Scenario reduction techniques and solution stability for stochastic unit commitment problems

© 2016 IEEE. In power systems rife with uncertainty, stochastic unit commitment (SUC) models may be used to properly size and allocate operational reserves, in order to ensure a reliable and cost-efficient operation of the power system. The performance of SUC-based unit commitment schedules is however fully dependent on the scenario sets used to describe the uncertainty at hand. Dedicated scenario generation & reduction techniques (SGT & SRT) have been developed to generate and select scenario sets that capture the uncertain parameter, e.g. wind power, and yield a cost-optimal unit commitment (UC) schedule in reasonable computing times. Probability-distance based SRTs are by far the most used. In an extensive numerical study, we analyze the performance of so-called cost functions used in these SRTs. In addition, we propose a new cost function, which allows selecting a well-balanced subset of scenarios, resulting in a tractable SUC model and a cost-optimal UC schedule.status: publishe