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.

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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

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article

Network Topology Flexibility-Aware Robust Electricity Trading for Distribution System Survivability Enhancement

Canqi Yao, Cheng Ma, Shunbo Lei, Chong Wang et al.
3 citations
IEEE Transactions on Smart Grid
Smart Grid Energy Management
2.19
article

Network Topology Flexibility-Aware Robust Electricity Trading for Distribution System Survivability Enhancement

Canqi Yao, Cheng Ma, Shunbo Lei, Chong Wang, Mingze Xu, Weimin Wu
article en
3 citations

Abstract

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.

IEEE Transactions on Smart GridVol. 16(3)
Hong Kong Polytechnic University (HK), Hohai University (CN), Shenzhen Research Institute of Big Data (CN), Chinese University of Hong Kong, Shenzhen (CN)
National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 13%
Smart Grid Energy Management
2.19
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