In recent years, there has been a rapid change in the portfolio of road vehicles. This transformation needs to be supported by reshaping fuel stations. The existing structures can be modified to supply multiple vehicle types while utilizing renewable energies. In this study, we introduce a hybrid fuel station (HFS) to supply both electric and natural gas vehicles. We incorporate an energy hub and thoroughly explain the structure and components in detail. The configuration of the proposed HFS includes renewable energy sources (RESs), a power to gas unit (P2G), a natural gas distributed generator (NGDG), an energy storage device, a compressor, and a gas storage device. The optimal operation of the HFS will be under uncertainty due to multiple parameters that display unpredictable behaviors. Thus, a stochastic scheduling scheme considering features of the proposed HFS is presented. The resulted optimization is modeled as a mixed-integer linear programming. The validity and capabilities of structure and scheduling schemes are evaluated with several numerical simulations. In particular, the impact of RESs, P2G, and NGDG on the operating cost of the HFS is analyzed. The results demonstrate that the economic benefits of the proposed HFS are significantly higher than a conventional one.

As the number of plug-in electric vehicles (PEVs) increases, so might their issues and impacts on the power system performance. Toward eliminating the negative impacts of PEVs on the power system, installation of a charging controller at customers' homes, which addresses such issues and brings convenience for customers, is fundamentally required in the smart grid era. This paper develops a novel in-home PEV charge/discharge scheduling method that employs vehicle-to-home (V2H) capability to schedule level of charging/discharging at each time slot. In doing so, a household controller minimizes customer payment cost and reliability cost. The proposed charging algorithm not only responds to time-varying tariffs but can also manage the home outage by supplying household appliances during the home load interruption. The proposed method is verified and tested with different case studies. Results show that applying the proposed method leads to significant decrement in home peak load and customer costs, i.e., payment and interruption costs.

Electric distribution networks have to deal with issues caused by natural disasters. These problems possess unique characteristics, and their severity can make load restoration methods impotent. One solution that can help in alleviating the aftermath is the use of microgrids (MGs). Employing the cumulative capacity of the generation resources through MG coupling facilitates the self-healing capability and leads to better-coordinated energy management during the restoration period, while the switching capability of the system should also be considered. In this paper, to form and schedule dynamic MGs in distribution systems, a novel model based on mixed-integer linear programming (MILP) is proposed. This approach employs graph-related theories to formulate the optimal formation of the networked MGs and management of their proper participation in the load recovery process. In addition, the Benders decomposition technique is applied to alleviate computability issues of the optimization problem. The validity and applicability of the proposed model are evaluated by several simulation studies.