Multi-Objective Operation Strategy for a Community with RESs, Fuel Cell EVs and Hydrogen Energy System Considering Demand Response

Nowadays, it can be said that loads in the residential and transportation sector constitute an important part of global energy consumption. In this respect, due to environmental problems and in terms of ensuring energy reliability, it is seen that the energy demand is moving towards supplying it from on-site, renewable energy sources (RESs) and in a small-scale region, namely community. In this context, in this study, a multi-objective stochastic optimum energy management model for a renewable-supported hydrogen-based community is proposed with mixed-integer linear programming method. In the structure, the hydrogen energy system (HES) consisting of an electrolyzer, hydrogen tank, and fuel cell provides energy to the community and fuel support for hydrogen fuel cell electric vehicles (HFCEVs). In addition, a demand response (DR) program is implemented, where the room temperatures of the houses are remotely controlled. While electricity can be purchased from the power grid, hydrogen can be traded with the main hydrogen gas network. Besides, renewable spillage and carbon emission costs are considered. Various case studies are conducted using real data from Spain to demonstrate the effectiveness of the presented model. According to the results obtained, the proposed method can reduce the cost of the community by 58.67%.

Automated summary

Nowadays, renewable energy sources (RESs) are becoming increasingly important in meeting the energy demand of residential and transportation sectors due to environmental concerns and the need for energy reliability. This study proposes a multi-objective stochastic optimum energy management model for a renewable-supported hydrogen-based community. The model utilizes a hydrogen energy system (HES) consisting of an electrolyzer, hydrogen tank, and fuel cell to provide energy to the community and fuel support for hydrogen fuel cell electric vehicles (HFCEVs). The effectiveness of the model is demonstrated through various case studies using real data from Spain, showing a 58.67% reduction in community cost.