Scalable Learning and Energy Management for Power Grids
2019-01
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Scalable Learning and Energy Management for Power Grids
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2019-01
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Contemporary power grids are being challenged by unprecedented levels of voltage fluctuations, due to large-scale deployment of electric vehicles (EVs), demand-response programs, and renewable generation. Nonetheless, with proper coordination, EVs and responsive demands can be controlled to enhance grid efficiency and reliability by leveraging advances in power electronics, metering, and communication modules. In this context, the present thesis pioneers algorithmic innovations targeting timely opportunities emerging with future power systems in terms of learning, load control, and microgrid management. Our vision is twofold: advancing algorithms and their performance analysis, while contributing foundational developments to guarantee situational awareness, efficiency, and scalability of forthcoming smart power grids. The first thrust to this end deals with real-time power grid monitoring that comprises power system state estimation (PSSE), state forecasting, and topology identification modules. Due to the intrinsic nonconvexity of the PSSE task, optimal PSSE approaches have been either sensitive to initialization or computationally expensive. To bypass these hurdles, this thesis advocates deep neural networks (DNNs) for real-time PSSE. By unrolling an iterative physics-based prox-linear PSSE solver, a novel model-specific DNN with affordable training and minimal tuning effort is developed. To further enable system awareness even ahead of the time horizon, as well as to endow the DNN-based estimator with resilience, deep recurrent neural networks (RNNs) are also pursued for state forecasting. Deep RNNs leverage the long-term nonlinear dependencies present in the historical voltage time series to enable forecasting, and they are easy to implement. Finally, multi-kernel learning based partial correlations accounting for nonlinear dependencies between given nodal measurements are leveraged to unveil connectivity of power grids. The second thrust leverages the obtained state and topology information to design optimal load control and microgrid management schemes. With regards to EV load control, a decentralized protocol relying on the Frank-Wolfe algorithm is put forth to manage the heterogeneous charging loads. The novel paradigm has minimal computational requirements, and is resilient to lost updates. When higher levels of EV load exceed prescribed voltage limits, the underlying grid needs to be taken into account. In this context, communication-free local reactive power control and optimal decentralized energy management schemes, are developed based on the proximal gradient method and the alternating direction method of multipliers, respectively.
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University of Minnesota Ph.D. dissertation. January 2019. Major: Electrical Engineering. Advisor: Georgios Giannakis. 1 computer file (PDF); ix, 117 pages.
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Zhang, Liang. (2019). Scalable Learning and Energy Management for Power Grids. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/202111.
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