An optimized demand response framework for enhancing power system reliability under wind power and EV-induced uncertainty.

The growing integration of wind energy and electric vehicles (EVs) introduces significant uncertainty and operational complexity to modern power systems. To address these challenges, this paper presents a novel and optimized demand response (DR) framework designed to enhance system reliability while accounting for wind generation variability and the flexible nature of EV loads. The proposed method incorporates a real-time uncertainty model using a statistical mean-standard deviation relationship to dynamically quantify wind power fluctuations. This modeling approach enables the allocation of DR incentives to be adjusted hour-by-hour based on wind volatility, demand elasticity, and EV charging patterns. Additionally, the framework evaluates system reliability through a well-being-based probabilistic assessment, distinguishing between healthy (P(H)), marginal (P(M)), and risk (P(R)) states. The innovation of this study lies in the integration of uncertainty-driven DR optimization with a probabilistic well-being assessment, allowing DR incentives to be adaptively tuned to real-time wind fluctuations-a capability not addressed in existing literature. This approach provides a practical pathway to managing the variability of renewables without over-reliance on costly storage or backup generation. The model is validated on the IEEE RTS-24 bus system under 12 EV penetration and charging scenarios. Results show that the proposed framework improves P(H) from 95.1% (no DR) and 97.2% (non-optimized DR) to 97.44%, reduces unsupplied energy from 52,230 to 51,900 MWh, and lowers DR incentive costs by 5.6%. These findings demonstrate the framework's capability to enhance cost-efficiency and system resilience in renewable-rich, EV-integrated power grids.