CAREER: Stochastic capacity scheduling and control of distributed energy storage enabling stacked services

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

The PI's long-term research objective is to develop approaches to ensure the reliability of electric power systems with massive amounts of fluctuating renewable energy resources by leveraging flexibility from distributed energy resources. As battery costs have decreased, more small-scale distributed batteries have been connected to the network to provide local services. These resources could be aggregated and used to provide grid services when not needed for their local service. Similarly, flexible loads can be aggregated and coordinated to behave like storage and provide multiple services simultaneously. Stacking services improves storage utilization and economics in addition to the ability of the grid to accommodate more renewables, improving its environmental and health impacts along with energy security. The research objective of this proposal is to develop a suite of computationally-tractable optimization and control algorithms that will enable aggregators to optimally coordinate thousands of highly-heterogeneous and distributed small-scale storage resources and loads to provide stacked services. The findings will impact energy policy, specifically the regulations surrounding stacked services. The PI's overall educational objectives are to inspire students to tackle the grand challenges of climate change and energy security, develop curricula that simultaneously teaches electric power systems and builds students' fundamental STEM skills, and increase public awareness and understanding of modern challenges and opportunities in power systems. To this end, the education plan includes four activities: i) international education and outreach in Liberia; ii) public education and outreach via the development of an online energy storage game highlighting the opportunities and challenges associated with distributed energy storage; iii) graduate and undergraduate education; and iv) outreach to practitioners, researchers, and policymakers via short courses, webinars, and talks. The educational activities will promote workforce development through curricular innovation at University of Michigan and the University of Liberia, and outreach to Liberian K-12 and undergraduate women. The research objective is challenging because the power system is uncertain, nonlinear, and high dimensional; sensing and communication systems required for storage coordination are imperfect; and coordination must not negatively impact the distribution network. The research will develop i) stochastic optimization approaches to schedule aggregations of distributed storage to provide stacked services, while managing storage nonconvexities; ii) robust predictive nonlinear control approaches to coordinate aggregations of distributed storage to provide stacked services, leveraging novel aggregate storage models capturing degradation dynamics; iii) aggregate load models (identified with experimental data) representing load flexibility as storage, including the efficiency and degradation associated with load coordination actions and the model error resulting from the use of approximate storage-type models; and iv) optimization/control approaches to coordinate highly-heterogeneous storage and load aggregations. The results will be validated in high-fidelity simulation environments and the most promising approaches will be implemented in a realistic physical test bed. Methodologically, the research will develop i) novel stochastic dual dynamic programming (SDDP) methods for nonconvex models, leveraging emerging extensions of SDDP to integer programming problems, and ii) novel robust predictive control methods that integrate sliding mode control and model predictive control. While past work has proposed methods of combining sliding and model predictive control, they require online optimization whereas we seek a control policy for fast online computation. In summary, the research will benefit the power systems, operations research, and control systems communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.