Abstract

In this paper, an Optimal Power Flow in Micro-Grid Operation is proposed. It is based on a learning algorithm combining prediction and optimization methods (Multi-objective Genetic Algorithm-Evolving Takagi-Sugeno) for implementing two objective functions i.e. minimizing losses and extending battery lifetime in online condition. This Micro-Grid operates in DC including the interest of redundancy i.e. parallel circuits for supplying loads from photovoltaic panels and batteries. The batteries use two way operations as energy generation and energy storage. It has been tested using PV power generation data and load data in a region. It is also demonstrated the comprehensive comparisons with some other learning algorithms. The results illustrate a higher online performance with optimal solution in many cases with the efficiency are higher than 97%. Moreover, reducing a high amount of CPU-time and large disk space for saving data can be achieved by the proposed approach.

Original languageEnglish
Title of host publicationProceedings - 2021 International Seminar on Intelligent Technology and Its Application
Subtitle of host publicationIntelligent Systems for the New Normal Era, ISITIA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages52-57
Number of pages6
ISBN (Electronic)9781665428477
DOIs
Publication statusPublished - 21 Jul 2021
Event2021 International Seminar on Intelligent Technology and Its Application, ISITIA 2021 - Virtual, Online
Duration: 21 Jul 202122 Jul 2021

Publication series

NameProceedings - 2021 International Seminar on Intelligent Technology and Its Application: Intelligent Systems for the New Normal Era, ISITIA 2021

Conference

Conference2021 International Seminar on Intelligent Technology and Its Application, ISITIA 2021
CityVirtual, Online
Period21/07/2122/07/21

Keywords

  • evolving takagi sugeno
  • genetic algorithm
  • micro-grid operation
  • multiobjective
  • online learning
  • optimal power flow

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