A stochastic agent-based cooperative scheduling model of a multi-vector microgrid including electricity, hydrogen, and gas sectors
- 주제(키워드) Multiagent , Scheduling , Electricity-hydrogen-gas systems , Energy storage , Electrolysis , Methanation , Microgrid
- 주제(기타) Chemistry, Physical
- 주제(기타) Electrochemistry
- 주제(기타) Energy & Fuels
- 주제(기타) Materials Science, Multidisciplinary
- 설명문(일반) [Khaligh, Vahid; Ghezelbash, Azam; Liu, Jay] Pukyong Natl Univ, Inst Cleaner Prod Technol, Busan 48547, South Korea; [Mazidi, Mohammadreza] Yazd Univ, Dept Elect Engn, Yazd, Iran; [Liu, Jay] Pukyong Natl Univ, Dept Chem Engn, Busan 48513, South Korea; [Ryu, Jun-Hyung] Dongguk Univ, Dept Energy Syst Engn, Gyeongju Campus, Gyeongju, South Korea; [Na, Jonggeol] Ewha Womans Univ, Dept Chem Engn & Mat Sci, Grad Program Syst Hlth Sci & Engn, Seoul 03760, South Korea
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- 발행기관 ELSEVIER
- 발행년도 2022
- URI http://www.dcollection.net/handler/ewha/000000194557
- 본문언어 영어
- Published As https://doi.org/10.1016/j.jpowsour.2022.231989
초록/요약
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.
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