极端灾害下有功-无功协同优化的两阶段配电网韧性提升策略OA

Extreme weather including typhoons,earthquakes and torrential rain disrupt power systems through distinct mechanisms,yet all induce multi-point failures.While focusing on distribution line damage scenarios,the proposed methodology applies equally to other equipment failures.Typhoons represent a high-impact archetype—causing widespread blackouts and equipment destruction—serving as a representative disturbance.This research addresses post-disaster resilience enhancement for active distribution networks under extreme typhoon scenarios characterized by two conditions:complete disconnection of affected distribution lines and isolation from transmission grids due to substation failures.During such events,remote controll switches partition distribution systems into self-powered islanded microgrids,coordinating operational distributed energy resources to restore critical loads.Simultaneously,distribution networks face challenges from fluctuating load demands and renewable generation.Insufficient reactive power can trigger voltage instability or even cause islanded microgrids to collapse.Given higher R/X ratios in distribution networks compared to transmission systems,neglecting reactive power distribution exacerbates voltage drops at nodes,leading to voltage limit violations and additional load shedding.Furthermore,unified optimization across a single timescale fails to leverage the full potential of controllable devices networks due to their heterogeneous response speeds,yielding suboptimal strategies in active distribution. This study proposes a two-stage islanded microgrid partitioning strategy.This strategy coordinates active and reactive power optimization while dispatching multiple devices across temporal scales,dynamically partitioning distribution systems into self-powered microgrids to ensure continuous reliable power supply for critical loads,thereby enhancing post-outage resilience.As electromechanical devices,remote controll switches and capacitor banks should avoid frequent operations.Thus,these devices were optimized at long-term timescales(set to 1-hour intervals)to minimize unnecessary switching and prolong service life.In contrast,distributed energy resources and static var compensators enable continuous control and rapid response to power fluctuations.Consequently,they were optimized at short timescales(20-minute intervals)for real-time system state adjustments.This timescale differentiation balances system responsiveness against equipment durability,with adjustable intervals accommodating operational requirements across scenarios.In the first stage,renewable generation and load demand forecasts for the next time window informed long timescale optimization of remote controlled switches,capacitor banks,and energy storage charge/discharge strategies.The second stage leveraged updated high-accuracy forecasts and the first stage decisions to refine distributed resource outputs and static var compensator operations at short timescales,compensating for prediction errors from the first stage.The coupled two-stage mechanism enhanced extreme-scenario resilience through robust the first stage decisions while enabling real-time error mitigation in the second stage.A rolling-horizon optimization dynamically updated source-load forecasts,synchronously adjusting network topology and equipment outputs to maximize decision timeliness. Simulations on a modified IEEE 33-node system demonstrated that:(1)The two-stage optimization framework effectively addresses renewable generation and load demand uncertainty through long timescale and short timescale coordination.Compared to the first stage robust strategies,the second Stage real-time dispatch enhanced load restoration outcomes,validating the critical function of dynamic adjustment mechanisms in eliminating source-load uncertainty impacts.(2)The coordinated optimization of active and reactive powers improved voltage profiles while enhancing load recovery,strengthening resilient distribution systems'post-disaster recovery capability and ensuring stable operation under extreme events.Versus conventional single-timescale optimization,this approach further increased load restoration by matching differentiated device response characteristics while maintaining system stability.