Grid-price Dependent Optimal Energy Storage Management Strategy for Grid-connected Industrial Microgrids

Abinet Tesfaye Eseye*, Dehua Zheng, Jianhua Zhang, Han Li

*Corresponding author for this work

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


This paper presents an optimal energy management strategy for the operation of multiple energy storage units (batteries) in grid-connected industrial microgrids with high-penetration renewables in a variable grid-price scenario. The approach is based on a regrouping particle swarm optimization (RegPSO) formulated over a day-ahead scheduling horizon with one hour time interval, considering forecasted renewable energy generations and electric load demands. Besides satisfying its local energy demands, the microgrid considered in this paper (a real industrial microgrid, "Goldwind Smart Microgrid System" in Beijing, China), participates in energy trading with the main grid; it can either sell power to the main grid or buy from the main grid. Performance objectives include minimization of operation and maintenance costs and energy purchasing expenses from the main grid, and maximization of financial profit from energy selling revenues to the main grid. Simulation results demonstrate the effectiveness of various aspects of the proposed strategy in different scenarios. To validate the performance of the proposed strategy, obtained results are compared to a genetic algorithm (GA) based reference energy management approach and reveal that the RegPSO based strategy was able to find a global optimal solution in considerably less computation time than the GA based reference approach.

Original languageEnglish
Title of host publicationProceedings of the Ninth Annual IEEE Green Technologies Conference, GreenTech 2017
Number of pages8
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication
EventIEEE Green Technologies Conference - Denver, United States
Duration: 29 Mar 201731 Mar 2017
Conference number: 9

Publication series

NameIEEE Green Technologies Conference
ISSN (Print)2166-546X
ISSN (Electronic)2166-5478


ConferenceIEEE Green Technologies Conference
Abbreviated titleGreenTech
CountryUnited States


  • Energy management
  • Energy storage
  • Genetic algorithm
  • Microgrid
  • Regrouping particle swarm optimization
  • Renewable energy
  • Variable grid prices
  • Particle swarm optimization

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