Optimization of virtual energy hub with hybrid vehicles in power-transportation networks towards a low-carbon sustainable transition: A probabilistic regret adjustment

Abstract

Transportation is one of the main fields that leads to environmental issues from emission perspective. In the transition to a sustainable future, hybrid vehicles are an essential part of transportation for developing nations, helping to bridge the gap until fully electric vehicles are widely adopted. While electric vehicles have gained the significant attention of researchers, plug-in hybrid CNG electric vehicles (PH-CNG-EVs), playing a major role in the transportation system of developing countries, have not been thoroughly studied in terms of modeling and optimization. This paper focuses on modeling and optimizing PH-CNG-EVs and their charging stations within a virtual energy hub (VEH), where the power-to-gas (P-to-G) technology is proposed to address CO2 emissions arising from hybrid vehicles and other sources of energy by capturing and recycling of polluted carbon. However, the optimal operation of the proposed VEH is influenced by uncertainties including electricity and gas demands, energy market prices, and wind renewable generation. To manage the associated uncertainties, p-robust optimization is applied by scenario-based regret measure hoping to achieve risk-averse strategy. Results show that p-robust has imposed a 9.29 % reduction in revenue for reliability improvement, in addition to a 10 % emission reduction in the presence of the P-to-G facility.