A stochastic agent-based cooperative scheduling model of a multi-vector microgrid including electricity, hydrogen, and gas sectors

Vahid Khaligh, Azam Ghezelbash, Mohammadreza Mazidi, Jay Liu, Jun Hyung Ryu, Jonggeol Na

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

With increasing hydrogen usage, hydrogen subsystem should be considered in the multi-energy chain and a model is required that assumes current energy infrastructures, while preserving independent energy subsystems. In this study, a stochastic agent-based model is introduced for the coordinated scheduling of multi-vector microgrids considering interactions between electricity, hydrogen, and gas agents. Power to hydrogen (P2H) through electrolysis, hydrogen to power (H2P) through fuel cells, hydrogen to gas (H2G) through methanation, and gas to power (G2P) through distributed generation (DG) units are modeled to present the interactions among energy agents. The interactions in terms of shared variables and coupling constraints are described using augmented Lagrangian relaxation (ALR) and alternating direction method of multipliers (ADMM) to obtain three correlated optimization problems, preserving the privacy of energy sectors with minimum data exchange. An iterative process is accomplished among energy sectors to reach a consensus. Uncertainties in the wind turbine (WT) and photovoltaic (PV) power output, hydrogen vehicles (HVs), demands, and prices are captured using a stochastic method. To evaluate the proposed method, case studies are conducted using a multi-energy microgrid. The results verify that the microgrid is well scheduled and the interactions are accurately modeled, representing the effectiveness of the proposed method.

Original languageEnglish
Article number231989
JournalJournal of Power Sources
Volume546
DOIs
StatePublished - 30 Oct 2022

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) , funded by the Ministry of Science and ICT ( 2019R1A2C2084709 , 2021R1A4A3025742 ).

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • Electricity–hydrogen–gas systems
  • Electrolysis
  • Energy storage
  • Methanation
  • Microgrid
  • Multiagent
  • Scheduling

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