The Regional Energy Deployment System (ReEDS) helps the U.S. Department of Energy, utilities, public utility commissions, state/local regulators and others optimize and visualize the build-out of U.S. electricity generation and transmission systems.
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.
Unique Value of ReEDS
Spatial Resolution and Variability Consideration
The Regional Energy Deployment System (ReEDS) model has singular capabilities that differentiate it from other models and that make it uniquely suitable for certain types of analyses. While ReEDS can model all types of power generators and fuels—coal, gas, nuclear, renewables—it was designed primarily to address considerations for integrating renewable electric technologies into the power grid. In particular, it was designed to address the variable resource issues associated with solar and wind power as well as the remote nature of many of the best wind resources and their need for transmission. These capabilities require the two primary structural elements of ReEDS—a multiplicity of regions and a sequential-solve formulation.
The high spatial resolution in ReEDS allows the model to better explore implications of the spatial mismatch between resource and load: the model can directly account for both the transmission requirements associated with developing a remote wind or solar site, the value of the resource quality, and geographic diversity associated with that site. ReEDS allows the different characteristics to play off one another and development decisions are co-optimized considering all relevant factors.
An important consideration regarding the integration of wind and solar technology is how variability and uncertainty of output from those sources will impact the operation and reliability of the system into which it will be integrated. ReEDS uses a statistical methodology to quantify the impacts of variability along multiple dimensions, allowing representation of those effects throughout the scenario. Because those effects are highly non-linear and dependent on both the characteristics of the potential development site and the balance-of-system, ReEDS recomputes the variability parameters between each two-year optimization. Along with improving ReEDS' representation of variable renewables, this approach also allows endogenous learning, dynamic interactions with other models, limited foresight, price penalties for rapid growth, and other non-linear effects.
While many models take advantage of geographic information system (GIS) databases of existing generation and transmission capacity, load centers, political boundaries, etc., ReEDS goes a step further and uses GIS capabilities to drive the effective spatial resolution of certain aspects of ReEDS to a sub-region level. For example, ReEDS uses GIS to develop supply curves of wind resources within each of the model's 358 resource regions. For each region and class of wind resource, ReEDS creates a supply curve that captures the cost of connecting that class of wind resource to nearby transmission lines while taking into account the individual wind resources within the region and the line's location and transmission capacity.
Coupling with Production-Cost Models
ReEDS capacity-expansion capabilities can be coupled with production-cost models like GridView (from ABB) or PLEXOS (Energy Exemplar) to further resolve generation and transmission details. ReEDS scenario outputs can be used to define the infrastructure for the production cost model, which can verify the ReEDS operational decisions and provide additional operational detail. GridView and PLEXOS scenarios not only ensure that there are adequate generation resources to support loads and the variable resources of wind and solar, but can also ensure that the transmission system is adequate to handle the resulting flows.
While charts and tables are helpful, visualizing geospatial results provides a whole new level of appreciation for system operation and the value of the spatially-defined capacity expansion estimates. Along with ReEDS-output-based maps, the outputs of GridView or PLEXOS can be returned to the GIS system to show system-level operation—both generation and transmission flows—at very fine spatial and temporal detail.