MODELS AND DATA
The Power Lab specializes in model-based collaboration with a range of scholars and partners in order to capture requisite technical detail and an understanding of the political economy of energy systems. We aim to provide open and broadly accessible data, models and insights, which offer several advantages:
- Facilitating replicability and transparency with respect to assumptions that affect model results
- Taking the power of scenario creation out of the hands of entrenched incumbents
- Encouraging more creative uses of modeling tools to explore non-technical elements of system transitions
The Electricity Market Tracker (EMT) 中国新型电力市场研究库 is a bilingual open data and model platform that supports in-depth research on China’s electricity market and policies in collaboration with the Regulatory Assistance Project. EMT utilizes regional unit commitment and economic dispatch (UCED) models to track and analyze the rapidly evolving power sector reforms of China.
The Power Systems Optimization Course materials provide an introduction to the theory and mathematics of constrained optimization problems and develop several canonical problems in electric power systems planning and operations, including: economic dispatch, unit commitment, optimal network power flow, and capacity planning. The materials are co-developed with Princeton University.
The RESPO model is a spatially-granular capacity planning and operations optimization to explore the coupled land and grid implications of renewable energy development in China co-developed with Tsinghua University. Further information on the methodology and obtaining data to run the model can be obtained from Zhang et al. (2024) PNAS.
The Renewable Energy Pathways Model extends the RESPO model co-developed with Tsinghua University to consider multiple planning years and firm resource retirement. See Zhang et al. (2025) Cell Reports Sustainability for 2030/2035 modeling and data, and Zhang et al. (2025) Adv in Applied Energy for decadal modeling and data.
We have developed an Indonesia island and industrial park model that optimizes capacity planning and operations in major island grids. It optimizes emissions reductions in captive power facilities considering plant-level options (e.g., captive renewable energy) and grid upgrades. Read A Gnapathy, Chen & Davidson (2025) for the methodology and data access.
We have developed a Western U.S. capacity expansion model that optimizes resources and operations subject to different scenarios of market and institutional coordination. See Kucuksayacigil, Zhang & Davidson (2025) Nature Communications for methodology.
Geodata is a library of geospatial data collection and "pre-analysis" tools tailored to renewable energy resource assessments. It is optimized for use with ERA5 and MERRA2 weather data.
The Solar Learning Model is a two-factor learning model of solar technology costs modified to incorporate geopolitical fragmentation in globalized supply chains. It is co-developed with John P. Helveston and Gang He.
We are developing a Vietnam power capacity expansion model that considers 6-zones based on the revised PDP-8 (2025). See our Policy Brief for initial modeling results to 2035. The model is currently being prepared for full release.
Repositories
