Regional Energy Deployment System

Regional Energy Deployment System
Name	Regional Energy Deployment System
Developer	National Renewable Energy Laboratory
Released	0 2009
Programming language	Python (programming language)
Operating system	Cross-platform
Genre	Energy modeling
License	Open source

Regional Energy Deployment System. It is a comprehensive, open-source energy planning model developed by the National Renewable Energy Laboratory to simulate the evolution of the U.S. electric sector under different policy, technology, and market conditions. The system provides granular, long-term forecasts to inform decisions by federal agencies, state governments, utilities, and research institutions.

Overview and Purpose

The primary purpose is to assess the potential impacts of energy policies, renewable technologies, and conventional fuels on the power generation mix, emissions, and system costs. It was designed to address the limitations of earlier models by offering higher spatial and temporal resolution, allowing analysis at the level of individual balancing areas or NERC regions. This capability supports the EPA's regulatory analyses and state-level initiatives like those under the California Air Resources Board.

Key Components and Architecture

The architecture integrates several core modules, including a capacity expansion model that determines optimal investment in technologies like solar PV, wind power, and natural gas plants. A dispatch module simulates hourly operation of the grid to meet demand, considering constraints from variable resources and transmission infrastructure. Data inputs draw from sources such as the Annual Energy Outlook published by the Energy Information Administration and technology cost projections from DOE reports. The system is built using the Python programming language and leverages libraries like Pandas for data analysis.

Modeling and Analysis Capabilities

Its capabilities include simulating the effects of renewable portfolio standards, carbon taxes, and federal tax credits on technology adoption. The model can project levelized costs, water use, and air pollutant emissions from sectors like transportation if integrated with electrification scenarios. It employs linear programming and mixed-integer optimization techniques to solve for least-cost system configurations, with validation against historical data from FERC and MISO.

Applications and Use Cases

Notable applications include supporting the Clean Power Plan analysis for the EPA and evaluating deep decarbonization pathways for states like New York and Colorado. Utilities such as PG&E have used it for integrated resource planning, while researchers at MIT and Stanford University have employed it in studies published in journals like Nature Energy. The model has also been applied to assess resilience benefits of distributed energy resources following events like Hurricane Sandy.

Development and History

Initial development began in 2009 at the National Renewable Energy Laboratory with funding from the DOE's Office of Energy Efficiency and Renewable Energy. Key figures in its creation included researchers like David H. Bielen and Daniel Steinberg. The model evolved from earlier tools such as the Wind Energy Deployment System and has undergone major updates, including enhancements to represent storage technologies and HVDC transmission. Its codebase was released under an open-source license in 2016, fostering collaboration with institutions like the Electric Power Research Institute.

Integration with Energy Planning

The system is designed to interface with other prominent models, such as the GCAM from the PNNL and the EnergyPATHWAYS model, providing detailed electric sector insights for broader energy system or economic analyses. This integration helps inform comprehensive strategies at organizations like the World Resources Institute and the International Energy Agency. By aligning with frameworks from the IPCC, it supports the creation of consistent, transparent scenarios for national and subnational climate mitigation planning.

Category:Energy modeling Category:Energy in the United States Category:Open-source software