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Dongbo Zhao
Researcher at Argonne National Laboratory
Publications - 81
Citations - 3550
Dongbo Zhao is an academic researcher from Argonne National Laboratory. The author has contributed to research in topics: Electric power system & Distributed generation. The author has an hindex of 20, co-authored 72 publications receiving 2159 citations. Previous affiliations of Dongbo Zhao include Tsinghua University & Texas A&M University.
Papers
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Resilient Distribution System by Microgrids Formation After Natural Disasters
TL;DR: A novel distribution system operational approach by forming multiple microgrids energized by DG from the radial distribution system in real-time operations to restore critical loads from the power outage to maximize the critical loads to be picked up.
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Load Modeling—A Review
TL;DR: A thorough survey on the academic research progress and industry practices is provided, and existing issues and new trends in load modeling are highlighted.
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Optimal Scheduling of an Isolated Microgrid With Battery Storage Considering Load and Renewable Generation Uncertainties
TL;DR: By modeling the uncertainty of spinning reserves provided by energy storage with probabilistic constraints, a new optimal scheduling mode is proposed in this paper for minimizing the operating costs of an isolated microgrid (MG) by using chance-constrained programming.
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Optimal distributed generation planning in active distribution networks considering integration of energy storage
TL;DR: In this article, a two-stage optimization method is proposed for optimal distributed generation (DG) planning considering the integration of energy storage in the PG&E 69-bus distribution system.
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Optimal Scheduling of an Isolated Microgrid with Battery Storage Considering Load and Renewable Generation Uncertainties
TL;DR: This paper proposes a new optimal scheduling mode for minimizing the operating costs of an isolated microgrid (MG) by using chance-constrained programming and significantly exceeds the commonly used hybrid intelligent algorithm with much better and more stable optimization results and significantly reduced calculation times.