Collaborative Research: Stochastic Multi-scale Optimization for Energy Resource Planning

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The optimal design and control of energy resource portfolios requires solving a multiperiod stochastic optimization problem that covers both fine-grained and coarse-grained types of uncertainty over thousands of time periods, extending decades into the future. We need to plan investments into energy resources such as wind, solar, natural gas, biomass and nuclear to meet specific energy goals, capturing both hourly variations in intermittent energy and demand, in addition to changes in technology, policy and climate. This problem produces a very high-dimensional stochastic optimization problem with hundreds of thousands of time periods. We will combine the strengths of approximate dynamic programming (ADP) and machine learning to handle the fine-grained sources of uncertainty (wind, solar, demand) with generalized stochastic decomposition (GSD) to handle coarse-grained uncertainties (changes in technology, policy and climate). Developments using GSD will make it possible to handle complex intertemporal dependencies in the evolution of technology and policy. We are investigating new Dirichlet mixture models and learning rates to enhance the speed and robustness of ADP algorithms to handle more complex problems.This research will make it possible to evaluate new energy generation and storage technologies with far more realism than older models by properly accounting for uncertainties and producing a more accurate estimate of the marginal value of different technologies. We will gain a better understanding of the most important parameters such as responsiveness, storage capacity and losses. This research will also enhance our ability to develop robust policies to meet goals such as 20 percent renewable by 2030. A broader methodological benefit will be the integration of the fields of stochastic programming and approximate dynamic programming, which have evolved along parallel but separate paths with distinctly different vocabularies, oriented toward different problem classes.