Qualitative Model Description
The Regional Energy Deployment System (ReEDS) is a long-term capacity-expansion model for the deployment of electric power generation technologies and transmission infrastructure throughout the contiguous United States. Developed by the National Renewable Energy Laboratory's (NREL's) Strategic Energy Analysis Center (SEAC) with support from the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy, ReEDS is designed to analyze critical issues in the electric sector, especially with respect to potential energy policies, such as clean energy and renewable energy standards or carbon restrictions.
ReEDS provides a detailed representation of electricity generation and transmission systems and specifically addresses a variety of issues related to renewable energy technologies, including accessibility and cost of transmission, regional quality of renewable resources, seasonal and diurnal load and generation profiles, variability and uncertainty of wind and solar power, and the influence of variability on the reliability of electric power provision. ReEDS addresses these issues through a highly discretized regional structure, explicit statistical treatment of the variability in wind and solar output over time, and consideration of ancillary service requirements and costs.
Along with numerous independent analyses, ReEDS was used prominently for the 20% Wind Energy by 2030 report and is currently being applied to the Renewable Electricity Futures Study—an analysis of how the United States might provide 80% of its electricity from renewable sources.
Qualitative model description
To determine potential expansion of electricity generation, storage, and transmission systems throughout the contiguous United States over the next several decades, ReEDS chooses the cost-optimal mix of technologies that meet all regional electric power demand requirements, based on grid reliability (reserve) requirements, technology resource constraints, and policy constraints. This cost-minimization routine is performed for each of 23 two-year periods from 2006 to 2050. The major outputs of ReEDS include the amount of generator capacity and annual generation from each technology, storage capacity expansion, transmission capacity expansion, total electric sector costs, electricity price, fuel prices, and carbon dioxide (CO2) emissions. Figure 1 shows an example of ReEDS capacity estimates for the United States for different generation technologies over the 44 year evaluation period.
Time in ReEDS is subdivided within each 2-year period, with each year divided into four seasons with a representative day for each season, which is further divided into four diurnal time-slices. Also, there is one additional summer-peak time-slice. These 17 annual time-slices enable ReEDS to capture the intricacies of meeting electric loads that vary throughout the day and year—with both conventional and renewable generators.
Generator types in ReEDS include all of the major conventional electricity-generating technologies: hydropower, simple and combined cycle natural gas, several varieties of coal, oil/gas steam, nuclear, wind, solar (both thermal and photovoltaic), geothermal, biopower, and a few electricity storage options.
Although ReEDS includes all major generator types, it has been designed primarily to address the market issues that are of the greatest significance to renewable energy technologies. As a result, renewable and carbon-free energy technologies and barriers to their adoption are a focus. Diffuse resources, such as wind and solar power, come with concerns that conventional dispatchable power plants do not have, particularly regarding transmission and variability. The ReEDS model examines these issues primarily by using a much greater level of geographic disaggregation than other long-term large-scale capacity expansion models. ReEDS uses 356 different resource regions in the continental United States. These 356 resource supply regions are grouped into four levels of larger regional groupings—balancing areas (BA), reserve-sharing groups, North American Electric Reliability Council (NERC) regions (NERC 2010), and interconnects. States are also represented for the inclusion of state policies.
Much of the data inputs to ReEDS are tied to these regions and derived from a detailed geographic information system (GIS) model/database of the wind and solar resource, transmission grid, and existing plant data. The geographic disaggregation of renewable resources enables ReEDS to calculate transmission distances as well as the benefits of dispersed wind farms or solar power facilities.
Annual electric loads and fuel price supply curves are exogenously specified to define the system boundaries for each period of the optimization. To allow for the evaluation of scenarios that might depart significantly from the base scenario, price elasticity of demand is integrated into the model and the exogenously-defined demand projection can be adjusted based on a comparison of the computed electricity price with an externally specified expected price.