A-Priori Reduction of Scenario Approximation for Automated Generation Control in High-Voltage Power Grids with Renewable Energy

TL;DR

This paper introduces a method to reduce data requirements in stochastic power grid optimization, improving efficiency while maintaining reliability in the context of renewable energy integration.

Contribution

It proposes an a priori sample reduction technique for scenario approximation in chance-constrained DC-OPF with AGC, enhancing computational efficiency.

Findings

Requires up to twice less data than traditional methods

Maintains solution reliability with reduced data size

Demonstrates effectiveness through theoretical and empirical analysis

Abstract

Renewable energy sources (RES) are increasingly integrated into power systems to support the United Nations' Sustainable Development Goals of decarbonization and energy security. However, their low inertia and high uncertainty pose challenges to grid stability and increase the risk of blackouts. Stochastic chance-constrained optimization, particularly data-driven methods, offers solutions but can be time-consuming, especially when handling multiple system snapshots. This paper addresses a dynamic joint chance-constrained Direct Current Optimal Power Flow (DC-OPF) problem with Automated Generation Control (AGC) to facilitate cost-effective power generation while ensuring that balance and security constraints are met. We propose an approach for a data-driven approximation that includes a priori sample reduction, maintaining solution reliability while reducing the size of the data-driven approximation. Both theoretical analysis and empirical results demonstrate the superiority of this approach in handling generation uncertainty, requiring up to twice less data while preserving solution reliability.