Development of a New Unit Commitment Method With Quantum Predator Prey Brain Storm Optimization

ABSTRACT This paper proposes a new method for unit commitment (UC) with Quantum Predator Prey Brain Storm Optimization (QPPBSO). The UC problems may be expressed as a mixed integer nonlinear programming problem in which binary variables mean on/off conditions of units and continuous ones imply their...

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Veröffentlicht in:Electrical engineering in Japan Jg. 218; H. 2
Hauptverfasser: Kawauchi, Yusuke, Mori, Hiroyuki, Chiang, Hsiao‐Dong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken Wiley Subscription Services, Inc 01.06.2025
Schlagworte:
Cost function
Evolutionary computation
Mixed integer
Nonlinear programming
Optimization
Predator Prey BSO
Predators
Quantum BSO
Quantum computing
Steam turbines
Unit commitment
ISSN:0424-7760, 1520-6416
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Zusammenfassung:ABSTRACT This paper proposes a new method for unit commitment (UC) with Quantum Predator Prey Brain Storm Optimization (QPPBSO). The UC problems may be expressed as a mixed integer nonlinear programming problem in which binary variables mean on/off conditions of units and continuous ones imply their output. Recently, Evolutionary Computation (EC) has been applied to the UC problems due to the existence of indifferentiable cost functions such as large‐scale steam turbine units, etc. However, there is still room for improvement in EC because the UC problems have high nonlinear features. This paper focuses on the integration of EC with Quantum Computing (QC) that is promising in power systems. Specifically, this paper combines QC with Predator Prey Brain Storm Optimization (PPBSO) of high‐performance EC. The effectiveness of the proposed method is demonstrated in the New England 39‐node system.
Bibliographie:IEEJ Transactions on Power and Energy
Translated from Volume 145 Number 2, pages 114–122, DOI
10.1541/ieejpes.145.114
(Denki Gakkai Ronbunshi B).
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ISSN:0424-7760
1520-6416
DOI:10.1002/eej.23509