Power system resilience: Optimal risk-based planning with renewable energy sources and energy storage systems

• A resilient scheduling framework integrating RES, BESSs, and traditional systems to address uncertainties and disruptions. • A probabilistic CVaR-based framework mitigating renewable energy variability and adverse event risks with risk-neutral and risk-averse strategies. • The resilience curve evaluates performance pre-, during, and post-failures, showing RESs and BESSs enhance resilience metrics. • A robust optimization framework enhances resilience by managing diverse load demands using RESs, BESSs, and various traditional power units. • The framework’s effectiveness is validated on the IEEE RTS 24-Bus, showcasing operational and resilience improvements. This paper proposes a resilience-oriented, risk-aware day-ahead scheduling framework for power distribution networks with high penetration of renewable energy sources (RES) and battery energy storage systems (BESS). The framework explicitly accounts for renewable generation uncertainty and disruptive events by integrating probabilistic modeling with Conditional Value-at-Risk (CVaR)-based optimization. A stochastic optimization model is developed to coordinate RES, BESS, and conventional generation while satisfying operational and power flow constraints under both normal and islanded conditions. System resilience is quantitatively assessed using resilience curves and a General Resilience Metric (GRM), enabling evaluation of system performance before, during, and after high-impact events. The proposed approach is validated on the IEEE RTS 24-bus test system using real wind, solar, temperature, and load data. Simulation results demonstrate that integrating RES improves overall resilience by 4.66%, while the combined deployment of RES and BESS yields an improvement of 12.98%. Moreover, risk-averse operation significantly mitigates extreme outcomes and enhances recovery performance during islanding periods. The results confirm that data-driven uncertainty modeling combined with risk-aware optimization provides an effective and scalable framework for enhancing the resilience and recovery capability of modern power distribution networks.