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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Electrical engineering in Japan Ročník 218; číslo 2
Hlavní autori: Kawauchi, Yusuke, Mori, Hiroyuki, Chiang, Hsiao‐Dong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hoboken Wiley Subscription Services, Inc 01.06.2025
Predmet:
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia: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).
of
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0424-7760
1520-6416
DOI:10.1002/eej.23509