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In modeling battery energy storage systems (BESS) in power systems, binary variables are used to represent the complementary nature of charging and discharging. A conventional approach for these BESS optimization problems is to relax binary variables and convert the problem into a linear program. However, such linear programming relaxation models can yield unrealistic fractional solutions, such as simultaneous charging and discharging. In this paper, we develop a regularized mixed-integer programming (MIP) model for the optimal power flow (OPF) problem with BESS. We prove that, under mild conditions, the proposed regularized model admits a zero integrality gap with its linear programming relaxation; hence, it can be solved efficiently. By studying the properties of the regularized MIP model, we show that its optimal solution is also near optimal to the original OPF problem with BESS, thereby providing a valid and tight upper bound for the OPF problem with BESS. The use of the regularized MIP model allows us to solve a trilevel [Formula: see text]-[Formula: see text]-[Formula: see text] network contingency problem, which is otherwise intractable to solve. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: N. Jiang (as a graduate student at the Georgia Institute of Technology) and W. Xie were supported in part by the National Science Foundation [Grant 2246414] and the Office of Naval Research [Grant N00014-24-1-2066]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0771 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0771 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
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This content will become publicly available on November 28, 2025
An Agile Mobilizing Framework for V2G-Enabled Electric Vehicles under Wildfire Risk
Public safety power shut-offs (PSPSs), implemented by California utilities during high wildfire risk periods, lead to the de-energization of grid sections and leave certain customers out of power for several hours and even days. We propose an agile decision support system (DSS) to mitigate these impacts by harnessing electric vehicles (EVs) as mobile energy sources serving the multiple microgrids (μGs) formed within affected communities. Given that not all μGs possess adequate energy storage and distributed energy resources (DERs), we advocate for the mobilization of vehicle-to-grid (V2G)-enabled EVs for equitable and resilient energy access. Our emergency service relocation (ESR) model incentivizes EV owners to transport stored energy between μGs. However, traditional DSS cannot promptly solve the associated mixed-integer programming (MIP) problem, necessitating a faster solution algorithm for rapid EV deployment under emergency conditions. We introduce a learning framework employing graph convolutional networks (GCNs) that significantly expedites the MIP problem's solution by predicting binary values. Our results demonstrate the effectiveness of the proposed framework in promoting grid resilience and considerably reducing solve time when the problem has 69k binary decision variables.
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- Award ID(s):
- 2302015
- PAR ID:
- 10570087
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Vehicular Technology
- ISSN:
- 0018-9545
- Page Range / eLocation ID:
- 1 to 12
- Subject(s) / Keyword(s):
- Electric Vehicle Resilience Microgrid Mixed-Integer Programming Graph Convolutional Networks
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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