Operating cost reduction of DC microgrids under real-time pricing using adaptive differential evolution algorithm

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)

Abstract

Virtual resistance-based droop control is widely adopted as secondary-layer control for grid-connected converters in DC microgrids. This paper presents an alternative usage of the virtual resistances to minimize the total operating cost of DC microgrids under real-time pricing. The total operating cost covers the running cost of utility grids, renewable energy sources (RES), energy storage systems (ESS), fuel cells, and power loss on the distribution lines. An adaptive Differential Evolution (ADE) algorithm is adopted in this paper to optimize the virtual resistances of the droop control for the grid-connected converters of dispatchable units, such that the power flow can be regulated. The performances of the proposed strategy are evaluated by the case studies of a 12-bus 380 V DC microgrid using Matlab and a 32-bus 380 V DC microgrid using a Real-Time Digital Simulator (RTDS). Both results validate that the ADE can significantly reduce the operating cost of DC microgrids and outperform the conventional Genetic Algorithm (GA) in terms of cost saving. Comparisons among the microgrids with different numbers of dispatchable units reveal that the cost saving is more prominent when the expansion of dispatchable units.

Original languageEnglish
Pages (from-to)169247-169258
Number of pages12
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

Keywords

  • Adaptive differential evolution (ADE)
  • DC microgrids
  • Operating cost
  • Virtual resistance

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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