Extreme Scenario Characterization for High Renewable Energy Penetrated Power Systems over Long Time Scales

TL;DR

This paper introduces novel risk indices and a scenario generation method to identify and analyze extreme long-term power shortages and fluctuations in high renewable energy systems, improving system reliability.

Contribution

It proposes new risk indices and a Gaussian mixture model-based approach for extreme scenario characterization over long time scales in renewable-heavy power systems.

Findings

The risk indices effectively quantify severity of shortages and fluctuations.

The scenario generation method accurately identifies critical extreme events.

Case studies validate the method's utility in enhancing system security.

Abstract

Power systems with high renewable energy penetration are highly influenced by weather conditions, often facing significant challenges such as persistent power shortages and severe power fluctuations over long time scales. This paper addresses the critical need for effective characterization of extreme scenarios under these situations. First, novel risk indices are proposed to quantify the severity of continuous power shortages and substantial power fluctuations over long-term operations. These indices are independent of specific scheduling strategies and incorporate the system's resource regulation capabilities. By employing a filtering-based approach, the proposed indices focus on retaining key characteristics of continuous power shortages and fluctuation events, enabling the identification of extreme scenarios on long time scales. Secondly, an extreme scenario generation method is developed using Gaussian mixture models and sequential Monte Carlo simulation. Especially, this method periodically evaluates the severity of generated scenarios based on the defined risk indices, retaining extreme scenarios while discarding less critical ones. Finally, case studies based on real-world data demonstrate the efficacy of the proposed method. The results confirm that integrating the identified extreme scenarios significantly enhances the system's ability to ensure long-term security and reliability under high renewable energy penetration.