energy production data are collected from energy production sites, such as power plants. nearby communities reap many of the benefits and harmful effects of these production sites, though communities generally do not own or operate them themselves. because of this relationship between communities and energy production sites, communities are increasingly finding ways to influence the types of energy production occurring at nearby sites. to encourage these community/energy production partnerships, this visualization associates each energy production site with its nearest community.
it is worth noting that (1) the sites are owned and operated by utility companies and cooperatives, (2) the benefits and harmful effects of an energy production site may extend beyond its nearest community, to other communities in the area, in a way that is not captured by this visualization, and (3) communities with no nearby energy production site do not appear on the first two maps of this visualization, but still have potential for affecting energy production in their area and should continue to the later maps to learn more.
energy production data were collected from the us energy information administration, via form 923. this data set shows the net mwh generated by fuel type for every power plant in the us in 2018. for this visualization, we aggregated fuel types into the following categories: coal, gas, petro, nuclear, hydroelectric, biomass, solar, wind, geothermal, and other. the other category includes trash, tires, storage, purchased steam, and unknown.
we also added energy production estimates for seven community solar gardens from the nrel .
we show resource potential for four renewable energy technologies: solar, wind, biomass, and geothermal. a resource potential represents the amount of energy that may be available for a given technology in a given area. it does not suggest anything about the economical or environmental costs of producing this energy. for example, solar energy resource potential tells us how much energy could be captured if an entire area were covered in solar panels; it does not indicate what would be a realistic area to put solar panels.
to determine the resource potentials associated with each community, we examined the areas within the community boundary as well as a small area around the communities. for biomass, we considered the county containing the community, because biomass availability data were recorded at the county level. for the other technologies, we considered the community and a 5-10 km area around it.
potentials for newer renewable energy technologies were calculated from data sets provided by the national renewable energy laboratory.
solar: solar energy potential was provided by as average annual kwh/km^2/day at ~10 km resolution. we included places with positive average solar energy potential in and around the community in our map of places with solar potential.
wind: was provided by nrel as a wind class designation, which is based upon the wind speeds at 10 m and 50 m heights. this vector data set was based upon data ranging in resolution from 200 m to 1 km. we included areas with a wind class of 3 or higher in our map of places with wind energy potential.
买球网开户geothermal: was provided by the nrel as classes of favorability based on temperature at depth for 3 km to 10 km. we included places with a favorability class between 1 and 5 in our map of places with geothermal energy potential.
买球网开户biomass: biomass energy potential was provided by the as the power available from residue within each county. we included places residing within counties with positive biomass power available from residue in our map of places with biomass potential.