An optimized demand response framework for enhancing power system reliability under wind power and EV-induced uncertainty
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 reliabil...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-05482-3 |
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| Summary: | 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. |
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| ISSN: | 2045-2322 |