TY - GEN
T1 - Coordinated Management Strategy for Off-Grid Electric Hydrogen Integrated Supply Station
AU - Zhang, Zelong
AU - Wang, Lei
AU - Yang, Shijie
AU - Du, Yuhua
AU - Huangfu, Yigeng
AU - Li, Zhipeng
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This paper proposes a novel energy management strategy for an off-grid electric hydrogen integrated supply station (EHISS). The fuel cell electric vehicle (FCEV) could be supplied both by hydrogen and electric energy, and by utilizing such an energy substitute characteristic, a novel energy trading framework for EHISS is developed. Under this framework, a two-stage price energy coordinated management strategy (PECMS) is proposed. The PECMS combines the source side energy management and demand side energy management, and could influence the users' tendency by regulating the selling price of hydrogen, which further improves the performance of energy management. The PECMS contains a genetic algorithm-mixed integer linear programming (GA-MILP) solving frame in the scheduling stage and a finite state machine algorithm based on the day-ahead scheduling results in the real-time control stage. The optimization of the proposed strategy is then validated in both the scheduling stage using the Gurobi solver and the realtime control stage by simulation.
AB - This paper proposes a novel energy management strategy for an off-grid electric hydrogen integrated supply station (EHISS). The fuel cell electric vehicle (FCEV) could be supplied both by hydrogen and electric energy, and by utilizing such an energy substitute characteristic, a novel energy trading framework for EHISS is developed. Under this framework, a two-stage price energy coordinated management strategy (PECMS) is proposed. The PECMS combines the source side energy management and demand side energy management, and could influence the users' tendency by regulating the selling price of hydrogen, which further improves the performance of energy management. The PECMS contains a genetic algorithm-mixed integer linear programming (GA-MILP) solving frame in the scheduling stage and a finite state machine algorithm based on the day-ahead scheduling results in the real-time control stage. The optimization of the proposed strategy is then validated in both the scheduling stage using the Gurobi solver and the realtime control stage by simulation.
KW - DC microgrids
KW - Energy management strategy
KW - Fuel cell electric vehicle
UR - http://www.scopus.com/inward/record.url?scp=105008670436&partnerID=8YFLogxK
U2 - 10.1109/IAS55788.2024.11023649
DO - 10.1109/IAS55788.2024.11023649
M3 - 会议稿件
AN - SCOPUS:105008670436
T3 - Conference Record - IAS Annual Meeting (IEEE Industry Applications Society)
BT - 2024 IEEE Industry Applications Society Annual Meeting, IAS 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE Industry Applications Society Annual Meeting, IAS 2024
Y2 - 20 October 2024 through 24 October 2024
ER -