Data-Driven Real-Time Power Dispatch for Maximizing Variable Renewable Generation

TL;DR

This paper introduces a data-driven real-time dispatch method that maximizes variable renewable generation utilization by defining do-not-exceed limits through hybrid stochastic and robust optimization, reducing curtailment.

Contribution

It proposes a novel approach that effectively exploits historical VRG data to optimize dispatch intervals and maximize renewable utilization while maintaining system reliability.

Findings

Reduces VRG spillage compared to traditional methods

Effectively exploits historical data for better dispatch decisions

Improves renewable energy utilization in power systems

Abstract

Traditional power dispatch methods have difficulties in accommodating large-scale variable renewable generation (VRG) and have resulted in unnecessary VRG spillage in the practical industry. The recent dispatchable-interval-based methods have the potential to reduce VRG curtailment, but the dispatchable intervals are not allocated effectively due to the lack of exploiting historical dispatch records of VRG units. To bridge this gap, this paper proposes a novel data-driven real-time dispatch approach to maximize VRG utili-zation by using do-not-exceed (DNE) limits. This approach defines the maximum generation output ranges that the system can ac-commodate without compromising reliability. The DNE limits of VRG units and operating base points of conventional units are co-optimized by hybrid stochastic and robust optimization, and the decision models are formulated as mixed-integer linear programs by the sample average approximation technique exploiting historical VRG data. A strategy for selecting historical data samples is also proposed to capture the VRG uncertainty more accurately under variant prediction output levels. Computational experiments show the effectiveness of the proposed methods.