The process to map opportunities for utility-scale wind development is similar to PV, with some important differences. Before proceeding with wind, it is important to consider the scale of the system, because area-based constraints are modeled in relation to specific technologies and system designs. For example, regulated set back distances for wind turbines would apply to all wind energy technologies, but they are likely to be further for larger wind turbines and for wind farms with more wind turbines, than they are for smaller wind turbines or wind farms with fewer turbines. In what follows, we are assuming 3MW turbines, with a hub height of 100 meters, which is currently the standard commercial size for utility-scale on-shore wind systems.
A high-level summary of data requirements to run these processing steps is listed in this data inventory guideline. It may be helpful to read the processing steps with this guideline as a reference.
Mapping Technically Recoverable Wind Resources
Technically recoverable wind resources are modeled based on the following exclusions:
Areas with a slope above 35%. Steeper slopes would require additional supports or costly changes to the landscape
Land-cover types that are not capable of hosting utility-scale wind turbine for technical reasons, including urbanized areas, roads, railways, waterbodies, aggregate extraction sites, and airports
The technically recoverable resource base is also shaped by technology efficiency and by its ‘packing factor’, or the density with which turbines can be installed on a parcel of land. Site-specific project designs (turbine selection and layout) will ultimately determine realizable potential at a specific site. At this larger scale of analysis, it is sufficient to assume an energy density of 25-100 ha/MW installed to account for spacing. In other words, dividing total area by 25-100 will provide a back-of-envelope estimation for the total installed capacity of wind energy systems over the total area. Keep in mind this provides an estimate of nameplate capacity, not actual generation, which is determined by capacity factor (MWh/MW installed). Most estimates assume 2600-3000MWh/MW installed to estimate generation potential.
Mapping Legally Accessible Wind Resources
Land-use and environmental regulations from multiple levels of government are audited to identify ‘legally accessible’ wind resources. Legal constraints on development are grouped into three categories (see Table 1 below). First, ‘restricted areas’ are removed from consideration through binary mapping similar to the exclusions above, where the prohibited area is assigned ‘NoData’. This usually includes national parks, community parkland, wetland, and other environmentally or culturally significant areas that are protected from any sort of development, including RE development. Second, ‘regulated areas’ are mapped to indicate areas at which regulations do not provide a clear ‘go/no-go’ decision. In these areas, regulations influence, but do not absolutely restrict, RE development. Third, ‘unregulated areas’, which do not have any explicit regulatory control on RE development. This category includes all areas that have not been explicitly addressed in any policies or regulations, and which are considered technically accessible. Development in this category would still require an environmental impact assessment.
The process to map legally accessible resources unfolds as follows:
- Identify all policies and regulations that apply to renewable energy development in the area.
- Extract information about the specific natural features and land-cover/land-use categories to which the policies and regulations from step (1) apply.
- Interpret the level of protection that each policy or regulation assigns to a given natural feature or land-cover/land-use category relative to renewable energy development (see Table 1).
- Gather digital spatial data on the natural features and land-covers/land-use categories identified in step (2). Using data reclassification techniques in a geographic information system, assign the level of protection from step (3) accordingly to create a map of environmental protections for each policy / regulation.
- Repeat step (4) for all policies and regulations, and their associated natural features and land-cover/land-use categories.
- Combine all maps from step (5) into a single map, showing the levels of protection across natural features and land-cover/land-use categories in the study area. Where multiple policies/regulations apply to the same natural feature or land-cover/land-use category, the most stringent protection level is applied.
- Apply this regulatory classification to all areas that have been deemed ‘technically accessible’, being sure to classify all ‘restricted areas’ as NoData so that they are removed from further consideration.
Table 1: The classification system used to map restricted and regulated areas
|Restricted area||Regulations inhibit RE development||Significant wetlands are often prohibited from any sort of development, including PV systems.|
|Regulated area||Regulations control, and under some conditions will inhibit, RE development||Regulations that manage the extent of site alterations within 100 metres of a body of water.|
|Un-regulated area||There are no predetermined regulations applying to RE, but an EIA will still be required||Open areas such as active or abandoned farmland that do not fall within any specific regulatory control relative to RE development.|
When accounting for regulated setback distances — e.g., from dwellings and ‘noise receptors’ or from sensitive habitats — it will be important to include spatial data for those specific features that lay outside of the boundaries of the study region. A wind turbine placed at the very edge of the municipal border may be far enough away from a dwelling within the municipality but may be just meters away from a dwelling that is located within a neighboring municipality.
In the end, you will now have a map that classifies all land deemed technically accessible and legally accessible into two categories: regulated and unregulated. Generally speaking, lands classified as ‘regulated’ can be considered to be associated with a higher level of sensitivity.
Mapping Policy Scenarios for Wind Farms
The analysis described above is the ‘base case’. It maps ‘legally accessible’ resources based on prevailing regulatory conditions. Against this base-case we can model the impacts of hypothetical changes or proposed changes to policies and regulations. For wind energy systems, the key policy decisions that should be mapped include: (1) wider/narrower set back distances from noise receptors (residential areas, community centers, and so on); (2) stronger/looser protections on natural features, wildlife habitats, and other ecologically sensitive areas.
Mapping Relative Economic Value of Accessible Wind Resources
Relative spatial capital costs
These are costs that will vary spatially, depending on where a wind system is located, and ultimately represent a relatively small proportion of total system costs relative to the costs of the turbine and other installation materials. This portion of the model should account for the following:
- Site acquisition – property values
- Site access – building site access roads
- Site connection – building power lines
- Site preparation – land clearing and grading
Land acquisition costs are highly context-dependent and difficult to determine at the pre-feasibility stage. The table below provides a breakdown of other costs.
Table 2: Spatial capital cost estimates for site access and site development
|Spatial capital cost component||Cost estimate||Source|
|Transmission Lines||$1000 – 1200 / Meter (not including the cost of permitting and land acquisitions).||Ontario Power Authority. (2005). Northern York Region Electricity Supply Study Submission to the Ontario Energy Board. York, Ontario. Retrieved from: https://www.oeb.ca/documents/cases/EB-2005-0315/exhibit_c_300905.pdf|
|Roads||$450 / Meter||Values were obtained from an industry specialist in Ontario.|
|Clearing Costs||Forest – $3,000/Acre||Haaren, R. V., & Fthenakis, V. (2011). GIS-based wind farm site selection using spatial multi-criteria analysis (SMCA): Evaluating the case for New York State. Renewable and Sustainable Energy Reviews, 15(7), 3332–3340. doi: 10.1016/j.rser.2011.04.010|
|Shrub land – $1,000/Acre|
|Cropland – $40/Acre|
|Barren / Sparse – $40/Acre|
The mapping process unfolds as follows:
- Run the cost distance tool using the utility grid as the source layer and a cost per linear kilometer of line as the cost raster (see Table 2). Be sure to include utility lines that are outside of your study area since a system built in your study area could technically connect to those lines. There may be policies that do not allow for transmission lines to be built in certain areas. In this case, reclassify to a very high cost, to ensure they are avoided. For example, we assume that significant wetlands would not be crossed with a new transmission corridor, and so those areas would be assumed to have a very high value. The model would then be encouraged to go around these areas rather than through them. The tool will then calculate the cost of connecting from any given cell to the source layer (utility line).
- Run the cost distance tool using the road network as the source layer and a cost per linear kilometer of line as the cost raster (see Table 2). NOTE: if your study area is a relatively developed region with sufficient road infrastructure to make this cost negligible and therefore not worth incorporating.
- Reclassify a landcover map to apply a standard site-clearing and site preparation cost on a per cell basis. These costs are very small relative to other spatially varying costs and, as a result, are very unlikely to change the model outputs.
- Combine the outputs by summing all cost rasters. Make sure to keep only the sites that have already been deemed legally accessible.
As mentioned, this process provides a map that indicates relative spatial capital costs. If you wish to examine a specific site in more detail, including costs related to technology and finance, there are a few tools that may be applied, including RETScreen from Natural Resources Canada; System Advisory Model (SAM) from the National Renewable Energy Laboratory (U.S.)
Mapping market readiness indicators
The second dimension of relative economic value includes factors that might make one site more viable or more economically attractive than another, even if those capital costs are equal – we map this as relative ‘market readiness’. Indicators of market readiness include the following (this list is not exhaustive and you may layer in other indicators of market readiness):
Locational marginal price (LMP) of heat/electricity: the marginal cost/price of heat/electricity can be higher in an area due to system congestion or the relative cost to run local generating facilities. High LMP due to congestion issues indicates a region that may benefit from a distributed resource that can help to alleviate that congestion. A map of LMP can help to indicate where a new generating facility, especially small scale RE systems, might be more competitive.
Brownfields: these areas provide opportunities for RE development with less competition from, and less need to displace, other economic activities.
Publicly owned land: especially in jurisdictions with a commitment to support RE development, these lands may be acquired at a more favorable rate than private land.