Dependable multi‐population improved brain storm optimization with differential evolution for optimal operational planning of energy plants

This paper proposes dependable multi‐population improved brain storm optimization with differential evolution for optimal operational planning of energy plants. The problem can be formulated as a mixed‐integer nonlinear programming problem and various evolutionary computation techniques such as part...

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Veröffentlicht in:Electrical engineering in Japan Jg. 207; H. 4; S. 25 - 35
Hauptverfasser: Arai, Kiyo, Fukuyama, Yoshikazu, Iizaka, Tatsuya, Matsui, Tetsuro
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken Wiley Subscription Services, Inc 01.06.2019
Schlagworte:
brain storm optimization with differential evolution
Computer networks
Customer services
Data centers
dependability
Distributed processing
Energy costs
Evolutionary computation
mixed‐integer nonlinear programming problem
multi‐population
Nonlinear programming
optimal operational planning of energy plant
Particle swarm optimization
Population
Strategic planning
ISSN:0424-7760, 1520-6416
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Zusammenfassung:This paper proposes dependable multi‐population improved brain storm optimization with differential evolution for optimal operational planning of energy plants. The problem can be formulated as a mixed‐integer nonlinear programming problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolutionary PSO (DEEPSO), multi‐population DEEPSO (MP‐DEEPSO), and brain storm optimization have been applied so far. When optimal operational planning of numbers of energy plants is calculated simultaneously in a data center, a challenge is to generate optimal operational planning as rapidly as possible considering control intervals and numbers of treated plants. One of the solutions for the challenge is speeding up by parallel and distributed computing. It utilizes numbers of processes and countermeasures for various faults of the distributed processes should be considered. Moreover, successive calculation at every control interval is required for keeping customer services. Therefore, sustainable (dependable) calculation keeping appropriate solution quality is required even if some of the calculation results cannot be returned from distributed processes. It is verified that total energy cost by the proposed dependable multi‐population improved brain storm optimization with differential evolution strategy based method is lower than those by the compared methods, and higher quality of solutions can be kept even with high fault probabilities.
Bibliographie:Translated from Volume 139 Number 5, pages 330–337, DOI
IEEJ Transactions on Power and Energy
Denki Gakkai Ronbunshi B
10.1541/ieejpes.139.330
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ISSN:0424-7760
1520-6416
DOI:10.1002/eej.23231