TY - JOUR
T1 - An optimal energy management strategy with subsection bi-objective optimization dynamic programming for photovoltaic/battery/hydrogen hybrid energy system
AU - Huangfu, Yigeng
AU - Tian, Chongyang
AU - Zhuo, Shengrong
AU - Xu, Liangcai
AU - Li, Peng
AU - Quan, Sheng
AU - Zhang, Yonghui
AU - Ma, Rui
N1 - Publisher Copyright:
© 2022 Hydrogen Energy Publications LLC
PY - 2023/1/26
Y1 - 2023/1/26
N2 - Nowadays, the scheme of a stand-alone microgrid utilizing renewable energy is regarded as an effective approach to guarantee the power supply of an off-grid system. However, the intermittent nature of renewables brings new challenges to the determination of the optimal operation point for a hybrid energy system (HES). To address this issue, this paper proposes a subsection bi-objective optimization dynamic programming strategy for the HES consisting of photovoltaic, fuel cell, electrolyzer, hydrogen storage system, and battery bank. Within the proposed strategy, reasonable rule-based judgment is introduced to reduce the complexity of system control. Moreover, dynamic programming is selected to obtain the global optimal power distribution scheme. Meanwhile, a multi-objective genetic algorithm strategy is designed for comparative analysis. The results in two typical cases indicate the proposed strategy can improve photovoltaic utilization by 0.95% and 0.0003%, and fuel economy by nearly 50%.
AB - Nowadays, the scheme of a stand-alone microgrid utilizing renewable energy is regarded as an effective approach to guarantee the power supply of an off-grid system. However, the intermittent nature of renewables brings new challenges to the determination of the optimal operation point for a hybrid energy system (HES). To address this issue, this paper proposes a subsection bi-objective optimization dynamic programming strategy for the HES consisting of photovoltaic, fuel cell, electrolyzer, hydrogen storage system, and battery bank. Within the proposed strategy, reasonable rule-based judgment is introduced to reduce the complexity of system control. Moreover, dynamic programming is selected to obtain the global optimal power distribution scheme. Meanwhile, a multi-objective genetic algorithm strategy is designed for comparative analysis. The results in two typical cases indicate the proposed strategy can improve photovoltaic utilization by 0.95% and 0.0003%, and fuel economy by nearly 50%.
KW - Dynamic programming
KW - Energy management
KW - Fuel economy
KW - Stand-alone microgrid
KW - Subsection bi-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=85141449340&partnerID=8YFLogxK
U2 - 10.1016/j.ijhydene.2022.10.133
DO - 10.1016/j.ijhydene.2022.10.133
M3 - 文章
AN - SCOPUS:85141449340
SN - 0360-3199
VL - 48
SP - 3154
EP - 3170
JO - International Journal of Hydrogen Energy
JF - International Journal of Hydrogen Energy
IS - 8
ER -
