Analytical Forecast-Aware Operational Planning for Resilient Distribution Network Operation under Extreme Weather

Le résumé fourni par la source

Extreme weather events introduce highly uncertain and time-dependent failure and recovery processes in electrical distribution networks, increasing the challenge of maintaining adequate service levels during system operation. This paper presents a forecast-aware operational planning framework under stochastic component availability for resilience oriented control of distribution networks under weather-driven uncertainty. The framework explicitly models the stochastic evolution of component availability induced by forecasted failures and repair dynamics and embeds this information into multi-step operational decision making.To keep the optimization computationally manageable, the framework estimates expected ENS and its variability analytically, avoiding repeated scenario sampling during optimization. These analytically derived metrics are embedded into a genetic-algorithm-based reconfiguration strategy, enabling scalable exploration of the decision space. The framework is implemented within a co-simulation environment integrating weather modeling, failure estimation, and grid operation, and is validated using Monte Carlo simulation combined with AC power flow analysis.Results on a benchmark medium voltage distribution network demonstrate that forecast-aware strategies systematically reduce cumulative exposure to extreme events, as measured by empirical CVaR and electrical constraint violation severity, while maintaining acceptable operational costs. In contrast to conventional scenario-based stochastic optimization, the proposed approach incorporates forecast uncertainty through analytical availability estimates within a deterministic multi-step optimization framework.

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