TY - JOUR
T1 - Multi-objective optimal day-ahead scheduling of desalination-hydrogen system powered by hybrid renewable energy sources
AU - Liu, Boyu
AU - Rahimpour, Hossein
AU - Musleh, Ahmed S.
AU - Zhang, Daming
AU - Thattai, Kuthsav
AU - Dong, Zhao Yang
PY - 2023/8/15
Y1 - 2023/8/15
N2 - An energy system with desalination and hydrogen production and storage is a promising option for remote areas with shorelines, e.g., Middle East, to jointly manage electricity, desalinated water and hydrogen resources. Thus, a hybrid renewable energy system considering seawater reverse osmosis desalination, proton exchange membrane electrolyzer stacks, and also proton exchange membrane fuel cell, is proposed. This work focuses on the optimal operation problem of the system. It is established in a multi-objective optimization manner, with consideration of minimizing system total cost, power transmission and renewable energy curtailment. The problem is solved with Non-dominated Sorting Genetic Algorithm-III, a meta-heuristic method dedicated to multi-objective optimization. Results show through the solving of the optimization problem the optimal energy management strategy can be obtained, and in the studied scenario, the system can avoid 38–42% of carbon dioxide emission compared to conventional electricity generation and gray hydrogen production measures. The operational benefit of fuel cell is also verified. Compared to existing works, this work maintains the flexibility and cleanness of green hydrogen production components, consider more aspect in operation and can be solved with limited computational resources and obtain a satisfying result. © 2023 Elsevier Ltd
AB - An energy system with desalination and hydrogen production and storage is a promising option for remote areas with shorelines, e.g., Middle East, to jointly manage electricity, desalinated water and hydrogen resources. Thus, a hybrid renewable energy system considering seawater reverse osmosis desalination, proton exchange membrane electrolyzer stacks, and also proton exchange membrane fuel cell, is proposed. This work focuses on the optimal operation problem of the system. It is established in a multi-objective optimization manner, with consideration of minimizing system total cost, power transmission and renewable energy curtailment. The problem is solved with Non-dominated Sorting Genetic Algorithm-III, a meta-heuristic method dedicated to multi-objective optimization. Results show through the solving of the optimization problem the optimal energy management strategy can be obtained, and in the studied scenario, the system can avoid 38–42% of carbon dioxide emission compared to conventional electricity generation and gray hydrogen production measures. The operational benefit of fuel cell is also verified. Compared to existing works, this work maintains the flexibility and cleanness of green hydrogen production components, consider more aspect in operation and can be solved with limited computational resources and obtain a satisfying result. © 2023 Elsevier Ltd
KW - Hydrogen production
KW - NSGA-III
KW - Optimal operation
KW - Renewable energy
KW - Seawater desalination
UR - http://www.scopus.com/inward/record.url?scp=85161649236&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85161649236&origin=recordpage
U2 - 10.1016/j.jclepro.2023.137737
DO - 10.1016/j.jclepro.2023.137737
M3 - RGC 21 - Publication in refereed journal
SN - 0959-6526
VL - 414
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 137737
ER -
