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Optimizing a unit commitment problem using an evolutionary algorithm and a plurality of priority lists

Vasilios A. Tsalavoutis (), Constantinos G. Vrionis and Athanasios I. Tolis
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Vasilios A. Tsalavoutis: National Technical University of Athens
Constantinos G. Vrionis: National Technical University of Athens
Athanasios I. Tolis: National Technical University of Athens

Operational Research, 2021, vol. 21, issue 1, No 1, 54 pages

Abstract: Abstract The Unit Commitment Problem (UCP) is an operational research problem commonly encountered in energy management. It refers to the optimum scheduling of the generating units in a power system to efficiently meet the electricity demand. UCP comprises two interrelated sub-problems: the Unit Commitment for deciding the operating state of the units at each scheduling period and the Economic Dispatch (ED) for allocating the demand among them. Various Evolutionary Algorithms (EA) have been adopted for solving UCP, commonly assisted by the Lambda iteration method for solving the ED. In this study, an EA-based method is proposed for dealing with both sub-problems, avoiding binary variables through a simple transformation function. The method takes advantage of a repair mechanism utilizing the Priority List (PL) to steer the search towards adequate generating schedules. The impact of the cost metric chosen for creating the PL on the computational results is investigated and the use of a Plurality of PL is suggested to alleviate the biases introduced by employing constant cost metrics. Furthermore, an Elitist Mutation strategy is developed to enhance the performance of the proposed EA-based method. Simulation results on various power systems validate the beneficial effect of the proposed modifications. Compared to state of the art, the algorithm proposed has been at least equivalent, exhibiting consistently solutions of lower or competitive costs in all systems examined.

Keywords: Unit Commitment Problem; Priority List; Ramp rate constraints; Differential Evolution; Feasibility Rules; Evolutionary Algorithms (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s12351-018-0442-x

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