Data-driven adaptive robust operation of PV generation in distribution systems

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The objective of this research is to develop a novel data-driven decision support system (DSS) to determine efficient short-term operation strategies for accommodating large-scale PV generation and mitigating its adverse effects on distribution network reliability and security. The proposed DSS will 1) improve the spatiotemporal variability and uncertainty quantification for PV generation in distribution networks; 2) determine the accommodated variability and uncertainty boundaries of PV generation to ensure the economic efficiency and security of the distribution networks; 3) propose cost effective dynamic solutions that incorporate the temporal and spatial variability and uncertainty of demand and supply in the distribution networks; 4) capture the interactions among autonomous entities such as microgrids, distributed energy resources (DERs), and controllable demands; with distribution system operator (DSO). This research plan facilitates rapid dissemination of the generated knowledge to the research and education community. Specifically, it promotes innovative collaboration among graduate and undergraduate students to provide effective solutions for the current challenges in the distribution network operation. This project ensures the highest quality of integrated research and education to meet the emerging workforce and educational needs of the U.S. energy sector by introducing new curriculum for undergraduate and graduate programs, promoting interdisciplinary collaboration, recruiting underrepresented minorities and female students, and developing K-12 outreach activities.The specific objectives of this research are as follows. a) develop a scalable data-driven approach that leverages a multi-task deep learning framework to provide improved spatiotemporal uncertainty measures for the large-scale PV generation in the distribution network. b) quantify the flexibility measures as tertiary regulation services and form distributionally adaptive robust optimization problems to quantify the accommodated spatiotemporal variability and uncertainty. c) provide a tight convex relaxation for the non-convex risk-averse short-term operation problem for the unbalanced distribution networks. The non-convexity in feasibility set is as a result of the introduced integer variables for switching and commitment decisions as well as the unbalanced AC power flow constraints. d) develop decentralized optimization framework to capture the spatial interdependence among the dynamic temporal decisions made by the autonomous entities and the DSO.