Fossil based liquid fuels, primarily used in transportation systems, are likely to be replaced with renewable resources thanks to energy transition policies. However, shifting from stable energy production (using coal, natural gas) to highly volatile renewable production will bring a number of problems as well. On the other side, tremendous developments in solar and wind power technologies encourage energy investors to maximize their contributions over the electricity grid. This highly volatile energy resources bring a strong research question to the attention: How to benefit from excess energy? Power-to-gas seems to be a strong candidate to store excess energy. Besides, power-to-hydrogen is seen as a liquid fuel for fuel cell vehicles. This paper aims to analyze trading approaches of a power-to-hydrogen system to minimize the energy costs. To achieve that, Markov Decision Process (MDP) along with Q-learning is modelled as well as a number of trading approaches. This research aims to reveal the feasibility of hydrogen as a fuel option in future smart grid.