Wind Farm Site Research Articles

Investigation of the importance of criteria in potential wind farm sites via machine learning algorithms

Wind energy has received greater attention than other energy resources due to its superior economics, low greenhouse gas emissions, and limitless wind resources. As a result, wind energy capacity has significantly increased, and the selection of the best locations for wind farms is an issue that has received extensive research. A significant step toward environmentally responsible land use planning is the site suitability assessment for the placement of wind farms. This study was conducted to determine the best locations for wind farms and to prioritize different locations and alternatives in the West of Turkey by using Maximum Entropy (MaxEnt) and Logistic Regression (LR) Methods based on Geographic Information Systems (GIS). Eight criteria were selected for creating the suitability map: air density, power density, wind speed, capacity factor, elevation, slope, aspect, and land use. Both methods were effective at choosing locations for wind farms because all the results were statistically significant in the consistency tests. MaxEnt calculated the potential wind energy fields with high accuracy and reliability with 0.915 AUC and LR multiple R square values of 0.883. Compared to the current installed power values, the MaxEnt analysis results were more consistent with the recent status. İzmir has been calculated as the province with the highest potential for wind energy area of 663 km2 by MaxEnt and 620.4 km2 by LR.

Offshore wind farm site selection in Norway: Using a fuzzy trigonometric weighted assessment model

Maximising the energy potential of offshore wind farms requires an in-depth assessment of technological, economic, sociopolitical, and environmental aspects. Given the large economic impact of large-scale projects, a robust site selection procedure is critical for limiting financial risks while supporting informed investments. This research uncovers a novel and multidisciplinary approach for boosting the efficacy of Norwegian and global offshore wind farm siting investments. The proposed method uses a two-stage fuzzy mathematical model that considers technical, economic, logistical, and environmental factors. It combines the Ordinal Priority Approach (F-OPA) and Trigonometric Weighted Assessment (TRWA) technique by using an in-depth techno-economic assessment. An alternative reactive power compensation model, power loss calculations, and associated techno-economic analysis were performed for the investigated offshore wind farm locations. Furthermore, the energy economic calculations are carried out to provide support for the proposed decision-making framework. The proposed methodology was tested through a case study, focusing on ranking Norwegian offshore wind farm sites selected from potential locations announced by The Norwegian Water Resources and Energy Directorate (NVE). Within the Norwegian offshore wind farm sites, the approach demonstrated a versatile and efficient decision-making process at both individual and collective levels, identifying the Sandskallen-Sørøya Nord project as a pivotal investment priority and providing valuable managerial insights to enhance Norway’s offshore wind initiatives. The model’s stability was affirmed through a sensitivity analysis, underscoring its potential to enhance renewable energy policy and decision-making globally.

Spatial multi-criteria decision making approach for wind farm site selection: A case study in Balıkesir, Turkey

This study presents a spatial multi-criteria decision making (SMCDM) approach based on geographic information system (GIS) for determining the sites that have potential for wind farm installation in Balıkesir, Turkey. Topographical, structural, climatic, and environmental criteria consisting of wind speed, land cover type, slope of terrain, and distance from substations, road networks, settlements, fault lines, electricity grid, and seaports are considered in the approach. Weights, derived from evaluation involving the use of the analytical hierarchy process (AHP) questionnaire, and scored criteria maps, are used in creation of a wind farm suitability map. The results of the study demonstrate that 2.34 % of the total area under consideration is in the most suitable class, while 9.34 % is in the next most suitable class. The study introduces the seaport criterion for the first time in onshore wind turbine site selection. The findings show that considering the seaport criterion provides a more realistic approach of feasible wind turbine site selection. This is clearly seen in the 13 % decrease in the most suitable area. When the wind farm suitability map is evaluated on a district scale, Bandırma takes first place compared to other districts. A sensitivity analysis performed with equal criteria weights indicates that the results are generally reliable.

A hybrid AHP-PROMETHEE II onshore wind farms multicriteria suitability analysis using kNN and SVM regression models in northeastern Greece

Wind energy presents a high growth potential in the EU as an emission reduction strategy and to achieve the climate neutrality goal by 2050. Wind farms suitability analysis is one of the primary goals in the spatial planning of wind energy developments. This research paper introduces a hybrid spatial multicriteria GIS-based framework that combines Analytic Hierarchy Process (AHP), PROMETHEE II and Machine Learning algorithms to determine and predict the most efficient onshore wind farm locations by generating suitability index mappings. The methodology allows to overcome PROMETHEE II limitations in raster driven suitability analysis, utilizing machine learning regression methods as the k Nearest Neighbor and Support Vector Machines to predict a graduating mapping of suitability index for wind farm locations in northeastern Greece. The best configured models presented a RMSE of 0.0344 and 0.0154 respectively, indicating a quite high predictive performance. Suitability results indicate that 56.10 % of the feasible locations in the Thrace area present a positive outranking character for the kNN model and 56.79 % for the SVR model. The proposed framework, enriched by PROMETHEE II capabilities, assists energy and spatial planners in identifying suitable sites for wind farm siting and enables rational decision making that enhances efficient wind energy investments.

Wind power access site selection method based on scenario probability

The fluctuation of new energy output makes the power system operation more complex, and the traditional power system tide calculation is no longer fully applicable. Therefore, the determination of wind farm output and site selection is important and fundamental research at present. Based on this, a combination of wind farm output scenario and probability is proposed. The site selection model with the minimum network loss of the distribution network as the optimization objective is proposed. First, the wind farm output is clustered into several typical scenarios by K-Means, and the output time series model and the probability of each typical scenario are obtained. The probabilities of each scenario are multiplied by the network losses under that scenario. Then, the network losses obtained for all individual scenarios are summed up and used as the minimum objective function. The model is solved by a genetic algorithm with tidal current constraint and node voltage constraint to derive the optimal wind farm access point. With the IEEE33 node, simulations are performed to derive the minimum value of the daily average network loss, which verifies the rationality and feasibility of the optimized model.

Geospatial analysis of wind indicators and terrain impacts in determining optimal wind farm sites in Azerbaijan

Geospatial analysis of wind indicators and terrain impacts in determining optimal wind farm sites in Azerbaijan

Multi-objective turbine allocation on a wind farm site

The Multi-Objective Turbine Allocation (MOTA) method is introduced as a novel approach for wind farm layout optimization and site selection. By incorporating Geographic Information System (GIS) tools and the Analytical Hierarchy Process (AHP), the MOTA method offers a comprehensive solution to balance energy production, cost factors, and environmental impacts. In this study, the MOTA method is applied to Gökçeada, Türkiye, for wind farm development. Results show that the MOTA method effectively proposes the optimum wind farm layout by selecting the best site for each turbine. The sequential turbine allocation approach, integration of multiple objectives, and use of GIS tools and AHP are the key capabilities and novelties of the MOTA method. The method allows for flexible investment decisions, considering technical and economic aspects. The outcomes from the Gökçeada case study highlight the effectiveness of the MOTA method in maximizing energy production while considering cost factors and environmental impacts. The results indicate that for the selected objective functions, the optimal net profit is attained with the installation of 155 turbines on Gökçeada. The MOTA method presents a practical and efficient solution for wind farm development, contributing to sustainable and efficient renewable energy generation.

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.

Wind resource assessment and potential development of wind farms along the entire coast of South Korea using public data from the Korea meteorological administration

The feasibility of a wind farm locations should be evaluated on a thorough analysis and assessment of the available wind resources which necessitates wind data measurement for a minimum period of one year. Nevertheless, several limitations arise during this process, including challenges related to the site selection, cost considerations such as the installation of meteorological masts subsequent to marine physical and geotechnical surveys, and the requirement for ongoing maintenance. Therefore, several studies have been carried out to determine wind energy potential using reanalysis data. However, in areas with distinct terrain characteristics, such as maritime and coastal areas, uncertainties exist in the use of reanalysis data. Therefore, an analysis of wind resources based on measurement data is necessary in such areas. In this study, wind data measured by the Korea Meteorological Administration's automatic weather system and light house automatic weather system for 56 points along the Korean coast is obtained and analyzed. Additionally, the expected annual energy production and capacity factor were predicted when offshore wind turbines are installed using the power curve of a reference model of large offshore wind turbines in the 15–20 MW class. As a results, five points at which a capacity factor of over 35% is predicted as promising sites for future wind farm development. These public data from the Korea Meteorological Administration are also limited in their ability to provide accurate wind resource analysis for wind power generation due to the wind speed measurement instrument's height being lower than that of the wind turbine hub and environmental influences. However, these results can be useful in identifying possible candidate sites for wind farms in South Korea.

Data-driven optimization for wind farm siting

Both increasing power output and minimizing the variability of that output are key factors in a highly utilized wind farm. Finding a location that offers this type of wind resource requires the use of stochastic methods to explicitly model the uncertainty of wind power output. While the literature is rich with wind speed resource assessments, this work moves the field to wind farm power assessment at undeveloped locations. A wind farm power prediction is achieved with improved spatial process representation. The power prediction results are utilized directly in a wind farm location siting optimization model which endogenously incorporates uncertainty in wind power output. The overall framework for siting new wind farms, termed the Farm Finder tool, is demonstrated in a case study in the Electric Reliability Council of Texas (ERCOT). It is shown that the formulation of this model will find an optimal solution to the siting problem on the Pareto front of high power output, with high reliability. Such location decisions improve the overall social welfare in the electricity market.

A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset

Abstract. Peak wind gust (Wp) is a crucial meteorological variable for wind farm planning and operations. However, for many wind farm sites, there is a dearth of on-site measurements of Wp. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset and, in turn, generates multi-year, site-specific Wp series. Through a systematic feature importance study, we also identify the most relevant meteorological variables for Wp estimation. The INTRIGUE approach outperforms the baseline predictions for all wind gust conditions. However, the performance of this proposed approach and the baselines for extreme conditions (i.e., Wp>20 m s−1) is less satisfactory.

Norwegian offshore wind power—Spatial planning using multi‐criteria decision analysis

AbstractThe Norwegian government recently agreed on the goal 30by40, which involves opening Norwegian offshore areas to host 30 GW of installed wind power by 2040. We address this goal by presenting a first mapping of wind power suitability scores (WPSS) for the entire Norwegian economic zone (NEZ) using a multi‐criteria decision analysis framework (MCDA), namely, the analytical hierarchical process (AHP) approach. We obtain WPSS considering relevant criteria like wind resources, techno‐economic aspects, social acceptance, environmental considerations, and met‐ocean constraints such as wind and wave conditions. The results starts with a baseline scenario, where the criterion importance is pairwise compared in the context of balancing economic incentives and conflicting interests. Additionally, to reveal regions that are robust to changes in criterion importance, we carry out a sensitivity analysis by introducing three additional scenarios. These scenarios represent stereotypical actors with distinct preferences for siting of wind farms: the investor, the environmentalist, and the fisherman. The results show that the southern part of the NEZ is the most suitable and robust region for offshore wind power deployment. This region receives the highest suitability category (“very high” suitability for wind power application) throughout all the scenarios. Areas in the Norwegian part of the Barents Sea and the near‐coastal areas outside mid‐Norway are also well suited regions, but these are more sensitive to the choice of criterion importance. The use of AHP within the framework of MCDA is shown to be a promising tool for pinpointing the best Norwegian offshore areas for wind power application.

Stochastic modelling of wind speed over northern states in Nigeria

This paper presents a stochastic modeling of wind speed over sixteen (16) Northern states in Nigeria using thirty-seven years (1984-2020) wind speed real time data. A Markov Chain Model was developed for the monthly wind speed state across study locations. In order to obtain the Markov chain transitional probabilities, the wind speed data was categories into various states using the Beaufort wind scale. It was observed that only the first four description of wind speed state A, B, C and D exist in the study locations. Uniform random states were also formed by generating uniform random number. The comparison of monthly simulated and actual wind speed state clearly shows that the model simulated over six months correctly across study locations except Niger. Given a current wind speed state conditions, the stochastic models available in this paper can be adapted to generate future wind speed state condition. The understanding of wind speed state helps in wind turbine design and selection of wind farm sites for wind energy generation.

The status of breeding Red-throated Divers Gavia stellata and Black-throated Divers Gavia arctica in Kintyre, Scotland, 2016–2020

ABSTRACT Capsule An onshore wind farm monitoring programme carried out across Kintyre, Scotland from 2016 to 2020 provided an insight into the status of the area’s breeding Red-throated Diver Gavia stellata and Black-throated Diver Gavia arctica populations and their current threats. Aims This study provides an example of targeted monitoring undertaken for a renewables development which can inform conservation decision-making at a population and species level. The aims were to: (i) determine the population status of the breeding diver populations; (ii) determine whether the Cour Wind Farm, or other wind farms in Kintyre, have had any effects on the populations; and (iii) identify the most likely conservation threats to breeding divers within Kintyre. Methods Annual breeding diver surveys across Kintyre recorded presence, breeding activity and productivity. Within the Cour Wind Farm site carcass searches were conducted to record any incidences of diver collisions with wind turbines. Results Divers were recorded on approximately one third of waterbodies monitored in each year. The current Kintyre breeding populations are estimated to be 18–23 Red-throated Diver pairs, and between two and six Black-throated Diver pairs. The Kintyre Red-throated Diver population appears to have lower productivity rates than the UK average. Predation and interspecific competition may be limiting breeding success. No Black-throated Divers were observed to have successfully bred in Kintyre from 2016 to 2020, which may be partly due to a shortage of waterbodies with preferred conditions for nesting. No diver collisions were recorded during carcass searches and there is no evidence to suggest that the Cour Wind Farm is influencing the numbers or distribution of divers. Conclusion The Kintyre Red-throated Diver population may be, and the Black-throated Diver population is likely to be, in unfavourable condition. Opportunities for conservation should be considered, particularly to manage waterbodies so that they are optimal for breeding.

Predicting commercial wind farm site suitability in the conterminous United States using a logistic regression model

Guiding wind energy sector growth through suitability analysis is a growing priority. We present in this work a logistic regression model that predicts suitable sites for state-level and nationwide wind energy development in the United States. The model's aggregation of publicly available data to 20 different grid cell resolutions, along with four predictor configurations, allows end-users to investigate commercial wind farm site suitability for their region, project size, and predictors of interest. Model performance maximizes at higher grid cell resolutions and when using a complete and/or refined predictor set. Validation of the model's performance against existing commercial wind farm locations demonstrates its ability to consistently diagnose over 75% of grid cell states correctly. As such, high suitability grid cells that currently lack wind farms could represent candidate locations for wind farm construction. This model and its aggregated datasets can be applied in other suitability analysis contexts, particularly solar energy development.

Vessel noise prior to pile driving at offshore windfarm sites deters harbour porpoises from potential injury zones

Efforts to meet climate change targets are resulting in rapid and global expansion of offshore windfarms. In many regions, development areas are also used by protected marine mammals, requiring the assessment and mitigation of any risk of injury during construction and operation. For small cetaceans such as the harbour porpoise, there is particular concern over the risk of injury from impulsive noise should individuals remain within near-field injury zones during the installation of pile driven turbine foundations. Currently, this risk is assessed by comparing predicted noise levels at the start of piling with baseline estimates of animal density, which are, in turn, based on data collected at least one year earlier. However, vessel-based preparation work immediately prior to piling may displace animals, thus reducing any risk of injury when pile-driving begins. We investigated the effects of pre-piling activities on local soundscapes and harbour porpoise occurrence during the construction of two deep-water offshore windfarms in NE Scotland. Arrays of echolocation click detectors deployed at a sub-set of turbine sites were used to assess porpoise occurrence within a 5 km buffer during a 48-h period prior to the initiation of piling. In parallel, we characterised local vessel activity using AIS data and underwater broadband noise levels. We then used daily engineering records to characterise variation in construction activities and explore how porpoise occurrence varied during the 48 h prior to piling. On average, vessels arrived onsite 11–15 h before the start of pile-driving activities at both windfarms. In both installation campaigns, harbour porpoise acoustic detection gradually declined by up to 33% during the 48 h prior to piling. This decrease in detections was associated with increased levels of vessel and pre-piling installation activities, and increased local underwater broadband noise levels. These results provide strong evidence of porpoise displacement prior to active mitigation activities, highlighting the need to account for disturbance from multiple sources when optimising mitigation measures aimed at reducing impacts of windfarm construction on protected marine mammal populations.

Identification of optimal sites for the deployment of wave energy converters: the importance of a technology-centred approach

Driven by climate issues and geopolitical uncertainties, Europe faces the need to transform its energy supply dramatically and quickly. Various renewable technologies are proposed as a medium- to long-term solution for an environmentally and economically sustainable energy mix: among the available solutions, wave energy converters (WECs) are attracting growing interest due to the large untapped wave energy potential in European seas. In this context, the choice of optimal locations for the use of wave energy is fundamental to limit the technological gap with other fully developed conversion technologies, and to ensure competitive energy costs. In this paper, we compare different possible strategies to identify suitable sites for the installation of WECs, namely the one based on pure analysis of the wave energy resource, and that considering the productivity of the device in different sea states, i.e., its power matrix. Using the performance matrices of notional WECs, particularly an Oscillating Surge Wave Energy Converter (OSWEC) and a Heaving Point Absorber (HPA), we estimate optimal locations on the Italian coasts and highlight the advantages and disadvantages of the two approaches. The analysis shows the importance of a technology-based approach for the spatial planning of future wave power plants and highlights significant differences compared to the approach that can be used for the preliminary identification of sites for wind farms and, especially, for photovoltaic plants. We use the obtained results to introduce the MORE-EST platform, a novel web-based tool for straightforward estimation of wave resources and WECs productivity in European seas. The proposed platform is able to integrate information on wave resource assessment, bathymetry, marine space use and technological features, and represents a tool aimed at researchers, WECs developers, and policy makers.

NEAL: an open-source tool for audio annotation.

Passive acoustic monitoring is used widely in ecology, biodiversity, and conservation studies. Data sets collected via acoustic monitoring are often extremely large and built to be processed automatically using artificial intelligence and machine learning models, which aim to replicate the work of domain experts. These models, being supervised learning algorithms, need to be trained on high quality annotations produced by experts. Since the experts are often resource-limited, a cost-effective process for annotating audio is needed to get maximal use out of the data. We present an open-source interactive audio data annotation tool, NEAL (Nature+Energy Audio Labeller). Built using R and the associated Shiny framework, the tool provides a reactive environment where users can quickly annotate audio files and adjust settings that automatically change the corresponding elements of the user interface. The app has been designed with the goal of having both expert birders and citizen scientists contribute to acoustic annotation projects. The popularity and flexibility of R programming in bioacoustics means that the Shiny app can be modified for other bird labelling data sets, or even to generic audio labelling tasks. We demonstrate the app by labelling data collected from wind farm sites across Ireland.

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.

Bird surveys at windfarms in the Eastern Cape, South Africa

Bird surveys were conducted at four windfarm sites and four control sites in the Eastern Cape, South Africa. Numbers of the Blue Crane Grus paradisea decreased at three out of four windfarm sites after commencement of windfarm activities and did not decrease at any of the control sites. Numbers for several bird species of the fynbos and grassland tended to increase at windfarm sites after commencement of windfarm activities, and did not increase at the control sites.