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Coordinated Planning and Configuration of Wind Power and Energy Storage in Park Microgrids

  • Siqi Bo
  • , Minghang Lv
  • , Mei Yan
  • , Jiaojiao Zhang
  • , Zile Xu
  • , Chongshen Guo*
  • *Corresponding author for this work
  • Dalian University of Technology
  • School of Chemistry and Chemical Engineering, Harbin Institute of Technology
  • University of Shanghai for Science and Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper addresses the optimal allocation of energy storage in park microgrids operating under a combined power supply mode of wind power generation and the main grid. The goal is to enhance wind power utilization and reduce power abandonment caused by the mismatch between load demand and power generation timings, thereby improving economic efficiency and system stability. Various energy storage configuration schemes are developed, and systematic energy storage configuration and operation strategies are provided using optimization models, genetic algorithms, multi-objective optimization, decision trees, and intelligent algorithms. This research offers theoretical foundations and practical guidance for the planning and construction of future park microgrids, highlighting its significant practical relevance.

Original languageEnglish
Title of host publication2024 3rd International Conference on Energy and Electrical Power Systems, ICEEPS 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages94-99
Number of pages6
ISBN (Electronic)9798350375138
DOIs
StatePublished - 2024
Externally publishedYes
Event3rd International Conference on Energy and Electrical Power Systems, ICEEPS 2024 - Guangzhou, China
Duration: 14 Jul 202416 Jul 2024

Publication series

Name2024 3rd International Conference on Energy and Electrical Power Systems, ICEEPS 2024

Conference

Conference3rd International Conference on Energy and Electrical Power Systems, ICEEPS 2024
Country/TerritoryChina
CityGuangzhou
Period14/07/2416/07/24

Keywords

  • Energy storage optimization
  • genetic algorithm
  • multi-objective optimization
  • optimization model

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