Robust Unit Commitment and the Promise of Higher Reliability in Electricity Markets

Abstract

Unit commitment (UC), one of the critical tasks in the operations of electricity markets, is an optimization problem in power systems that determine the optimal schedule and dispatch of the generating units in the day-ahead market. UC is a challenging problem due to the many sources of uncertainty such as demand, generators’ failures, transmission lines’ outages, and more importantly, intermittent supply of renewable energy. Comparing with the other uncertainty handling approaches for UC, robust optimization is extensively used to address uncertainty in the UC problem. Robust unit commitment (RUC) results in a higher degree of flexibility and provides a stronger layer of protection against uncertainty for decision makers of the power systems. This research delves into the works done in the RUC problems to shed greater light on existing modeling approaches, definitions of uncertainty, and developed solution methods. The review of the literature reveals that the stage-based, the two-stage in particular, is a popular and effective modeling approach in the RUC problems. The stage-based modeling approach is capable of incorporating any source of uncertainty from different components of the electricity markets in its formulation, typically in the form of budgeted uncertainty sets. Furthermore, hybrid decomposition-based algorithms are tested as effective methods for handling the complexity of the RUC problems. Exploring uncertainty estimation techniques that offer more flexibility such as chance-constraint modeling is a promising research direction for the RUC problem, particularly in the presence of higher degrees of uncertainty. Failure of a generating facility could tremendously affect the reliability of the operations of the electricity markets. However, this source of uncertainty is not addressed properly in the RUC literature. In addition, stage-based modeling approach has substantial potential to handle the complexity of the RUC problems. Lastly, when the problem size increases, the complexity and computational time increase substantially, so the (meta)heuristics, either as a standalone solution method or in combination with the exact methods, are promising approaches for handling the complexity of RUC models in a reasonable time.