Jun-Hyung Ryu’s research while affiliated with Dongguk University and other places

Hydrogen is drawing increasing attention as a carbon-neutral energy carrier. The effects of climate change are increasing the pressure to establish a hydrogen energy infrastructure. To facilitate the transition to hydrogen energy, a large number of hydrogen refueling stations (HRS)s will need to be constructed throughout the entire transportation network. With a limited financial budget, constructing them simultaneously is not possible. However, it is economical to develop a systematic decision-making framework for determining construction priorities for HRSs. In this study, we propose p-median based mixed integer linear programming (MILP) models to establish location and construction priorities. The models aim to maximize the contribution impact that is represented by the sum of average distances between HRS and its allocated hydrogen vehicles. The metropolitan city of South Korea, Seoul, is used as a case study to illustrate the applicability of the proposed methodology.

Hydrogen is drawing increasing attention as a carbon neutral energy carrier. The effects of climate change are increasing the pressure to establish hydrogen energy infrastructure. To facilitate the transition to hydrogen energy, a large number of hydrogen refueling station (HRS)s should be constructed throughout the entire transportation network. With a limited financial budget, constructing them simultaneously is not possible. However, it is economically to develop a systematic decision-making framework for determining construction priorities for HRSs. In this study, we proposed p-medianbased mixed integer linear programming (MILP) models to select location and construction priorities. The models aim to maximize the contribution impact that is represented by the sum of average distances between HRS and its allocated hydrogen vehicles. The metropolitan city of South Korea, Seoul is used as a case study to illustrate the applicability of the proposed methodology.

Given the optimistic future of hydrogen energy in transition toward a low-carbon society, hydrogen subsystem and its role in the optimal energy management of multi-energy microgrids (MEMG) should be considered. Uncertainties of hydrogen vehicles (HVs), demands, wind turbine (WT) and photovoltaic (PV) power output, and prices associated within MEMG can impose regret to the optimality of solution. In this study, a stochastic p-robust optimization approach is developed to cope with uncertainties and obtain an optimal solution for the proposed MEMG while satisfying a robustness criterion. The proposed MEMG considers different energy conversions including power to hydrogen (P2H), hydrogen to power (H2P), hydrogen to gas (H2G), gas to power (G2P) and gas to power and heat (G2He). Additional energy transactions with the external electricity grid as well as liquified natural gas (LNG) are considered and application of renewables and hydrogen storage system (HSS) towards a sustainable isolated MEMG is investigated via the ϵ-constraint approach. The effectiveness and applicability of the proposed approach is evaluated on a case study and results verify that the proposed MEMG is scheduled to be robust and obtain the maximum profit.

With increasing hydrogen usage, hydrogen subsystem should be considered in the multi-energy chain and a model is required that assumes current energy infrastructures, while preserving independent energy subsystems. In this study, a stochastic agent-based model is introduced for the coordinated scheduling of multi-vector microgrids considering interactions between electricity, hydrogen, and gas agents. Power to hydrogen (P2H) through electrolysis, hydrogen to power (H2P) through fuel cells, hydrogen to gas (H2G) through methanation, and gas to power (G2P) through distributed generation (DG) units are modeled to present the interactions among energy agents. The interactions in terms of shared variables and coupling constraints are described using augmented Lagrangian relaxation (ALR) and alternating direction method of multipliers (ADMM) to obtain three correlated optimization problems, preserving the privacy of energy sectors with minimum data exchange. An iterative process is accomplished among energy sectors to reach a consensus. Uncertainties in the wind turbine (WT) and photovoltaic (PV) power output, hydrogen vehicles (HVs), demands, and prices are captured using a stochastic method. To evaluate the proposed method, case studies are conducted using a multi-energy microgrid. The results verify that the microgrid is well scheduled and the interactions are accurately modeled, representing the effectiveness of the proposed method.

This study used process simulation to assess the overall economic feasibility of a hydrogen energy system involving a power-to-ammonia process as a transportation energy source. While the relevant individual processes for the overall energy system have already been used in the chemical industry, it is financially important to approach them in a holistic simulation framework. According to the proposed simulation, the ammonia and hydrogen production costs were 288.3 $/t and 5.43–6.13$/kg, respectively. The results could motivate further studies on accelerating the implementation of renewable energy systems to mitigate climate change.

Liquefied natural gas (LNG) plants require large amounts of energy to liquefy natural gas (NG). Therefore, many optimization studies have been conducted to minimize this energy consumption. Such studies have usually focused on optimization approaches to overcome the high nonlinearity of NG liquefaction process models. By contrast, decision variables and design bases have barely been investigated. In this study, a NG liquefaction process is modeled to perform sensitivity analysis of the design parameters and decision variables to determine their effects on the optimal operating conditions and process efficiency. A base case optimization is performed to investigate the convergence rate. Among 120 optimization runs, 57.5% are converged and 15.4% of the converged results show less than 0.1% difference in specific work compared to the best result. The effects of 11 decision variables and four design parameters are studied to obtain sensitivities. Among the decision variables, methane fraction and outlet temperature of a hot stream in an LNG heat exchanger strongly influence process efficiency. When changing the values of the design parameters within the ranges mentioned in the literature, specific work can vary from 724 kJ/kg LNG to 1509 kJ/kg LNG. Irreversibility in the coolers are the major reason to this variation.