About the Regional Energy Deployment System Model
NREL designed the Regional Energy Deployment System (ReEDS) to simulate electricity sector investment decisions based on system constraints and demands for energy and ancillary services.
The ReEDS model is unique in its high-spatial resolution and advanced algorithms for representing the cost, value, and technical characteristics of integrating renewable energy technologies.
Although it covers a broad geographic and technological scope, ReEDS is designed to reflect the regional attributes of energy production and consumption. The model considers a large suite of generating technologies, including fossil, nuclear, and renewable technologies, as well as transmission and storage expansion options.
The ReEDS model has also been used to help inform decision-making outside of the electricity sector. For example, it has been linked to a variety of models including:
- Distributed generation adoption models
- Natural gas supply models
- Water and climate models
- Economy-wide market equilibrium models.
Many examples have been published. See our publications.
NREL researchers have put substantial work into making ReEDS into a capable and robust model, but it’s not perfect. It is an analysis tool rather than a prediction tool.
The ReEDS model is built using GAMS (General Algebraic Modeling System) version 24.7, Python version 3.7, and R version 3.4.4. A GAMS license and appropriate solver will be required to run the model. The model is run from the command line and does not currently include a GUI. The model is built to work in Windows, but it can be configured to run on Unix.
A typical model run using CPLEX will use 10-15 GB of memory and two cores; although, additional cores or memory can improve the solution time for many scenarios or model configurations. Model run time varies by the model settings used, ranging from minutes to days.
The ReEDS model has been publicly available since September 2019. To use, you must request access to NREL’s GitHub repository.
ReEDS is a large, complex optimization model with many inputs, outputs, variables, and constraints. Understanding and appropriately using the model may take time and require some knowledge of optimization modeling.
A typical model run includes hundreds of thousands or millions of variables and constraints and produces millions of outputs. Because of this complexity and size, it can be easy to misinterpret results or to ascribe more accuracy to certain model results than is merited.
For suggestions based on NREL’s experience running and developing ReEDS, see our user guide.
For additional information about using the model, see the Regional Energy Deployment System Model Documentation.
For standard scenarios definitions, see 2019 Standard Scenarios Report: A U.S. Electricity Sector Outlook.