Robust Generation Dispatch with Purchase of Renewable Power and Load Predictions

TL;DR

This paper introduces a robust two-stage dispatch model that optimally purchases and utilizes renewable and load predictions, improving decision accuracy amid uncertainty in power systems.

Contribution

It proposes a novel robust optimization framework with decision-dependent uncertainty and a specialized algorithm for efficient solution in power dispatch.

Findings

The model enhances dispatch robustness against prediction uncertainties.

The proposed algorithm guarantees convergence and optimality.

Case studies validate the model's effectiveness and scalability.

Abstract

The increasing use of renewable energy sources (RESs) and responsive loads has made power systems more uncertain. Meanwhile, thanks to the development of advanced metering and forecasting technologies, predictions by RESs and load owners are now attainable. Many recent studies have revealed that pooling the predictions from RESs and loads can help the operators predict more accurately and make better dispatch decisions. However, how the prediction purchase decisions are made during the dispatch processes needs further investigation. This paper fills the research gap by proposing a novel robust generation dispatch model considering the purchase and use of predictions from RESs and loads. The prediction purchase decisions are made in the first stage, which influence the accuracy of predictions from RESs and loads, and further the uncertainty set and the worst-case second-stage dispatch performance. This two-stage procedure is essentially a robust optimization problem with decision-dependent uncertainty (DDU). A mapping-based column-and-constraint generation (C&CG) algorithm is developed to overcome the potential failures of traditional solution methods in detecting feasibility, guaranteeing convergence, and reaching optimal strategies under DDU. Case studies demonstrate the effectiveness, necessity, and scalability of the proposed model and algorithm.