Utility Planning Models 101—How NREL Uses Geospatial Data in its Resource Planning Model

Authors: Matthew Irish*, National Renewable Energy Laboratory, Brady Stoll, National Renewable Energy Laboratory, Elaine Hale, National Renewable Energy Laboratory, Scott Nicholson, National Renewable Energy Laboratory
Topics: Energy, Natural Resources, Sustainability Science
Keywords: renewable energy, capacity expansion model, power system, electricity, solar, wind
Session Type: Paper
Day: 4/8/2020
Start / End Time: 9:35 AM / 10:50 AM
Room: Grand Ballroom 2, Sheraton, IM Pei Tower, Second Floor Level
Presentation File: No File Uploaded

For the past few decades, researchers at the National Renewable Energy Laboratory have been innovating in the development of electric power system planning models, known in our field as capacity expansion models. The objective of these models is to help us imagine the ideal power system of the future, finding the least-cost investment pathway to meet future electricity demand under possible environmental constraints or emissions policies, all within the context of projected changes in costs for each generation, transmission, and storage technology and a limited availability of renewable energy resources. This means that our capacity expansion models rely heavily on geospatial analysis. In this talk, we will discuss NREL's Resource Planning Model (RPM), introducing capacity expansion modeling with a focus on its geospatial aspects.

RPM makes use of a mixed zonal-nodal spatial composition that allows us to maintain computational tractability while representing the grid network down to the individual generating unit and transmission line for a region of interest (and aggregating the rest of the grid into interacting zones). The model also includes multiple solar and wind spatial resource regions.
We will also explore how our geospatial methods and our capacity expansion models have evolved as the nature of our research questions has changed and advances in computing power have enabled more detailed analysis.

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