Economic Dispatch of DC Microgrids under Real-Time Pricing Using Adaptive Differential Evolution Algorithm

Xiaoyan Qian, Yun Yang, Chendan Li, Siew Chong Tan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

This paper presents an adaptive Differential Evolution (ADE) algorithm to minimize the operating cost of DC microgrids via the optimization of the virtual resistances of the droop control for grid-connected converters. The total operating cost of DC microgrids include the running cost of utility grids, renewable energy sources (RES), energy storage systems (ESS), fuel cells, and the power losses on the distribution lines. The performances of the proposed strategy are evaluated in simulation on the case studies of a 12-bus 380 V DC microgrid using Matlab. The results validate that the ADE can significantly reduce the total operating cost of the DC microgrid and that it outperforms the Genetic Algorithm (GA) in terms of cost saving.

Original languageEnglish
Title of host publication2020 IEEE 9th International Power Electronics and Motion Control Conference, IPEMC 2020 ECCE Asia
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1114-1120
Number of pages7
ISBN (Electronic)9781728153018
DOIs
Publication statusPublished - 29 Nov 2020
Event9th IEEE International Power Electronics and Motion Control Conference, IPEMC 2020 ECCE Asia - Nanjing, China
Duration: 29 Nov 20202 Dec 2020

Publication series

Name2020 IEEE 9th International Power Electronics and Motion Control Conference, IPEMC 2020 ECCE Asia

Conference

Conference9th IEEE International Power Electronics and Motion Control Conference, IPEMC 2020 ECCE Asia
Country/TerritoryChina
CityNanjing
Period29/11/202/12/20

Keywords

  • Adaptive Differential Evolution (ADE)
  • DC microgrid
  • operating cost
  • virtual resistance

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Control and Optimization

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