Unit Commitment with Cost-Oriented Temporal Resolution

TL;DR

This paper introduces a cost-oriented, bilevel optimization approach for adaptive unit commitment scheduling that reduces costs by dynamically adjusting temporal resolution, outperforming traditional clustering methods.

Contribution

It presents a novel bilevel optimization framework and heuristic algorithms to better align temporal resolution with cost minimization in unit commitment.

Findings

Significant cost reductions achieved without increasing temporal periods.

The proposed method outperforms clustering-based approaches.

Enhanced scheduling efficiency demonstrated through numerical tests.

Abstract

Time-adaptive unit commitment (UC) has recently been investigated to reduce the scheduling costs by flexibly varying the temporal resolution, which is usually determined by clustering the net load patterns. However, there exists a misalignment between cost and net load patterns due to the discrete start-up costs and out-of-merit-order dispatch triggered by ramping and other constraints. The optimal time-adaptive resolution cannot be completely captured by clustering-based method. This paper proposes a cost-oriented method to address this misalignment by a novel bilevel optimization approach that is efficiently solved through a heuristic greedy algorithm. The impact of varying temporal resolution on the final scheduling costs are tested, based on which the temporal resolution is heuristically updated, achieving significant cost reduction without increasing the number of temporal periods. Subsequently, an improved discretized Adam optimization method together with offline warm start and online refinement strategy is proposed to efficiently search for the better temporal resolution configuration. Results show that the proposed cost-oriented UC temporal resolution determination method achieves enhanced cost efficiency.