CyberSEES Type 2: Achieving Clean Power System Flexibility: Sensing, Modeling, and Optimal Control

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Growing renewable generation capacity is leading to unprecedented variability and uncertainty in power system operations, and a lack of power system flexibility threatens further increases in global sustainability in electricity sectors across the globe. This project seeks to aggregate and control demand-side resources to provide this flexibility through a distributed control framework that is robust to failures in cyber-infrastructure, and is scalable across institutional dynamics, energy markets, and levels of power system development infrastructure. Research objectives in this proposal will advance current applications and theories in computing, modeling and control, and the effectiveness and scalability of this work will be rigorously demonstrated through a field trial in Nicaragua. This project will result in new tools to facilitate sustainable energy systems throughout the globe, an open data platform that strengthens the ability of global researchers to develop global solutions to challenges underling the integration of renewable sources, and advances in education to equip the workforce with new tools to tackle sustainability challenges. The envisioned system employs a centralized computing infrastructure to produce optimal control laws for individual thermostatically controlled loads. The control laws can operate with or without reliable cyber-infrastructure in real time; in the absence of cyber-infrastructure, control can be based on time of use and local frequency measurements only. Control laws will be published by a central server and periodically accessed by local intelligence at each load. Challenges to this work include creating computing infrastructure that can actively distribute control laws and validate participation, modeling and control methods that are effective on real-world loads, and identifying control strategies with effective incentive mechanisms for consumers to participate. At the conclusion of the project the investigators will have: (1) built a framework by which an innovative, cloud-based data management platform called Mezuri can robustly track the provenance and processing of all data and disseminate control laws; (2) developed hierarchical statistical models for state estimation and prediction of large fleets of thermally controlled loads that directly include the effects of human behavior, and adapt models to require as few observations as possible; and (3) devised new stochastic optimal control formulations that account for human activity and accommodate both system- and local-level objectives. Taken together, these contributions build a new framework for control of demand-side resources that is globally scalable in a short time horizon.