Optimization of unit commitment problem with ramp-rate constraint and wrap-around scheduling

• Published in: Electric power systems research Vol. 177; p. 105948
• Main Authors: Deka, Dimbalita, Datta, Dilip
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.12.2019; Elsevier Science Ltd
• Subjects: Complexity; Evolutionary algorithms; Optimization; Production costs; Repetition; Scheduling; Scheduling algorithms; Turbines; Unit commitment; Unit commitment problem; Wind power; Scheduling; Evolutionary algorithms; Unit commitment problem; Optimization
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2019.105948

•Unit commitment problem (UCP) is investigated to minimize the total production cost.•Wrap-around in scheduling and ramp-rate constraint is implemented in the present UCP.•Three evolutionary algorithms are investigated and their performances are compared.•Best results of each EA are further investigated by other two EAs.•The potentiality of wrap-around scheduling is discussed. The unit commitment problem (UCP) is one of the most studied problems in power systems. The scheduling of the units to meet the forecasted power demand at each time instant at the minimum production cost, subject to a set of system constraints, makes it a complex nonlinear mixed-integer optimization problem. The complexity of the UCP increases further if the dynamic power limit based ramp-rate constraint is also taken into account. In the present work, the problem is made even more complicated by implementing the concept of wrap-around in scheduling, which allows to handle another practical scenario that a planning horizon is the repetition of a production cycle, e.g., a week in the UCP can be handled as the repetition of days, or a month as the repetition of weeks or days. Owing the inadequacy of deterministic methods to tackle this complicated model of the UCP, real-binary variants of three evolutionary algorithms are investigated and their comparative performances on some benchmark instances are analyzed.