This layer displays several key factors relevant to the siting of large-scale wind power and combines them, demonstrating the “suitability” of regions within Michigan to this technology. The purpose of this tool is to aid proactive planning and zoning for local governments by highlighting the quantity and location of areas of potential development interest. These models were based on the Geospatial Energy Mapper (GEM) but have been enhanced with higher-resolution data and modifications tailored to Michigan's landscape. The key factors involved are average annual wind speed, land use type, distance to substations, distance to major roads, and population density. For land use type, each type was assigned a suitability score by the creators. These four factors are then weighted based on total relevance to energy development, also assigned by the creators, to ultimately produce a total suitability score. The creators determined land use type scores and weighting based on research, peer feedback, and general experience with energy siting. An additional layer of high voltage transmission lines is included for further relevant context, though this is not involved in the suitability score.Model Parameters (Modified upon GEM models): Land-Based Wind Turbine (Wind)

Parameters

Weight

Distance to Major Road

1

Land Cover

3

Population Density

2

Mean Annual Wind Speed

5

Distance to substation

2

Data Sources and Model Weighting:Land Cover (Multi-Resolution Land Characteristics (MRLC) Consortium – CONUS 2021) GEM - wind REA - wind SUITABILITYRANGE/CLASSSUITABILITYRANGE/CLASS100Unclassified (0)0Unclassified (0)1Open Water (11)0Open Water (11)10Perennial Snow/Ice (12)0Perennial Snow/Ice (12)75Developed, Open Space (21)0Developed, Open Space (21)75Developed, Low Intensity (22)0Developed, Low Intensity (22)75Developed, Medium Intensity (23)0Developed, Medium Intensity (23)75Developed, High Intensity (24)0Developed, High Intensity (24)100Barren Land (31)50Barren Land (31)50Deciduous Forest (41)20Deciduous Forest (41)50Evergreen Forest (42)20Evergreen Forest (42)50Mixed Forest20Mixed Forest90Shrub/Scrub (52)100Shrub/Scrub (52)100Herbaceous (71)100Herbaceous (71)90Hay/Pasture (81)100Hay/Pasture (81)90Cultivated Crops (82)100Cultivated Crops (82)40Woody Wetlands (90)0Woody Wetlands (90)Mean Annual Wind Speed at 100m (meters/second) (NREL U.S. Multiyear Average Wind Speeds at All Heights)SUITABILITYRANGE/CLASS10 to 3 203 to 4504 to 5755 to 6856 to 7957 to 81008 to 9100Over 9Distance to Major Roads (TIGER/Line Shapefile, 2023, State, Michigan, Primary and Secondary Roads)SUITABILITYRANGE/CLASS00 - 0.1 miles1000.1 - 5 miles855 - 10 miles7510 - 15 miles6015 - 20 miles4020 - 50 miles1050 - 75 miles5Over 75 milesDistance to Substation (220 to 345kV) (ArcGIS Substations)SUITABILITYRANGE/CLASS1000 - 1 miles901 - 5 miles755 - 10 miles50Over 10 milesPopulation Density (GPW v4 Population Density)SUITABILITYRANGE/CLASS75101 - 15050151 - 20025201 - 3000301 and higher0No Data

For any questions, please contact Ian O’Leary at olearyi@michigan.gov, or reference the Renewable Energy Academy website to see how EGLE offers technical assistance for renewable energy.

Discover the booming solar resource assessment software market! Our analysis reveals a $250 million market in 2025, growing at a 12% CAGR to 2033. Learn about key trends, leading companies like SolarGIS and Solargis, and regional market share data for North America, Europe, and more. Invest wisely in the renewable energy revolution.

This Wind Generation Interactive Query Tool created by the CEC. The visualization tool interactively displays wind generation over different time intervals in three-dimensional space. The viewer can look across the state to understand generation patterns of regions with concentrations of wind power plants. The tool aids in understanding high and low periods of generation. Operation of the electric grid requires that generation and demand are balanced in each period. The height and color of columns at wind generation areas are scaled and shaded to represent capacity factors (CFs) of the areas in a specific time interval. Capacity factor is the ratio of the energy produced to the amount of energy that could ideally have been produced in the same period using the rated nameplate capacity. Due to natural variations in wind speeds, higher factors tend to be seen over short time periods, with lower factors over longer periods. The capacity used is the reported nameplate capacity from the Quarterly Fuel and Energy Report, CEC-1304A. CFs are based on wind plants in service in the wind generation areas.Renewable energy resources like wind facilities vary in size and geographic distribution within each state. Resource planning, land use constraints, climate zones, and weather patterns limit availability of these resources and where they can be developed. National, state, and local policies also set limits on energy generation and use. An example of resource planning in California is the Desert Renewable Energy Conservation Plan. By exploring the visualization, a viewer can gain a three-dimensional understanding of temporal variation in generation CFs, along with how the wind generation areas compare to one another. The viewer can observe that areas peak in generation in different periods. The large range in CFs is also visible.

Annual average wind resource potential for North and South Dakota at a 50 meter height in a GIS shapefile and links to US national wind resource information tools. This data set was produced and validated by NREL using their WRAM model.

This is the data set behind the Wind Generation Interactive Query Tool created by the CEC. The visualization tool interactively displays wind generation over different time intervals in three-dimensional space. The viewer can look across the state to understand generation patterns of regions with concentrations of wind power plants. The tool aids in understanding high and low periods of generation. Operation of the electric grid requires that generation and demand are balanced in each period.

The site suitability criteria included in the techno-economic land use screens are listed below. As this list is an update to previous cycles, tribal lands, prime farmland, and flood zones are not included as they are not technically infeasible for development. The techno-economic site suitability exclusion thresholds are presented in table 1. Distances indicate the minimum distance from each feature for commercial scale wind developmentAttributes: Steeply sloped areas: change in vertical elevation compared to horizontal distancePopulation density: the number of people living in a 1 km2 area Urban areas: defined by the U.S. Census. Water bodies: defined by the U.S. National Atlas Water Feature Areas, available from Argonne National Lab Energy Zone Mapping Tool Railways: a comprehensive database of North America's railway system from the Federal Railroad Administration (FRA), available from Argonne National Lab Energy Zone Mapping Tool Major highways: available from ESRI Living Atlas Airports: The Airports dataset including other aviation facilities as of July 13, 2018 is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). The Airports database is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product. Available from Argonne National Lab Energy Zone Mapping Tool Active mines: Active Mines and Mineral Processing Plants in the United States in 2003Military Lands: Land owned by the federal government that is part of a US military base, camp, post, station, yard, center, or installation. Table 1

Wind

Steeply sloped areas

10o

Population density

100/km2

Capacity factor

<20%

Urban areas

<1000 m

Water bodies

<250 m

Railways

<250 m

Major highways

<125 m

Airports

<5000 m

Active mines

<1000 m

Military Lands

<3000m

For more information about the processes and sources used to develop the screening criteria see sources 1-7 in the footnotes.

Data updates occur as needed, corresponding to typical 3-year CPUC IRP planning cyclesFootnotes:[1] Lopez, A. et. al. “U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis,” 2012. https://www.nrel.gov/docs/fy12osti/51946.pdf[2] https://greeningthegrid.org/Renewable-Energy-Zones-Toolkit/topics/social-environmental-and-other-impacts#ReadingListAndCaseStudies[3] Multi-Criteria Analysis for Renewable Energy (MapRE), University of California Santa Barbara. https://mapre.es.ucsb.edu/[4] Larson, E. et. al. “Net-Zero America: Potential Pathways, Infrastructure, and Impacts, Interim Report.” Princeton University, 2020. https://environmenthalfcentury.princeton.edu/sites/g/files/toruqf331/files/2020-12/Princeton_NZA_Interim_Report_15_Dec_2020_FINAL.pdf.[5] Wu, G. et. al. “Low-Impact Land Use Pathways to Deep Decarbonization of Electricity.” Environmental Research Letters 15, no. 7 (July 10, 2020). https://doi.org/10.1088/1748-9326/ab87d1.[6] RETI Coordinating Committee, RETI Stakeholder Steering Committee. “Renewable Energy Transmission Initiative Phase 1B Final Report.” California Energy Commission, January 2009.[7] Pletka, Ryan, and Joshua Finn. “Western Renewable Energy Zones, Phase 1: QRA Identification Technical Report.” Black & Veatch and National Renewable Energy Laboratory, 2009. https://www.nrel.gov/docs/fy10osti/46877.pdf.[8]https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Urban+Areas[9]https://ezmt.anl.gov/[10]https://www.arcgis.com/home/item.html?id=fc870766a3994111bce4a083413988e4[11]https://mrdata.usgs.gov/mineplant/Credits

Title: Techno-economic screening criteria for utility-scale wind energy installations for Integrated Resource Planning

Purpose for creation: These site suitability criteria are for use in electric system planning, capacity expansion modeling, and integrated resource planning.

Keywords: wind energy, resource potential, techno-economic, IRP

Extent: western states of the contiguous U.S.

Use Limitations

The geospatial data created by the use of these techno-economic screens inform high-level estimates of technical renewable resource potential for electric system planning and should not be used, on their own, to guide siting of generation projects nor assess project-level impacts.Confidentiality: Public

ContactEmily Leslie Emily@MontaraMtEnergy.comSam Schreiber sam.schreiber@ethree.com Jared Ferguson Jared.Ferguson@cpuc.ca.govOluwafemi Sawyerr femi@ethree.com

The Geospatial Energy Mapper (GEM) provides mapping data and analysis tools for planning energy infrastructure in a geographic context. GEM is an interactive web-based decision support system that allows users to locate areas with high suitability for clean power generation and potential energy transmission corridors in the United States. Users can browse and download data layers, or create a custom suitability model to identify areas for energy development. GEM is built on the core data and capabilities of the Energy Zones Mapping Tool (EZMT). GEM features an improved user interface, updated data, and additional capabilities. Argonne National Laboratory hosts the tool with funding from the U.S. Department of Energy (DOE) Office of Electricity.This model is specific to land-based wind turbines (120 m). We modified the default model parameters to remove the habitat and protected areas elements. As a result, this layer represents the threat of energy development from a utility perspective and does not consider whether development is suitable from a conservation perspective. We considered a threat-only layer to be more complementary to the Southeast Blueprint, which already depicts conservation priorities. In addition, the source data for the habitat and protected areas elements were outdated and provided inconsistent coverage across the SECAS geography. We kept the default weights for all remaining model input layers as shown in the bulleted list below. The default and customizable models are downloadable from the GEM viewer. These data are provided for use in combination with the Blueprint and other data available on the SECAS Atlas. We chose the 120 m model (rather than the 80 m or 100 m models) based on the overall trend of increasing turbine size, and the reduction in wind shear and increase in available wind speed at higher altitudes (Department of Energy 2023).Distance in Meters to an Airport (weight = 2)Population Density (weight = 1)Distance (m) to Substation (≥ 345 kV) (weight = 3)Distance in Meters to Major Road (weight = 1)Land Cover (NLCD 2019) (weight = 1)Mean Annual Wind Speed (Land-based at 120 m) (weight = 5)For more information on these model parameters or to view and download this layer from its native mapper:Visit https://gem.anl.gov/toolSelect "Find suitable areas" from the sidebar on the leftChoose wind technologyChoose the Land-based wind turbine (120 m) modelView/download the default model or customize as described abovePlease direct any questions to gem@anl.gov.

This data was prepared as input for the Selkie GIS-TE tool. This GIS tool aids site selection, logistics optimization and financial analysis of wave or tidal farms in the Irish and Welsh maritime areas. Read more here: https://www.selkie-project.eu/selkie-tools-gis-technoeconomic-model/

This research was funded by the Science Foundation Ireland (SFI) through MaREI, the SFI Research Centre for Energy, Climate and the Marine and by the Sustainable Energy Authority of Ireland (SEAI). Support was also received from the European Union's European Regional Development Fund through the Ireland Wales Cooperation Programme as part of the Selkie project.

File Formats

Results are presented in three file formats:

tif Can be imported into a GIS software (such as ARC GIS) csv Human-readable text format, which can also be opened in Excel png Image files that can be viewed in standard desktop software and give a spatial view of results

Input Data

All calculations use open-source data from the Copernicus store and the open-source software Python. The Python xarray library is used to read the data.

Hourly Data from 2000 to 2019

• Wind - Copernicus ERA5 dataset 17 by 27.5 km grid
  10m wind speed

• Wave - Copernicus Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis dataset 3 by 5 km grid

Accessibility

The maximum limits for Hs and wind speed are applied when mapping the accessibility of a site.
The Accessibility layer shows the percentage of time the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5) are below these limits for the month.

Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined by checking if
the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total number of hours for the month.

Environmental data is from the Copernicus data store (https://cds.climate.copernicus.eu/). Wave hourly data is from the 'Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis' dataset.
Wind hourly data is from the ERA 5 dataset.

Availability

A device's availability to produce electricity depends on the device's reliability and the time to repair any failures. The repair time depends on weather
windows and other logistical factors (for example, the availability of repair vessels and personnel.). A 2013 study by O'Connor et al. determined the
relationship between the accessibility and availability of a wave energy device. The resulting graph (see Fig. 1 of their paper) shows the correlation between accessibility at Hs of 2m and wind speed of 15.0m/s and availability. This graph is used to calculate the availability layer from the accessibility layer.

The input value, accessibility, measures how accessible a site is for installation or operation and maintenance activities. It is the percentage time the
environmental conditions, i.e. the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5), are below operational limits.
Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined
by checking if the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total
number of hours for the month. Once the accessibility was known, the percentage availability was calculated using the O'Connor et al. graph of the relationship between the two. A mature technology reliability was assumed.

Weather Window

The weather window availability is the percentage of possible x-duration windows where weather conditions (Hs, wind speed) are below maximum limits for the
given duration for the month.

The resolution of the wave dataset (0.05° × 0.05°) is higher than that of the wind dataset
(0.25° x 0.25°), so the nearest wind value is used for each wave data point. The weather window layer is at the resolution of the wave layer.

The first step in calculating the weather window for a particular set of inputs (Hs, wind speed and duration) is to calculate the accessibility at each timestep.
The accessibility is based on a simple boolean evaluation: are the wave and wind conditions within the required limits at the given timestep?

Once the time series of accessibility is calculated, the next step is to look for periods of sustained favourable environmental conditions, i.e. the weather
windows. Here all possible operating periods with a duration matching the required weather-window value are assessed to see if the weather conditions remain
suitable for the entire period. The percentage availability of the weather window is calculated based on the percentage of x-duration windows with suitable
weather conditions for their entire duration.The weather window availability can be considered as the probability of having the required weather window available
at any given point in the month.

Extreme Wind and Wave

The Extreme wave layers show the highest significant wave height expected to occur during the given return period. The Extreme wind layers show the highest wind speed expected to occur during the given return period.

To predict extreme values, we use Extreme Value Analysis (EVA). EVA focuses on the extreme part of the data and seeks to determine a model to fit this reduced
portion accurately. EVA consists of three main stages. The first stage is the selection of extreme values from a time series. The next step is to fit a model
that best approximates the selected extremes by determining the shape parameters for a suitable probability distribution. The model then predicts extreme values
for the selected return period. All calculations use the python pyextremes library. Two methods are used - Block Maxima and Peaks over threshold.

The Block Maxima methods selects the annual maxima and fits a GEVD probability distribution.

The peaks_over_threshold method has two variable calculation parameters. The first is the percentile above which values must be to be selected as extreme (0.9 or 0.998). The second input is the time difference between extreme values for them to be considered independent (3 days). A Generalised Pareto Distribution is fitted to the selected
extremes and used to calculate the extreme value for the selected return period.

Many countries worldwide integrate renewable energy systems (RES) in their future energy plans to reduce the negative impacts of fossil fuel consumption and carbon emissions on the environment and have focused on sustainable energy options. The current need and challenges to use alternative energy sources are driven by the continued rise in fossil fuel prices, increasing population and migration, and energy demand, mainly in developing countries, such as South Africa. In addition, the continuous increase in energy demand, global warming, and other environmental problems related to the negative impact of using fossil fuels have raised severe global challenges. The solar, wind, hydro, and biomass resources and their potential to provide alternative energy sources have not been sufficiently utilised. As a result, RES is increasingly being considered as a potential solution for sustainable energy production and reduction of negative environmental impact. To obtain the suitable potentials, it is essential to assess, estimate, and model renewable energy resources in different locations to provide energy end-users, communities, the private sector, and decision makers with accurate, evaluated, and validated data to promote the construction of solar, wind, hydro, and biomass/bioenergy power plants. Furthermore, identifying suitable locations, the available capacity of renewable energy facilities, influencing factors of renewable energy development, and consumption play an essential role in planning renewable energy plants.The use of remote sensing (RS) technology and GIS tools enable detailed assessment, modelling, and quantification of RES distribution, abundance, and quality that yield an effective and efficient use of available potential. Therefore, determining the optimal locations, capacity and identifying the spatial influencing factors are essential in developing a scientific planning strategy with validated data. This research aims to create a GIS framework for evaluating alternative locations for wind, solar, biomass, biofuels, and hybrid power plants for suitable rural energy deployment. As renewable energy planning is essential, the model will be a valuable tool for decision support in spatial selection and explicit location planning strategies.In this study, the available energy potential measurements were developed using GIS and RS mappings as tools to assess renewable energy potentials in the Vhembe District Municipality from the perspective of spatial planning. The study's specific aims are to quantify and map the wind, solar, hydro, and bioenergy potential from a theoretical level, as well as environmental restrictions, and to analyse the suitability of the location for small power plants.For other regions, the proposed decision support methodology provides a multi-purpose approach for a complex exploration of RES potentials and their exploitation under specific environments and conditions. As a result, the methodology employed in this study can be used in other study areas to assess renewable energy potential in identifying new profitable regions based on the land suitability results that integrate spatial information from remote sensing. Lastly, from the results produced, the available potential can be used in the mapping process in other regions.

Supplementary files for article "A whole island approach to scoping renewable energy sites and yields"Island communities are particularly vulnerable to climate change and energy in?security; renewable energy can counter both threats. This study takes a whole is?land approach to scoping wind and solar energy potential. The Isle of Man (IOM) was selected because of the limited development of renewables to date, plus high reliance on energy imports. Potential sites for renewables development were eval?uated using social, environmental, technical, economic and political factors in a combined Geographic Information System (GIS)-multi-criteria decision analy?sis (MCDA). We find that 9% of the island is highly suitable for onshore wind development, and 2% for solar photovoltaic. These areas could potentially yield 107MW from onshore wind and 150MW from solar. Roof top and floating solar could add a further 30MW, and offshore wind 497MW. The total wind and solar renewables potential of onshore and offshore sites of 784MW is much greater than the historical (85MW) and projected (131MW) demand by 2050. Hence, our first stage estimates suggest that combinations of renewables could signifi?cantly improve energy security and even support energy exports from the IOM. The demonstrated GIS-MCDA modelling offers a tool for scoping the resource potential of other energy-import dependent islands.© The Author(s). CC BY 4.0

Scores for species’ population vulnerability to collision mortality at offshore wind turbines, with species ranked by overall score.

The method to create the Wind Resource Area datasets is to:Query Power Plant point locations from the California Energy Commission, California Power Plants data set by operational status and capacity greater than or equal to 2 MW at each facility from the Quarterly Fuel and Energy Report, CEC-1304A. Plants tracked include those of at least 1 MW, which are considered of commercial size. A polygon was generated around the resulting operational, commercial wind facilities using the Aggregate Points geoprocessing tool with an aggregation distance of 15 survey miles. A 5 mile spatial buffer was added to the resulting polygons. The buffer does not represent information regarding environmental analysis. It is used only to depict plant concentration regions.

The global market for solar resource assessment software is experiencing robust growth, driven by the increasing adoption of renewable energy sources and the need for accurate solar resource data to optimize solar power plant development and performance. The market size in 2025 is estimated at $500 million, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the expanding solar energy sector necessitates precise data for site selection, system design, and yield prediction, boosting demand for sophisticated assessment software. Secondly, advancements in software capabilities, including improved algorithms, higher-resolution data integration, and user-friendly interfaces, are making these tools more accessible and efficient. Thirdly, government initiatives promoting renewable energy adoption in various regions are further stimulating market growth by providing incentives and creating favorable regulatory environments. The market is segmented by pricing (paid and free) and application (personal and commercial), with the paid commercial segment currently dominating due to its advanced features and suitability for large-scale projects. Geographic expansion, particularly in developing economies with significant solar potential, also contributes to the overall market expansion. Despite the positive outlook, challenges remain. The high initial investment cost of the software can hinder adoption among smaller players, and data accuracy concerns, particularly in regions with limited historical data, pose a limitation. Competition among established providers and new entrants is also intensifying. However, ongoing technological advancements, coupled with the growing demand for renewable energy, are anticipated to overcome these challenges and sustain the market's impressive growth trajectory throughout the forecast period. The market is expected to reach approximately $1.5 billion by 2033, demonstrating the significant potential for continued expansion in the coming years.

Scores for English territorial waters marine bird species’ population risk due to displacement by offshore wind farms, ranked by species score.

Wind Turbine Shadow Flicker Assessment Market Outlook

According to our latest research, the global wind turbine shadow flicker assessment market size reached USD 320 million in 2024, reflecting a robust demand for advanced assessment solutions in the rapidly expanding wind energy sector. The market is exhibiting a steady growth trajectory, registering a CAGR of 6.8% from 2025 to 2033. By the end of the forecast period in 2033, the market is expected to attain a value of USD 613 million. This positive outlook is primarily driven by the increasing deployment of wind energy projects worldwide and stringent regulatory frameworks mandating comprehensive environmental impact assessments, including shadow flicker analysis, to ensure minimal disruption to communities and ecosystems.

The primary growth driver for the wind turbine shadow flicker assessment market is the accelerating global shift towards renewable energy sources, especially wind power. Governments and private sector players are investing heavily in wind energy infrastructure to achieve ambitious carbon reduction targets and energy security objectives. As wind farms proliferate, concerns regarding the environmental and social impacts of turbine operations, such as shadow flicker, have intensified. Shadow flicker occurs when rotating turbine blades cast moving shadows on nearby structures, potentially affecting human comfort and health. Regulatory bodies in regions such as Europe and North America have implemented strict guidelines requiring thorough shadow flicker assessments during the planning and permitting stages of wind projects. This regulatory impetus has significantly increased the adoption of specialized assessment tools and services, fueling market growth.

Another key factor contributing to market expansion is the rapid technological advancement in shadow flicker assessment solutions. The integration of sophisticated software platforms, Geographic Information Systems (GIS), and real-time data analytics has revolutionized the accuracy and efficiency of shadow flicker modeling. Modern assessment tools can simulate complex environmental scenarios, account for seasonal and diurnal variations, and provide actionable insights for wind farm developers and regulators. This technological evolution has not only enhanced compliance with environmental standards but also optimized turbine placement and operational strategies, reducing potential conflicts with local communities. As a result, demand for both hardware and software components in shadow flicker assessment has surged, propelling the overall market forward.

Furthermore, the growing emphasis on stakeholder engagement and social acceptance of wind energy projects is shaping the wind turbine shadow flicker assessment market. Public opposition due to perceived or actual shadow flicker impacts can delay or derail wind farm developments. To address these challenges, project developers are increasingly relying on comprehensive assessment reports and mitigation strategies to foster transparent communication with affected communities and regulatory authorities. Environmental consultants and engineering firms specializing in shadow flicker analysis are playing a pivotal role in bridging the gap between developers, regulators, and the public. This trend is expected to sustain demand for professional services and drive continuous innovation in assessment methodologies throughout the forecast period.

In addition to shadow flicker assessments, Wind Resource Assessment Service plays a crucial role in the planning and development of wind energy projects. This service involves the comprehensive analysis of wind patterns, speeds, and directions at potential project sites, providing essential data that informs turbine placement and energy yield predictions. By leveraging advanced meteorological models and long-term data collection, wind resource assessments help developers optimize site selection and enhance project feasibility. As the demand for wind energy continues to rise, the integration of wind resource assessment with shadow flicker analysis ensures that projects are both environmentally responsible and economically viable. The synergy between these assessment services underscores the importance of holistic planning in the wind energy sector, ultimately contributing to the sustainable growth of renewable energy infrastructure.

The three digital maps provided in this product aim to assess the degree of Offshore windfarm siting suitability existing over the geographical area extent with a focal point where waters of France, Ireland and UK meet. The maps display respectively the spatial distribution of the average and lowest windfarm siting suitability scores along with the average wind speed distribution over a time period of 10 years. They are part of a process set up to assess the fit for use quality of the currently available datasets to support a preliminary selection of potential offshore sites for wind energy development. To build these maps, GIS tools were applied to several key spatial datasets from the 5 data type domains considered in the project: Air, Marine Water, Riverbed/Seabed, Biota/Biology and Human Activities, collated during the initial stages of the project. Initially, each selected dataset was formatted and clipped to the study area extent and spatially classified according to suitability scores, to define raster layers with the variables depicting levels of current anthropogenic and environmental spatial occupation of activities, seabed depth and slope, distances to shoreline, shipping intensity, mean significant wave height, and substrate type. These pre-processed layers were employed as inputs for applying a spatial multi-criteria model using a wind farming suitability classification based on a discrete 5 grades index, ranging from Very Low up to Very High suitability. In adition to suitability maps, an average wind speed spatial distribution map for a 10 years period, at 10 m height, was obtained over the study area from the raster processing of a wind speed time series of monthly means available from daily wind analysis data. The characteristics of the datasets used in this exercise underwent an appropriateness evaluation procedure based on a comparison between their measured quality and those specified for the product.
All the spatial information made available in these maps and from the subsequent appropriateness analysis of the datasets, contributes to a clearer overview of the amount of public-access baseline knowledge currently existing for the North Atlantic basin area.

Renewable Energy Siting via Satellite Market Outlook

According to our latest research, the global Renewable Energy Siting via Satellite market size reached USD 1.42 billion in 2024, with the market demonstrating robust momentum driven by the urgent need for optimized renewable project placement. The sector is expected to expand at a CAGR of 13.7% from 2025 to 2033, with the market forecasted to attain USD 4.11 billion by 2033. This impressive growth trajectory is underpinned by increasing investments in renewable infrastructure, advancements in satellite imaging technologies, and the necessity for precise site selection to maximize energy yield and minimize environmental impact.

One of the primary growth factors propelling the Renewable Energy Siting via Satellite market is the escalating global commitment to decarbonization and the transition toward sustainable energy sources. Governments and private sector stakeholders are under mounting pressure to meet ambitious climate targets, which has accelerated the deployment of solar, wind, hydropower, biomass, and geothermal projects. Satellite-based solutions, leveraging remote sensing and GIS mapping, have become indispensable for identifying optimal locations, assessing resource potential, and mitigating environmental risks. These technologies enable stakeholders to make data-driven decisions, reduce project lead times, and enhance the overall viability of renewable energy investments. The integration of advanced data analytics and artificial intelligence further refines the siting process, fostering efficiency and reducing costs.

Another significant driver is the rapid evolution of satellite technology, which has substantially improved the accuracy, resolution, and frequency of earth observation data. The proliferation of commercial satellites and the advent of high-resolution imagery have empowered project developers, utility companies, and government agencies to conduct comprehensive site assessments remotely. This capability is especially critical in remote or underdeveloped regions where ground-based surveys are logistically challenging and costly. The synergy between satellite imagery and Geographic Information Systems (GIS) mapping facilitates multi-criteria analysis, enabling stakeholders to evaluate topography, land use, weather patterns, and environmental sensitivities with unprecedented precision. This technological leap is fostering a paradigm shift in how renewable energy projects are planned, permitted, and executed.

Furthermore, the growing emphasis on environmental and social governance (ESG) criteria in investment decisions has heightened the demand for transparent, data-backed siting methodologies. Satellite-based renewable energy siting offers robust tools for monitoring land use changes, biodiversity impacts, and community proximity, ensuring that projects adhere to regulatory and ethical standards. The ability to continuously monitor sites post-development also aids in compliance and performance optimization. As investors and regulators increasingly scrutinize project footprints, the adoption of satellite-powered siting solutions is expected to become a standard industry practice. This trend is further amplified by the proliferation of cloud-based platforms, which democratize access to sophisticated analytics and facilitate collaboration among geographically dispersed teams.

In the context of wind energy, the process of Wind Farm Site Assessment via Satellite has become increasingly vital. This method allows for the precise evaluation of wind resources, topographical features, and environmental constraints from a remote perspective. By utilizing satellite data, developers can identify optimal locations for wind farms, taking into account factors such as wind speed consistency, land use, and potential impacts on local wildlife. This approach not only enhances the efficiency of site selection but also reduces the need for extensive on-ground surveys, which can be both time-consuming and costly. As the demand for renewable energy continues to rise, the integration of satellite technology in wind farm site assessments is set to play a crucial role in meeting global energy needs.

From a regional perspective, North America and Europe currently dominate the Renewable Energy Siting via Satellite market, driven by mature renewable sectors, stro

(Link to Metadata) The Renewable Energy Atlas of Vermont and this dataset were created to assist town energy committees, the Clean Energy Development Fund and other funders, educators, planners, policy-makers, and businesses in making informed decisions about the planning and implementation of renewable energy in their communities - decisions that ultimately lead to successful projects, greater energy security, a cleaner and healthier environment, and a better quality of life across the state. Energy flows through nature into social systems as life support. Human societies depended on renewable, solar powered energy for fuel, shelter, tools, and other items for most of our history. Today, when we flip on a light switch, turn an ignition or a water faucet, or eat a hamburger, we engage complex energy extraction systems that largely rely on non-renewable energy to power our lives. About 90% of Vermont's total energy consumption is currently generated from non-renewable energy sources. This dependency puts Vermont at considerable risk, as the peaking of world oil production, global financial instability, climate change, and other factors impact the state.

The purpose of this project is to determine the most suitable location for a wind farm in 50Kms radius of Calgary. Different criterion need to be considered for choosing the final site, ranging from distance from settlements, water bodies, proximity to power lines to slope and wind speed intensity of the region. Based on the literature review, the areas that did not have the potential for hosting wind turbines were excluded by using the buffer tool in ArcGIS Pro. Afterwards, the wind speed and slope of the remaining regions were analyzed to pick the location with the highest wind speed and the most suitable slope. As can be seen in the final map, the final site is located in the western side of Calgary.

This layer displays several key factors relevant to the siting of large-scale solar power and combines them, demonstrating the “suitability” of regions within Michigan to this technology. The purpose of this tool is to aid proactive planning and zoning for local governments by highlighting the quantity and location of areas of potential development interest. The suitability models were created using the Suitability Modeler in ArcGIS Pro for both solar and wind energy. These models were based on the Geospatial Energy Mapper (GEM) but have been enhanced with higher-resolution data and modifications tailored to Michigan's landscape. The key factors involved are land use type, land slope, distance to substations, and population density. For land use type, each type was assigned a suitability score by the creators. These four factors are then weighted based on total relevance to energy development, also assigned by the creators, to ultimately produce a total suitability score. The creators determined land use type scores and weighting based on research, peer feedback, and general experience with energy siting. Notably, this map does not include solar potential/solar insolation due to relative consistency across the state, which led to the decreased distinction between other key factors, reducing the utility of this tool for local governments to plan and zone with. An additional layer of high voltage transmission lines is included for further relevant context, though this is not involved in the suitability score.

Model Parameters (Modified upon GEM models): Utility Scale PV (Solar)

Parameters

Weight

Slope

2

Land Cover

4

Population Density

1

Distance to substation

3

Data Sources and Model Weighting:

Slope (Land Fire slope)SUITABILITYRANGE/CLASS1000 - 1%902%903%304%105%16 - 10%0≥ 11% Land Cover (Multi-Resolution Land Characteristics (MRLC) Consortium – CONUS 2021)GEM - Solar REA - solarSUITABILITYRANGE/CLASS SUITABILITYRANGE/CLASS100Unclassified (0) 0Unclassified (0)1Open Water (11) 0Open Water (11)10Perennial Snow/Ice (12) 0Perennial Snow/Ice (12)75Developed, Open Space (21) 0Developed, Open Space (21)75Developed, Low Intensity (22) 0Developed, Low Intensity (22)75Developed, Medium Intensity (23) 0Developed, Medium Intensity (23)75Developed, High Intensity (24) 0Developed, High Intensity (24)100Barren Land (31) 50Barren Land (31)50Deciduous Forest (41) 0Deciduous Forest (41)50Evergreen Forest (42) 0Evergreen Forest (42)50Mixed Forest 0Mixed Forest90Shrub/Scrub (52) 90Herbaceous (71)90Hay/Pasture (81) 100Hay/Pasture (81)90Cultivated Crops (82) 100Cultivated Crops (82)40Woody Wetlands (90) 0Woody Wetlands (90) Distance to Substation (220 to 345kV) (ArcGIS Substations)SUITABILITYRANGE/CLASS1000 - 1 miles901 - 5 miles755 - 10 miles50Over 10 miles Population Density (GPW v4 Population Density)SUITABILITYRANGE/CLASS75101 - 15050151 - 20025201 - 3000301 and higher0No Data

For any questions, please contact Ian O’Leary at olearyi@michigan.gov, or reference the Renewable Energy Academy website to see how EGLE offers technical assistance for renewable energy.