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A methodology of a hybrid hydrogen supply network(HHSN)under alternative energy resources(AERs)of hydrogen footprint constraint for sustainable energy production(SEP)

机译:可持续能源生产氢足迹约束的替代能源资源(AERS)下的混合氢气供应网络(HHSN)的方法(SEP)

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We aim to suggest a methodology of a smart hybrid hydrogen supply network based on diverse alternative energy resources of hydrogen footprint constraint.To date,hydrogen production has been mostly dependent on fossil fuels.However,future hydrogen would be harnessed contingent on eco-friendiy energy resources to support environmentally benign hydrogen economy.In this study,a smart hybrid hydrogen supply network is designed considering hydrogen production from solar energy,wind energy and wastewater and hydrogen distribution by using reinforcement learning.A mathematical model is divided into two phases.First phase is a stochastic programming under demand uncertainty,where multi objective functions are to minimize the total annual costs and environmental costs,respectively.Second phase is a heuristic optimization problem based on Q-learning which is one of the reinforcement learning algorithms.The suggested model is applied to Gyeongsang province in the Republic of Korea as a case study.Alternative energy resources are selected considering regional characteristics.We verify possibilities for construction of a smart future hydrogen supply network based on various feasible scenarios,where can propose the best hydrogen network to decision-makers.
机译:我们的目标是,基于氢足迹约束的不同替代能源的智能混合氢气供应网络的方法。迄今为止,氢气产量主要取决于化石燃料。然而,未来的氢气将利用生态友好能量来利用偶然的氢气支持环境良性氢气经济的资源。在本研究中,考虑到从太阳能,风能和废水和使用加强学习的氢气分布的氢气产生设计。数学模型分为两个阶段。首次阶段是在需求不确定性下的随机编程,其中多目标职能分别最大限度地减少年度成本和环境成本。第二阶段是基于Q学习的启发式优化问题,这是强化学习算法之一。建议的模型是以朝鲜民共和国应用于庆州市为例。选择替代能源资源,考虑区域特征。我们验证了基于各种可行情景的智能未来氢气供应网络的可能性,在哪里可以将最佳的氢网络提出到决策者。

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