Dispatch-Embedded Long-Term Tail Risk Assessment and Mitigation via CVaR for Renewable Power Systems
This addresses long-term risk assessment and mitigation for renewable power systems, which is incremental as it builds on existing models by explicitly including dispatch strategies.
The paper tackles the underestimation of long-term power supply risks in renewable energy systems by proposing a framework that embeds dispatch strategies, using CVaR to quantify tail risks and refining dispatch for mitigation, with case studies on a modified IEEE-39 bus system showing efficacy.
Renewable energy (RE) generation exhibits pronounced seasonality and variability, and neglecting these features can lead to significant underestimation of long-term power system risks in power supply. While long-term dispatch strategies are essential for evaluating and mitigating tail risks, they are often excluded from existing models due to their complexity. This paper proposes a long-term tail risk assessment and mitigation framework for renewable power systems, explicitly embedding dispatch strategies. A representative scenario generation method is designed, combining multi-timescale Copula modeling to capture RE's long-range variability and correlation. Building on these scenarios, an evolution-based risk assessment model is established, where Conditional Value-at-Risk (CVaR) is employed as a robust metric to quantify tail risks. Finally, a controlled evolution-based risk mitigation scheme is introduced to refine long-term dispatch strategies for mitigating tail risks. Case studies on a modified IEEE-39 bus system incorporating real-world data substantiate the efficacy of the proposed method.