Journal Article10.1016/J.IJEPES.2021.107359
Two-stage robust optimization dispatch for multiple microgrids with electric vehicle loads based on a novel data-driven uncertainty set
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TL;DR: The present work addresses the need to reduce the operating cost of multi-microgrids and improve the convergence performance of the solution algorithms applied for their optimized electric power dispatch when considering the uncertainties associated with existing loads, renewable energy sources, and electric vehicle usage by proposing a novel double-layer robust optimization dispatch model.
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About: This article is published in International Journal of Electrical Power & Energy Systems. The article was published on 01 Jan 2022. The article focuses on the topics: Robust optimization & Microgrid.
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Citations
Distributed power generation planning for distribution networks using electric vehicles: Systematic attention to challenges and opportunities
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Multi-time scale dynamic robust optimal scheduling of CCHP microgrid based on rolling optimization
Zhiping Cheng,Dongqiang Jia,Zhongwen Li,Jikai Si,Shuai Xu +4 more
TL;DR: In this paper , a multi-time scale dynamic robust optimal scheduling strategy is proposed for the coordinated operation of CCHP microgrid, which includes two time scales: the day-ahead scheduling scale and the intraday adjustment scale.
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Projected transition to electric vehicles in India and its impact on stakeholders
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Multi-time scale optimization scheduling of microgrid considering source and load uncertainty
TL;DR: In this article , a multi-time scale optimal scheduling strategy for micro-grids considering the uncertainty of source and load is proposed, which can not only maintain the smooth power of contact lines, but also achieve robust and economic operation of the microgrid.
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A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties
TL;DR: In this article , the authors present and review typical architecture of multi-microgrid, including physical layer, information layer and application layer, and critically review and analyze challenges in uncertainty modelling and solution in MMGs.
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