Distributed optimization of a power, heat, and hydrogen-based virtual energy hub plant integrated with fuel cell and plug-in electric vehicles under a hybrid stochastic-robust approach

The deployment of distributed energy resources in modern energy systems, as well as the increasing demand for green hydrogen along with electricity and heating has provided a path to virtual energy hub plants (VEHP). In line with this recent emergence of VEHP, the main goal of this article is to present a robust distributed optimization model for nearby energy hubs (EHs) under the concept of a VEHP to coordinate them in order to participate in energy markets. To do so, multiple EHs comprising renewable energy resources (RES), power-to-heat (PtH) and power-to-hydrogen (PtH 2 ) technologies are considered. In addition, EHs are equipped with hydrogen refueling and charging stations for fuel cell and plug-in electric vehicles. To preserve EHs data privacy as well as improving the optimization scalability, accelerated alternative direction method of multipliers (Accelerated-ADMM) is proposed. A hybrid stochastic-robust uncertainty management approach is developed to tackle the uncertainty of RES, electricity market, and electric vehicles. The final problem is recast as a mixed integer convex quadratic programming (MICQP) which can be solved via a commercial solver to reach the optimal solution. The results obtained show the effectiveness of the proposed model. The distributed Accelerated-ADMM approach converges fast within eight iterations. Furthermore, the stochastic-robust approach demonstrated a good performance in handling multiple uncertain parameters simultaneously. • Accelerated-ADMM enables privacy-preserving distributed VEHP market coordination. • Hybrid stochastic-robust modeling handles RES/price and PEV charging uncertainty. • MICQP reformulation models PtH/PtH2, storage, CHP and V2G with optimal solving.