GIS Models Identify Renewable Energy Sites Suitable for Development

Geographic information system (GIS) technology is a powerful resource for compiling, organizing, and presenting information to help make faster, more informed decisions about site selection and permitting requirements.

GIS models can enable the development team to enter numerous queries to sort through thousands of parcels and identify those meeting defined parameters.

Key components for effective GIS modeling are comprehensively understanding the development team’s “fear factors” for assessing project viability and identifying the data needed to evaluate whether a parcel may result in a streamlined permitting process or is too constrained to economically develop.

1. Determine Site Parameters

The foundation is laid when the project development team (the developer, environmental experts, electrical engineers, and real estate brokers) identify the parameters to be factored into the site selection/screening process. Parameters can include a wide range of factors, including but not limited to the following:

  • Proximity to transmission lines and/or substations
  • Parcel orientation (e.g., south facing)
  • Parcel size
  • Parcel irregularities
  • Slopes/topography
  • Irradation
  • Habitat values
  • Williamson Act Lands
  • Real estate values
  • Zoning designations
  • Floodplains
  • Other renewable energy projects
  • Critical habitat

Each team member will have his or her own parameters. The goal is to obtain a comprehensive understanding of all the parameters and how much priority each one should be given in site evaluation. For instance, the project developer may only want to pursue projects between 20 and 25 megawatts. This results in the need to identify only parcels that are approximately 200 acres and located within 1.5 miles of transmission.

Early identification of parameters most critical for development success allows the GIS modeler to create a GIS model assigning priorities to parameters.

2. Compile & Organize Data

Data layers, or spatial information, are identified to allow sites to be screened using parameters established in the first step.

Data layers cover the geographic location of a study area or other specific site. Each is a single layer that when compiled into a GIS model creates powerful visual displays of information. For renewable energy projects, data layers typically draw upon databases identifying the following:

  • California Natural Diversity Database (CNDDB) sensitive species locations
  • U.S. Geological Survey (USGS) digital elevation models for percent slopes
  • Federal Emergency Management Act (FEMA) floodplains
  • U.S. Department of Agriculture Williamson Act Farmlands
  • National Renewable Energy Laboratory (NREL) solar irradiation values
  • Platts substation and transmission line locations.

Compiled data must be current and in the right spatial format (correct coordinate systems in relation to the study area). It is also key to identify data sources that, when compiled into a GIS model, will accurately reflect potential constraints for development in a given area or a particular site based on the project’s parameters.

It’s easy to see how massive amounts of data can be collected for a study area when multiple data (spatial) layers are combined with a large numbers of parameters.

All the data becomes manageable in step 3 when the GIS model can screen thousands of parcels to identify sites based on parameters and weighting criteria.

3. Run Queries Against GIS Model

The GIS model enables the development team to run queries combing any number of data layers and parameters.

The key to running effective queries is for the development team to prioritize the individual data layers and parameters to develop a ranking system for sites. Often, several sites may meet a majority of all the development criteria but may slightly exceed one parameter, such as the percent slopes on site.

For example, a sample query might include all currently undeveloped parcels within 2 miles of a given substation where the slopes do not exceed 8 percent. The team can quickly see a map of sites meeting those parameters that are suitable for development. The model has the flexibility to add data layers and parameters as issues arise.