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Distribution grids face increasing failure risks from extreme weather, cyber-attacks, aging assets, and high penetrations of distributed energy resources. Traditional contingency planning often depends on limited deterministic scenarios, which can miss compound and low-probability events. This article proposes an original generative-AI decision-support system for distribution-grid contingency planning. The system combines synthetic failure scenario generation, natural-language operator prompts, and risk-aware optimization to support resilient operational decision-making. The DSS generates synthetic line, transformer, load, weather, and cyber-contingency scenarios using generative models trained on historical and simulated grid states. Operator prompts are translated into structured scenario filters, optimization constraints, and explanation requests, while a risk-aware solver recommends reconfiguration and load-shedding actions. Simulation on a realistic distribution feeder shows that synthetic augmentation expands contingency coverage beyond deterministic N-1 analysis. The proposed DSS reduces expected load not served, lowers tail-risk exposure, and decreases operator workload relative to deterministic and non-augmented stochastic baselines. The integrated DSS improves contingency planning by linking generative scenario discovery, interactive human–AI exploration, and optimization-based response selection. Governance requirements related to model reliability, operator trust, privacy, and safe deployment are identified.

Low-carbon flexibility markets are becoming important tools for coordinating distributed energy resources, electric vehicles, storage, and responsive demand. Their practical value depends on how well they manage renewable forecast errors, uncertain prosumer behavior, and distribution-network constraints. This article develops a risk-aware optimization model for local flexibility market clearing under renewable forecast uncertainty, prosumer non-compliance, and feeder operating limits. The purpose is to reduce severe cost, carbon, and reliability outcomes rather than optimizing only average performance. The proposed model combines probabilistic renewable uncertainty, statistical prosumer compliance behavior, linearized distribution-network limits, and a conditional value-at-risk representation. The clearing problem is structured as a tractable optimization model suitable for commercial solvers. The model procures low-carbon flexibility while limiting exposure to delivery shortfalls and network infeasibility. The case study compares the proposed risk-aware clearing approach with deterministic and risk-neutral alternatives. The framework provides a practical risk-management tool for flexibility market operators. It supports reliable low-carbon dispatch while accounting for imperfect forecasts, incomplete prosumer delivery, and feeder constraints.