Development of Open Source Software for Simulating Energy System Operations
Energy systems around the world face a dual challenge: rapidly decarbonizing (including increasing dependence on variable renewable energy) while also withstanding increasingly severe weather and the longer term impacts of climate change. Consequently, incorporating hydrometeorological stressors into computational energy systems analysis is becoming an even more critical tool in long term planning and short term operations. However, there is a general lack of open-source customizable simulation software capable of exhaustively stress testing energy sysetms under hydrometeorological uncertainty, and/or examining potential risk mitigation pathways. A related, persistent challenge for energy sysetm modelers is striking an appropriate balance between model fidelity (e.g. spatial scale and time resolution) and computational tractability (wall clock run-time) (see figure below).
Our group develops solutions to this problem in the form of open-source software that allows users to seamlessly customize the scale and track the accuracy of operational models of the U.S. bulk electric power system. Our approach allows users to search over numerous model parameters (network topology, mathematical formulation, economic hurdle rates, and transmission line scaling) to identify model instantiations that accommodate experimental design. We then use the software we develop to demonstrate the importance of including extreme weather events in model validation and model selection, and to answer science questions of high societal importance.
Our ongoing efforts in this area include a collaboration with the DOE Office of Science -funded IM3 project, for which the Kern Group has developed interconnection scale power system models for the entire contiguous United States. In addition, with NSF support we are developing an open source daily natural gas market model for North America, which mirrors the topology of the U.S. Energy Information Administration’s natural gas market model used in their Annual Energy Outlook projections. These projects are being linked, and collectively provide unprecedented capabilities to explore the cascading impacts of system shocks (e.g. from extreme weather, cyber attacks, and/or other supply chain disruptions).
Denaro, S., Cuppari, R., Kern, J., Su, Y., Characklis, G. (2022). “Assessing the Bonneville Power Administration’s Financial Vulnerability to Hydrologic Variability“. Journal of Water Resources Planning and Management. Vol. 148, Issue 10. doi: 10.1061/(ASCE)WR.1943-5452.0001590
Akdemir, K., Kern, J.D., Lamontagne, J. (2022). “Assessing risks for New England’s wholesale electricity market from wind power losses during extreme winter storms“. Energy. Vol. 251. https://doi.org/10.1016/j.energy.2022.123886
Wessel, J., Kern, J.D., Voisin, N., Oikonomou, K., Haas, J. (2022). “Technology pathways could help drive the U.S. West Coast grid’s exposure to hydrometeorological uncertainty.” Earth’s Future. Volume 10, Issue 1. https://doi.org/10.1029/2021EF002187
Su, Y., Kern, J.D, Characklis, G. (2022). “The Effects of Retail Load Defection on a Major Electric Utility’s Exposure to Weather Risk” Journal of Water Resources Planning and Management. Volume 148, Issue 3.
Hill, J., Kern, J.D, Rupp, D., Voisin, N., Characklis, G. (2021). “The Effects of Climate Change on Interregional Electricity Market Dynamics on the U.S. West Coast” Earth’s Future. Volume 9, Issue 12. https://doi.org/10.1029/2021EF002400
Su, Y., Kern, J.D., Reed, P., Characklis, G. (2020). “Compound Hydrometeorological Extremes Across Multiple Timescales Drive Volatility in California Electricity Market Prices and Emissions”. Applied Energy.
Kern, J.D., Su, Y., Hill, J. (2020). “A retrospective study of the 2012-2016 California drought and its impacts on the power sector.” Environmental Research Letters. 15 094008
Su, Y., Kern, J., Denaro, S., Hill, J., Reed, P., Sun, Y., Cohen, J., Characklis, G. (2020). “An open source model for quantifying risks in bulk electric power systems from spatially and temporally correlated hydrometeorological processes” Environmental Modelling and Software. Vol. 126.