Implementation of a Decision-making Approach for a Hydrogen-based Multi-energy System Considering EVs and FCEVs Availability

Innovative green vehicle concepts have become increasingly prevailing in consumer purchasing habits as technology evolves. The global transition towards sustainable transportation indicates an increase in new-generation vehicles, including both fuel-cell electric vehicles (FCEVs) and plug-in electric vehicles (PEVs) that will take on roads in the future. This change requires new-generation stations to support electrification. This study introduced a prominent multi-energy system concept with a hydrogen refueling station. The proposed multi-energy system (MES) consists of green hydrogen production, a hydrogen refueling station for FCEVs, hydrogen injection into natural gas (NG), and a charging station for PEVs. An on-site renewable system projected at the station and a polymer electrolyte membrane electrolyzer (PEM) to produce hydrogen for two significant consumers support MES. In addition, the MES offers the ability to conduct two-way trade with the grid if renewable energy systems are insufficient. This study develops a comprehensive multi-energy system with an economically optimized energy management model using a mixed-integer linear programming (MILP) approach. The determinative datasets of vehicles are generated in a Python environment using Gauss distribution. The fleet of FCEVs and PEVs are currently available on the market. The study includes fleets of the most common models from well-known brands. The results indicate that profits increase when the storage capacity of the hydrogen tank is higher and natural gas injections are limitless. Optimization results for all cases tend to choose higher-priced natural gas injections over hydrogen refueling because of the difference in costs of refueling and injection expenses. The analyses reveal the highest hydrogen sales to the natural gas (NG) grid by consuming 2214.31 kg, generating a revenue of $6966, and in contrast, the lowest hydrogen sales to the natural gas grid at 1045.38 kg, resulting in a revenue of $3286. Regarding electricity, the highest sales represent revenue of $7701 and $2375 for distribution system consumption and electric vehicles (EV), respectively. Conversely, Cases 1 and 2 have achieved sales to EV of $2286 and $2349, respectively, but do not have any sales to distribution system consumption regarding the constraints. Overall, the optimization results show that the solution is optimal for a multi-energy system operator to achieve higher profits and that all end-user parties are satisfied.