Contingency management in power distribution networks via a decentralized contingency-aware energy trading framework

In recent years, the integration of decentralized local energy markets, such as Peer-to-Peer (P2P) trading, into power distribution networks has gained considerable attention. However, physical infrastructure failures can disrupt trading agreements, resulting in power imbalances, curtailment, and economic losses in real-time operation. Moreover, the disruptions may threaten the market structure and trust between market stakeholders. To address these challenges and mitigate the costs of unexpected outages, this study proposes a novel contingency-aware, reliability-oriented trading framework that explicitly incorporates network contingencies, such as line outages, into the market-clearing process. Unlike deterministic N − 1 security approaches that enforce strict post-contingency feasibility under any single outage, the proposed framework adopts a scenario-based formulation in which reliability impacts are internalized through expected reliability metrics. This enables risk-informed and economically efficient decision-making while avoiding excessive conservativeness. Market participants solve localized scenario-based optimization problems to account for credible contingencies in their scheduling decisions and to determine economically optimal contingency reserves. The market-clearing process is implemented using an Alternating Direction Method of Multipliers (ADMM) framework, preserving participant privacy while coordinating system-wide reliability considerations. Implementation on modified 22-bus and 37-bus distribution networks demonstrates that the proposed framework reduces operational costs and improves reliability performance under credible contingency scenarios in the tested systems. The results provide the evidence of promising scalability in the examined systems and scenario sets, suggesting the potential for cost-efficient and reliability-aware decentralized energy trading at the distribution level.