Robust Generation Dispatch with Strategic Renewable Power Curtailment and Decision-Dependent Uncertainty

TL;DR

This paper introduces a robust two-stage generation dispatch model that optimizes renewable curtailment schedules considering decision-dependent uncertainty, enhancing operational reliability amidst renewable integration.

Contribution

It develops a novel adaptive algorithm to solve the complex model efficiently, demonstrating its effectiveness through numerical validation.

Findings

The model effectively manages renewable uncertainty and reduces operational risks.

The proposed algorithm guarantees finite convergence to optimal solutions.

Numerical tests confirm the model's scalability and practical applicability.

Abstract

As renewable energy sources replace traditional power sources (such as thermal generators), uncertainty grows while there are fewer controllable units. To reduce operational risks and avoid frequent real-time emergency controls, a preparatory schedule of renewable generation curtailment is required. This paper proposes a novel two-stage robust generation dispatch (RGD) model, where the preparatory curtailment schedule is optimized in the pre-dispatch stage. The curtailment schedule will then influence the variation range of real-time renewable power output, resulting in a decision-dependent uncertainty (DDU) set. In the re-dispatch stage, the controllable units adjust their outputs within the reserve capacities to maintain power balancing. To overcome the difficulty in solving the RGD with DDU, an adaptive column-and-constraint generation (AC\&CG) algorithm is developed. We prove that the proposed algorithm can generate the optimal solution in finite iterations. Numerical examples show the advantages of the proposed model and algorithm, and validate their practicability and scalability.