A multi-period unit commitment problem under a new hybrid uncertainty set for a renewable energy source

• Published in: Renewable energy Vol. 118; pp. 909 - 917
• Main Authors: Melamed, Michal, Ben-Tal, Aharon, Golany, Boaz
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2018
• Subjects: electric power; electricity; energy; greenhouse gas emissions; issues and policy; market prices; markets; Optimization under uncertainty; planning; power plants; renewable energy sources; Robust optimization; uncertainty; Unit commitment; wind; Optimization under uncertainty; Robust optimization; Unit commitment
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2016.05.095

Recently, there is a growing use of renewable energy in the electricity markets due to governmental subsidy aiming to comply with reduced greenhouse gas emission targets. Jointly with its highly volatile generation it greatly affects the operation planning of power plants, particularly, when addressing the unit commitment problem (UCP). The UCP is imperative in electric power system operations. It seeks an operating policy for a system of generating units over a multi-period finite horizon to meet the demand, subject to equipment and physical constraints. We consider a profit based UCP (PUCP) of an energy producer operating in a deregulated market aiming to maximize its profit facing uncertainty in both market price and wind generation. Here, we employ the robust optimization (RO) methodology which provides a feasible solution for any realization of the uncertain parameters within a bounded set, resulting in a guaranteed value of the objective function. This leads to a model, which is a bilinear mixed integer problem. The method we develop in this paper results in a problem, which is notably as difficult to solve without uncertainty. Furthermore, its resulting policy is more successful in meeting the demand for electricity than that of currently used methods. •Apply robust optimization (RO) to the multi-period unit commitment under uncertain wind generation and price.•Introduce a novel modeling of the uncertainty set reflecting more realistically the uncertain wind generation and price.•Solve a generally intractable uncertain problem in meaningful cases with the same complexity as without uncertainty.•Conduct a computational study showing that the RO policy is prominently less risky compared with currently used methods.