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This SERDP and ESTCP webinar focuses on DoD-funded research efforts to develop and demonstrate advanced microgrid control algorithms. Specifically, investigators will cover the foundations, objectives, and core demonstration concepts for their respective microgrid control platform research efforts.
“Response Controllable PQ Control for Microgrids: Model-Based Analysis and Data-Driven Implementation” by Dr. Fran Li ( ESTCP Project EW20-5331)
A microgrid may accommodate inverter-based renewable resources for frequency and voltage control (also known as PQ control) in both grid-connected and islanded modes. Existing controllers, such as proportional-integral (PI) controllers, require sophisticated gain tuning and do not track the pre-defined trajectory accurately. To have a customized PQ response, a time-varying-gain based PQ control method for microgrids based on a combination of model-based analysis and data-driven implementation must be utilized. This presentation will cover foundational concepts of this method of PQ control for microgrids and associated benefits. First, model-based analysis proves that time-varying gains with both a constant factor and an exponentially decaying factor can ensure an exponential PQ output trajectory with a pre-defined time constant. Second, models are not always accessible or accurate, and the coefficients of the two factors and the decaying time constant are obtained using a model-free deep reinforcement learning (RL) approach known as the twin delayed deeper deterministic (TD3) policy gradient. Lastly, the method is verified on a modified Banshee microgrid under scheduling reference change, generation loss, and grounded fault. The model-based derivation simplifies the control problem, and the data-driven algorithm addresses real-world microgrid model unavailability and uncertainty. With the hybrid approach, inverters can be freely assigned exponential response time constants without manual gain tuning, which benefits microgrid performance under various disturbances.
“Integrated Microgrid Control Platform” by Dr. Gabor Karsai ( ESTCP Project EW20-5139)
This presentation will cover the main technical objective of the project, which is demonstrating how foundational technology for decentralized microgrid control can be applied in a field environment, as well as project goals, core demonstration concepts, and primary project benefit. The first project goal is to develop advanced, fully distributed microgrid control algorithms that solve the dynamic, real-time reconfiguration, and optimal dispatch problem for networked microgrids. The second project goal is to construct a concrete and functional demonstration based on a distributed software platform – an ‘operating system’ for power grids, that serves as a reference implementation for future installations. The technology to be demonstrated has two ingredients: (1) a collection of advanced, distributed microgrid control algorithms for various control and energy management functions, and (2) a generic, distributed software platform (an operating system like Android for cell phones) that serves as the foundation for a wide variety of software applications for the power grid. The benefit of the project approach is in the reusability of the algorithms and the interfaces across many DoD installations and microgrid use cases, as implemented in highly configurable and reusable software components. This approach builds on an open-source platform that allows for easy integration of state of the art and legacy equipment into a microgrid management system.
Dr. Fangxing “Fran” Li is the James McConnell Professor in Electrical Engineering at the University of Tennessee, Knoxville (UTK) and the campus director of CURENT (Center for Ultra-Wide Area Resilient Electric Energy Transition Networks), a National Science Foundation/Department of Energy Engineering Research Center headquartered at UTK. He is also the editor-in-chief of the Institute of Electrical and Electronics Engineers (IEEE) Open Access Journal of Power and Energy and the former Chair of IEEE Power System Operation, Planning and Economics committee. He has been the project lead of the CURENT Large-scale Test Bed, which received the R&D 100 Award in 2020. His research interests include renewable energy integration, demand response, power markets, power system control, and power system computing. Dr. Li received bachelor’s and a master’s degrees in electrical engineering from Southeast University in Nanjing, China, and a doctoral degree in electrical engineering from Virginia Tech.
Dr. Gabor Karsai is a professor of electrical engineering and computer science at Vanderbilt University and a senior research scientist and associate director at the Institute for Software-Integrated Systems. Dr. Karsai conducts research in the model-based design and implementation of cyber-physical systems, programming tools for visual programming environments, and the theory and practice of model-integrated computing. He has managed several large Defense Advanced Research Projects Agency (DARPA) projects, related to advanced scheduling and resource management algorithms that resulted in a technology transitioned into all tactical aviation squadrons of the U.S. Marine Corps. This fault-adaptive control technology is used in aerospace applications and on an information architecture platform for managed distributed real-time embedded software for fractionated spacecraft. Dr. Karsai received bachelor’s and master’s degrees in electrical engineering, as well as a technical doctoral degree in computer engineering from the Technical University of Budapest. He also received a doctoral degree in electrical and computer engineering from Vanderbilt University.