Computing Optimal Resilience-Oriented Operation of Distribution Systems Considering Heterogenous Consumer-Side Microgrids

Mobile battery energy storage systems (MBESs), also called power-to-mobility, have been field-validated as backup generation resources. However, the role of multi-energy coordination towards resilient distribution systems (DSs) against large area disasters is still not yet involved. To cope with such challenges, this paper proposes a computing optimal resilient operation model for post-disaster microgrid-penetrated DS recovery based on the energy-to-mobility approach. In this model, mobile multi-energy storages (MMESs) are invested and routed based on their different transportation mobility, while DS with consumer-side biogas-solar-wind multi-energy microgrids are topologized by network reconfiguration. The coordinated resilient operation of different recovery actions of DS, MMESs, and consumer-side microgrids is a difficult optimization problem as results of its inherent nonconvexities, temporal-spatial mobility and strong multi-energy couplings. The original mixed-integer non-linear programming (MINLP) problem is thus equivalently linearized and reformulated into a mixed-integer linear programming (MILP) for computation performance improvement. The proposed methodology is benchmarked and performed on a modified radial DS with three consumer-side microgrids. Numerical studies demonstrate the effectiveness and superiority of the energy-to-mobility scheme over MBESs on system resilient level and economy.

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Publication Details

Journal
IEEE Transactions on Consumer Electronics
Published
2024-10-07
DOI
https://doi.org/10.1109/tce.2024.3474769
Citations
6
Primary Topic
Optimal Power Flow Distribution
Type
article
Field-Weighted Citation Impact
1.25

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article

Computing Optimal Resilience-Oriented Operation of Distribution Systems Considering Heterogenous Consumer-Side Microgrids

Ziyi Bai, Bin Zhou, Da Xu, Xiaodong Yang et al.
6 citations
IEEE Transactions on Consumer Electronics
Optimal Power Flow Distribution
1.25
article

Computing Optimal Resilience-Oriented Operation of Distribution Systems Considering Heterogenous Consumer-Side Microgrids

Ziyi Bai, Bin Zhou, Da Xu, Xiaodong Yang, Ju Liu, Hanyu Yang
article en
6 citations

Abstract

Mobile battery energy storage systems (MBESs), also called power-to-mobility, have been field-validated as backup generation resources. However, the role of multi-energy coordination towards resilient distribution systems (DSs) against large area disasters is still not yet involved. To cope with such challenges, this paper proposes a computing optimal resilient operation model for post-disaster microgrid-penetrated DS recovery based on the energy-to-mobility approach. In this model, mobile multi-energy storages (MMESs) are invested and routed based on their different transportation mobility, while DS with consumer-side biogas-solar-wind multi-energy microgrids are topologized by network reconfiguration. The coordinated resilient operation of different recovery actions of DS, MMESs, and consumer-side microgrids is a difficult optimization problem as results of its inherent nonconvexities, temporal-spatial mobility and strong multi-energy couplings. The original mixed-integer non-linear programming (MINLP) problem is thus equivalently linearized and reformulated into a mixed-integer linear programming (MILP) for computation performance improvement. The proposed methodology is benchmarked and performed on a modified radial DS with three consumer-side microgrids. Numerical studies demonstrate the effectiveness and superiority of the energy-to-mobility scheme over MBESs on system resilient level and economy.

IEEE Transactions on Consumer ElectronicsVol. 71(2)
Nanjing Tech University (CN), Hefei University of Technology (CN), Hunan University (CN), China University of Geosciences (CN), First People’s Hospital of Jingmen (CN), Hubei University of Technology (CN)
National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province
Openalex Percentile: Top 20%
Optimal Power Flow Distribution
1.25
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