Efficient Scenario Generation for Chance-constrained Economic Dispatch Considering Ambient Wind Conditions

TL;DR

This paper introduces a novel scenario generation method for chance-constrained economic dispatch that filters scenarios based on ambient wind conditions, improving efficiency and risk management in power systems.

Contribution

The paper presents a new filtering and incremental scenario generation approach tailored for wind-influenced economic dispatch, addressing risk-tuning challenges in real-world applications.

Findings

Efficient scenario generation reduces resource usage.

The method achieves precise risk control in power dispatch.

Validated on 24-bus and 118-bus systems with real data.

Abstract

Scenario generation is an effective data-driven method for solving chance-constrained optimization while ensuring desired risk guarantees with a finite number of samples. Crucial challenges in deploying this technique in the real world arise due to the absence of appropriate risk-tuning models tailored for the desired application. In this paper, we focus on designing efficient scenario generation schemes for economic dispatch in power systems. We propose a novel scenario generation method based on filtering scenarios using ambient wind conditions. These filtered scenarios are deployed incrementally in order to meet desired risk levels while using minimum resources. In order to study the performance of the proposed scheme, we illustrate the procedure on case studies performed for both 24-bus and 118-bus systems with real-world wind power forecasting data. Numerical results suggest that the proposed filter-and-increment scenario generation model leads to a precise and efficient solution for the chance-constrained economic dispatch problem.