Network Topology Flexibility-Aware Robust Electricity Trading for Distribution System Survivability Enhancement
Extreme weather events significantly threaten power system security and market operations, causing either substantial load fluctuations or grid line failures. Current distribution-level electricity market mechanisms often prove inadequate during such events. Furthermore, limited research has explored electricity market mechanisms for improving distribution system survivability. To fill this gap, this study proposes a double-auction mechanism to facilitate distribution-level transactions of non-utility distributed energy resources through market incentives for proactive system resilience enhancement. This mechanism aims to utilize a market-driven approach to achieve the highest system survivability in disasters. This trading mechanism is divided into two stages to address the direct changes in the tradability caused by line failures: clearing and delivery. During market clearing, the DSO operates as an auctioneer to concurrently optimize social welfare and prior-event network topology. The pricing rule employs an enhanced Vickrey-Clarke-Groves mechanism to ensure truthful bidding and incentive compatibility. During delivery, the DSO performs redispatch to mitigate economic losses and compensates for energy curtailments through established settlement protocols. This market-driven resilience enhancement method formulates a bi-level two-stage robust optimization problem, solved using a customized column-and-constraint generation algorithm. Modified IEEE 13-node and 123-node systems are used to verify the effectiveness of the proposed approach.
Authors
- Canqi Yao (ORCID: https://orcid.org/0000-0002-7890-9324)
- Cheng Ma (ORCID: https://orcid.org/0009-0008-6149-8291)
- Shunbo Lei (ORCID: https://orcid.org/0000-0001-8169-0821)
- Chong Wang (ORCID: https://orcid.org/0000-0003-2076-9610)
- Mingze Xu (ORCID: https://orcid.org/0009-0003-8770-347X)
- Weimin Wu (ORCID: https://orcid.org/0000-0002-9326-152X)
Institutions
- Hong Kong Polytechnic University (HK)
- Hohai University (CN)
- Shenzhen Research Institute of Big Data (CN)
- Chinese University of Hong Kong, Shenzhen (CN)
Publication Details
- Journal
- IEEE Transactions on Smart Grid
- Published
- 2025-03-11
- DOI
- https://doi.org/10.1109/tsg.2025.3550363
- Citations
- 3
- Primary Topic
- Smart Grid Energy Management
- Type
- article
- Field-Weighted Citation Impact
- 2.19
Funders
- National Natural Science Foundation of China
- Basic and Applied Basic Research Foundation of Guangdong Province