India with a bio-inspired meta-heuristic scheme

Since the time of rapid industrial revolutions across the continents of the world, incessant emission of greenhouse gasses is severely deteriorating the quality of breathable air and causing abnormal increments in the surface air temperature. Global warming is a responsible factor for the melting of the polar ice caps, rising sea levels, uncharacteristic weather patterns, and immeasurable human distress throughout every corner of the planet. International communities are struggling to abate the global carbon footprint through the effective utilization of renewable resources of energy. Being the second most peopled realm in the globe, it turns out to be exceedingly momentous for India to resourcefully exploit its low-carbon sources of electricity like wind power to accomplish its net-zero emission ambition by 2070 while upholding the progress of its colossal economic structure. Although India presently sustains the fourth largest wind power generation competence in the world, it needs to develop its offshore wind power generation capacity rapidly for keeping up with its emission-cut aims. This paper attempts for augmenting the financial profitability of an offshore wind power generation system in the Bay of Khambhat near the seashore of Gujarat, India. An innovative modification of the Genetic Algorithm has been proposed to increase the annual profit of the proposed wind farm. The optimization trial outcomes authenticate the pre-eminence of the projected dynamic procedure for apportioning the factors of crossover and mutation functions over the standard static methods for assigning those proportions of the Genetic Algorithm.

Wind resource assessment and techno-economic analysis of wind energy and green hydrogen production in the Republic of Djibouti

The ever increasing energy demand of the Republic of Djibouti leads to the diversification of energy sources. While a few studies have explored the prospects of green hydrogen production from wind energy in developing countries and particularly in Africa, the economic risk analysis of wind power production for electricity generation and green hydrogen production has not been assessed for African countries. This study evaluates for the first time the potential of wind energy for electricity and green hydrogen production in the Republic of Djibouti. In this study, wind speed characteristics were analyzed using wind data measured at five meteorological stations from 2015 to 2019. The techno-economic analysis of five wind farms with a total capacity of 450 MW is performed. Levelized cost of energy production (LCOE), levelized cost of green hydrogen production (LCOH), sensitivity analysis, Monte Carlo simulation and economic performance indicators are presented. Results reveal that the annual wind speed varies between 5.52 m/s and 9.01 m/s for the five sites. ERA5 wind reanalysis indicates that the seasonal variability of wind is stable between different years. The proposed wind farms estimate 1739 GWh per year of electrical energy with LCOE ranging from 6.94 to 13.30 US cents/kWh, which is less than the locale electricity tariff. The production cost of green hydrogen was competitive with LCOH ranging from 1.79 to 3.38 US $/kg H2. The sensitivity analysis shows that the most relevant parameters in the economic analysis are the initial investment cost, the interest rate and the factor capacity.

Wind farm predictive maintenance considering component level repairs and economic dependency

Existing studies on wind farm predictive maintenance mostly incorporated repairs on the level of wind turbine (WT) subassemblies, e.g. drive train, gearbox and generator, including major repairs and minor repairs. The economic dependency among subassemblies was also considered. But assumptions needed to be made on major and minor repairs regarding their costs and impact on system functions. Besides, existing studies considered only fixed costs on the WT and farm levels without differentiating fixed costs that apply to a subset of components, such as crane costs, resulting in over-simplification compared to wind farm practices. In this work, a wind farm predictive maintenance approach is developed considering component level repairs and economic dependency. There are multiple components in a subassembly, e.g. the generator consists of components like generator rotor, generator bearings, contactor, etc. These components' failures lead to major or minor repairs, downtime, and demand on repair resources, particularly cranes. With the proposed wind farm predictive maintenance approach, component level major and minor repairs and their costs can be modeled explicitly in a more realistic and accurate way. Economic dependency can also be modeled more accurately by incorporating the downtime, i.e. revenue loss, and repair resource requirement caused by different component failures. A predictive maintenance optimization model is developed to find the optimal maintenance policy. The number of WTs eligible for preventive maintenance is introduced as a new decision variable, which also captures the economic dependency among WTs. A simulation-based method is developed for maintenance cost evaluation. Examples are used to demonstrate the proposed approach.

Intelligent Optimized Wind Turbine Cost Analysis for Different Wind Sites in Jordan

Choosing the right wind site and estimating the extracted energy of the wind turbines are essential to successfully establishing a wind farm in a specific wind site. In this paper, a method for estimating the extracted energy of the wind farms using several mathematical models is proposed. The estimating method, which was based on five wind turbines, Q1, Q2, Q3, Q4, and Q5 and three wind distribution models, gamma, Weibull, and Rayleigh, was used to suggest suitable specifications of a wind turbine for a specific wind site and maximize the extracted energy of the proposed wind farm. An optimization problem, developed for this purpose, was solved using the whale optimization algorithm (WOA). The suggested method was tested using several potential wind sites in Jordan. The proposed wind farms at these sites achieved the maximum extracted energy, maximum capacity factor (CF), and minimum levelized cost of energy (LCoE) based on the solution of the developed optimization problem. The developed model with Q3 and the Rayleigh distribution function was validated with real measurement data from several wind farms in Jordan. Error analysis showed that the difference between the measured and estimated energy was less than 20%. The study validated the provided model, which can now be utilized routinely for the assessment of wind energy potential at a specific wind site.

What explains the community acceptance of wind energy? Exploring benefits, consultation, and livelihoods in coastal Brazil

In the Global South, qualitative research has identified injustices arising from exclusionary community consultation, wide information gaps between host communities and decision makers, and high reliance among residents on land- and sea-based resources that may compete with renewable power infrastructure. Here we analyze results of a face-to-face survey applied in three communities hosting wind farms in Ceará state, northeastern Brazil. Results from three regression models for two dependent variables (“support wind farm” and “support more wind farms”) are reported. The comparative case study shows wide variation in support for wind farms among host communities with Benefits as a consistently significant independent variable, followed by Consultation and Environment variables. High support was observed in a community where a flawed consultation and construction process may have been partly overcome by mitigation funds that paid for new houses in one sub-community. Lowest support was found in a community with contested land tenure and a polemic consultation process, but the Benefits variable predicted increased odds of supporting a proposed wind farm. Support for wind farms was highest in a community where the wind investors negotiated royalties with landholders. The findings suggest that perceived or real economic benefits generated support for wind farms, especially when those benefits strengthened livelihoods and land-tenure security of host communities where livelihoods depended on fishing and farming and few employment opportunities exist. The varying consultation processes indicate that need for wind investors and state officials to improve community consultations.