Parallel dependable multi-population differential evolutionary particle swarm optimization for on-line optimal operational planning of energy plants

Norihiro Nishimura, Yoshikazu Fukuyama, Tetsuro Matsui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

This paper presents dependable parallel multi-population differential evolutionary particle swarm optimization (DEEPSO) for on-line optimal operational planning of energy plants. The problem can be formulated as a mixed integer nonlinear optimization problem (MINLP). Since Optimal operational planning of numbers of energy plants are calculated simultaneously in a data center, the problem is required to generate optimal operational planning as rapidly as possible considering control intervals and numbers of treated plants. One of the solutions for this challenge is speeding up by parallel and distributed processing (PDP). However, PDP utilizes numbers of processes, and countermeasures for various faults of the processes should be considered. The problem requires successive calculation at every control interval for keeping customer services. Therefore, sustainable (dependable) calculation keeping appropriate solution quality are required even if some of the calculation results cannot be returned from distributed processes. Multi-population based evolutionary computation methods have been verified that they can improve solution quality. Using the proposed dependable parallel multi-population DEEPSO based method, it is observed that calculation time becomes about 4.3 times faster than the conventional sequential DEEPSO, and appropriate solution quality can be kept even if some of the calculation results cannot be returned from distributed processes.

Original languageEnglish
Title of host publication2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-7
Number of pages7
ISBN (Electronic)9781538627259
DOIs
Publication statusPublished - 2 Feb 2018
Event2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
Duration: 27 Nov 20171 Dec 2017

Publication series

Name2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
Volume2018-January

Conference

Conference2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
CountryUnited States
CityHonolulu
Period27/11/171/12/17

Keywords

  • Dependability
  • Differential Evolutionary Particle Swarm Optimization
  • Energy Management System
  • Multi-population
  • Optimal Operational Planning of Energy Plant
  • Parallel and Distributed Processing

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    Nishimura, N., Fukuyama, Y., & Matsui, T. (2018). Parallel dependable multi-population differential evolutionary particle swarm optimization for on-line optimal operational planning of energy plants. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings (pp. 1-7). (2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2017.8280879