Dispatch-Embedded Long-Term Tail Risk Assessment and Mitigation via CVaR for Renewable Power Systems

TL;DR

This paper introduces a comprehensive framework for long-term tail risk assessment and mitigation in renewable power systems, explicitly integrating dispatch strategies and using CVaR to quantify and reduce risks.

Contribution

It develops a novel scenario generation and risk assessment model that incorporates long-term dispatch strategies and tail risk mitigation for renewable energy systems.

Findings

The proposed method effectively captures long-range variability of renewable energy.

CVaR-based risk assessment provides a robust measure of tail risks.

Case studies demonstrate significant risk reduction with the new dispatch strategies.

Abstract

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.