Two-Stage Robust Unit Commitment for Co-Optimized Electricity Markets: An Adaptive Data-Driven Approach for Scenario-Based Uncertainty Sets

TL;DR

This paper introduces a data-driven, adaptive scenario-based robust unit commitment model that dynamically updates uncertainty sets for renewable generation, improving solution robustness and computational efficiency in renewable-rich electricity markets.

Contribution

It proposes a novel adaptive data-driven framework for scenario-based uncertainty sets in robust unit commitment, enhancing interpretability and computational tractability over traditional methods.

Findings

Achieves cost-effective and robust solutions in renewable integration scenarios.

Demonstrates computational efficiency with the column-and-constraint generation algorithm.

Effectively captures complex, time-varying renewable uncertainties.

Abstract

Two-stage robust unit commitment (RUC) models have been widely used for day-ahead energy and reserve scheduling under high renewable integration. The current state of the art relies on budget-constrained polyhedral uncertainty sets to control the conservativeness of the solutions. The associated lack of interpretability and parameter specification procedures, as well as the high computational burden exhibited by available exact solution techniques call for new approaches. In this work, we use an alternative scenario-based framework whereby uncertain renewable generation is characterized by a polyhedral uncertainty set relying on the direct specification of its vertexes. Moreover, we present a simple, yet efficient, adaptive data-driven procedure to dynamically update the uncertainty set vertexes with observed daily renewable-output profiles. Within this setting, the proposed data-driven RUC ensures protection against the convex hull of realistic scenarios empirically capturing the complex and time-varying intra-day spatial and temporal interdependences among renewable units. The resulting counterpart features advantageous properties from a computational perspective and can be effectively solved by the column-and-constraint generation algorithm until $\epsilon$-global optimality. Out-of-sample experiments reveal that the proposed approach is capable of attaining efficient solutions in terms of cost and robustness while keeping the model tractable and scalable.