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

• Published in: Journal of power sources 2022-10, Vol.546, p.231989, Article 231989
• Main Authors: Khaligh, Vahid, Ghezelbash, Azam, Mazidi, Mohammadreza, Liu, Jay, Ryu, Jun-Hyung, Na, Jonggeol
• Format: Article
• Language: English
• Subjects: Electricity–hydrogen–gas systems; Electrolysis; Energy storage; Methanation; Microgrid; Multiagent; Scheduling
• ISSN: 0378-7753; 1873-2755
• DOI: 10.1016/j.jpowsour.2022.231989

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. •An electricity-hydrogen-gas microgrid is developed.•Different facilities including P2H, H2P, H2G, G2P and HSS are considered.•A multi-agent model is proposed to preserve the privacy of energy parties.•Independent optimization problems are solved cooperatively to reach a consensus.•Interactions among electricity, hydrogen, and gas subsystems are modeled.