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 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 decisionmakers 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.

Automated summary

This study proposes a hydrogen-based multi-carrier energy system that improves energy efficiency through thermal water storage and hydrogen tank storage. It utilizes renewable energy sources like wind and solar power to simultaneously increase energy utilization and reduce carbon emissions. The environmental and economic goals are satisfied through a weighted-sum multi-objective method, and the trade-off solution between operation and emission costs is obtained through a max-min fuzzy method. Chance constraint programming is used to manage the risk associated with stochastic optimization, leading to reduced operation costs.