Decision making under coupled multi-timescale uncertainty: Advanced electric power systems planning

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The objective of this research is to develop improved tools for planning for electric power systems. The approach of this research is to combine analysis at hourly and annual timescales so that constraints on how electricity generators operate are accounted for in long-term investment planning for generation technologies. Such constraints are particularly important with increasing use of renewables, storage, and responsive demand. The project will apply approximate dynamic programming and traditional integer optimization techniques to explore decisions under uncertainty in both time scales. The structure of the operations and investment sub-problems will be exploited to develop efficient methods for optimizing the full system, accounting for uncertainty in demand, renewable generation, fuel prices, and possible environmental regulations.Intellectual MeritThis project will develop new methods for optimizing large engineering systems under uncertainty by developing new algorithms and data structure designs. It will also advance the state-of-the art for multi-timescale decision models where the smaller timescale is computationally expensive. Broader ImpactsThis work will significantly improve planning for advanced electric power systems that combine renewables and other advanced technologies to meet environmental and energy requirements, and will identify system designs with lower costs and greater resiliency. The methods developed will be usable by power companies, independent system operators, and variety of governmental and non-governmental agencies that are in the process of designing the next-generation power system. This project will also provide education and training to undergraduate and graduate students, including women and underrepresented minorities, in operations research and power systems modeling.