Modeling a hydrogen-based sustainable multi-carrier energy system using a multi-objective optimization considering embedded joint chance constraints

Hydrogen-based power generation could increase the sustainability and efficiency of future energy systems due to the higher storing capabilities resulting from the higher gravimetric density of hydrogen energy. This work proposes a hydrogen-based multi-carrier energy system (HMES) comprising renewab...

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Vydáno v:Energy (Oxford) Ročník 278; s. 127643
Hlavní autoři: Zhang, Hui, Wang, Jiye, Zhao, Xiongwen, Yang, Jingqi, Bu sinnah, Zainab Ali
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.09.2023
Témata:
carbon
Chance constraint programming
decision making
electricity
energy efficiency
Energy storage
hydrogen
Hydrogen-based multi-carrier energy system
power generation
Renewable energy sources
risk
risk management
solar energy
Stochastic optimization
water storage
Weighted sum method
wind
Renewable energy sources
Chance constraint programming
Stochastic optimization
Weighted sum method
Hydrogen-based multi-carrier energy system
Energy storage
ISSN:0360-5442
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Shrnutí:Hydrogen-based power generation could increase the sustainability and efficiency of future energy systems due to the higher storing capabilities resulting from the higher gravimetric density of hydrogen energy. This work proposes a hydrogen-based multi-carrier energy system (HMES) comprising renewable energy resource (RES), electricity, and hydrogen markets as input energy carriers and power, cooling, and heating as demands. Thermal water storage and hydrogen tank storage are considered to improve energy efficiency. This system uses the full capacity of intermittent wind and solar energy to increase RES utilization and decrease carbon emissions simultaneously. The environmental and economic goals of the proposed HMES are satisfied through the weighted-sum multi-objective method. The trade-off solution between operation and emission costs is ultimately obtained through the max-min fuzzy method. Chance constraint programming (CCP), which offers decision-makers a variety of risk-taker strategies, is utilized as a tool to manage the risk associated with stochastic optimization. The confidence level of the CCP determines the risk tolerated by the decision-maker, improving the optimization process by creating a more realistic approach to risk management in real-world models. The results show the benefits of hydrogen tank by reducing operation costs by 4.5% and importance of CCP leading to operation costs reduction. •Multi-objective eco-environmental analysis of hydrogen-based energy system.•Using chance-constrained programming to model the probabilistic constraints.•Analyzing impacts of hydrogen storage on economic and environmental aspects.•Modeling the stochastic variables using the Mont Carlo-based scenario approach.
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ISSN:0360-5442
DOI:10.1016/j.energy.2023.127643