Bridging chance-constrained and stochastic optimization for risk estimation of virtual energy hubs dominated by hybrid vehicles under diverse uncertainties: To improve economic sustainability

Abstract

Virtual energy hubs (VRHBs) combine dispersed energy resources to enhance the efficient use of various energy sources, grid flexibility, and renewable energy adoption. This paper investigates the adoption of VRHBs with power-to-gas (P2G) facilities and hybrid electric-compressed natural gas vehicles (HNGEVs) in the presence of renewable resources to effectively tackle carbon emissions and increase the flexibility of VRHBs. The proposed P2G facility is constructed from carbon capture to collect carbon pollution from fossil fuel resources and HNGEVs to recycle into synthesis gas in a methanation plant. Furthermore, due to severe uncertainties due to renewable resources and energy consumption intensity, the proposed framework utilizes stochastic-driven chance-constrained optimization, which enables decision-makers in VRHBs to account for the probabilistic nature of uncertainties. Chance-constrained optimization explicitly considers uncertainty through probabilistic constraints, providing a more robust and reliable solution compared to deterministic methods. By modeling uncertainties using chance-constrained programming, VRHBs can manage the economic risks under unexpected worst realizations in uncertainties. Based on the results, the P2G facility leads to a significant reduction in the daily emissions by 63.06 % and 42.77 % observed under no-risk and full-risk conditions, respectively. In addition, the suggested chance-constrained method resulted in a major decrease of 69 % in overall operational costs.