An optimized demand response framework for enhancing power system reliability under wind power and EV-induced uncertainty - Hadi Pakbin; Amin Karimi; Mohammad Naseh Hassanzadeh (Scientific Reports)
Abstract 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.