Scenario separation-based optimal planning method for renewable electric energy system considering extreme weather event risks

The significant impacts of extreme weather events (EWEs) on renewable energy exacerbate the risk of power imbalances in high renewable energy penetration power systems. However, EWEs exhibit the high-impact, low-probability (HILP) characteristics, traditional planning methods struggle to incorporate their characteristics into optimization models. To this end, this paper proposes a scenario separation-based renewable electric energy system planning method. A two-stage extreme-weather-aware typical scenarios (EWATSs) construction approach is developed for incorporation of EWEs, where the first stage employs a shape-feature extraction-based clustering algorithm to select typical weeks containing both conventional and EWE scenarios, and a shape-preserving time-series aggregation technique is employed in the second stage to reduce computational complexity while maintaining chronological and shape characteristics. An optimal planning model is then proposed to minimize annualized investment cost while mitigating power imbalance escalated by EWEs. The energy balance constraints are innovatively decoupled into separate formulations for conventional and EWE scenarios so that the power imbalance under EWE can be calculated in isolation. A CVaR method is used to quantify tail risk caused by EWEs. Case studies on the Garver 6-bus and HRP 38-bus systems demonstrate that the proposed method effectively alleviates EWE-exacerbated power imbalance while maintaining economics.