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With the burgeoning of plug-in electric vehicles (PEVs), as well as the emergence of fuel cell vehicles (FCVs) and plug-in hybrid electricity/hydrogen vehicles (PH2EVs), the synergistic effect of electricity, hydrogen, and transportation networks should be investigated. To enhance the security of the coupled network and ensure the reliability of the charging/refueling services of EVs, a vulnerability assessment is required to assist in system contingency planning as well as post-contingency measures. In this paper, a vulnerability assessment strategy is formulated for coupled transportation and multi-energy networks. First, a novel graph representation for the coupled transportation and multi-energy networks is proposed. Then, a critical asset identification tool is applied to find the vulnerability point of the coupled networks, and the transfer margin ratio (TMR) is put forward to assess the dynamic vulnerability level under cascading contingencies. Finally, a vulnerability envelope (the lower bound and upper bound of vulnerability) is found based on a bi-level optimization problem. To demonstrate the effectiveness of the proposed methodology, case studies are performed on the IEEE 39-bus electricity network coupled with a 25-node transportation network and a 50-node hydrogen/gas network. It is verified that the vulnerable point of the coupled network can be found. Besides, it is concluded that the penetration of FCVs and PH2EVs can enhance energy flexibility through the energy substitution effect and thus mitigate the system vulnerability.

Hydrogen production has become an emerging technology and offered a pathway to sustainable energy utilization in transportation systems. In this paper, we propose a coordinated planning of the electricity network and hydrogen refueling and production station planning strategy for hydrogen fuel cell electric vehicles (FCEVs). In the proposed system structure, a hydrogen supply chain for FCEVs mainly composes hydrogen refueling stations (HRSs), hydrogen production stations (HPSs), hydrogen storage, and hydrogen delivery network. To enhance the system's flexibility, hydrogen delivery combines both gas pipeline delivery and truck logistics delivery. In the mathematical models, the synergistic effect and coupling relationship of the electricity network, gas network, and transportation network are presented. To ensure the efficiency and effectiveness of the hydrogen refueling system, a quality of service (QoS) and reliability assessment method is presented. The proposed methodology is verified in case studies. It is found out that compared with the single delivery methods, the proposed multi-network coordinated planning has the lowest total cost with less unserved energy. Besides, through QoS and reliability assessment, enough hydrogen reserves can be maintained to cope with contingencies.

Electric vehicles (EVs) have experienced rapid growth in recent years due to the concern about climate change. Meanwhile, hydrogen has become a versatile energy carrier that can be used in the EV industry. Thus, apart from pure EVs, hydrogen fuel cell EVs (FCEVs) and plug-in hybrid hydrogen and electric vehicles (PH2EVs) are also promising solutions for replacing gasoline vehicles. With the penetration of these alternative fuel vehicles, it is necessary to formulate proper strategies to guide EV charging and refueling. Hence, in this paper, a dynamic pricing strategy for integrated electricity charging and hydrogen refueling stations (IEHSs) is proposed to guide the charging/refueling decisions of a variety types of EVs and ensure the operation of IEHSs, power distribution network (PDN), and gas network (GN). First, a tri-level dynamic pricing strategy is proposed considering the interactions among EVs, IEHSs, and both PDN and GN. Second, the bounded rationality of EVs in station selection is modeled based on cognitive theory. Third, energy substitution for PH2EVs between electricity charging and hydrogen refueling is analyzed. Simulation results show that the proposed pricing strategy can increase the utility of EVs, enhance the profit of IEHSs, and mitigate the congestion of both PDN and GN.

Electric vehicles (EVs) are believed to play an important role in mitigating carbon emissions. To accommodate the increasing number of EVs, the co-planning of electrical distribution networks and the charging infrastructure, such as fast-charging stations (FCSs), becomes an emergent task. Both the charging demand of EVs and the stability of the electricity network should be satisfied. In this paper, we proposed a data-driven agent-based planning strategy for FCSs. Different from the conventional planning strategy, we utilized machine learning tools to consider EV behaviors at the microscopic level in a planning problem. First, a Partially Observable Markov Decision Process of EVs is established, and multi-agent deep reinforcement learning is utilized to learn the charging and driving decisions of EVs under different transportation network typologies and FCSs planning schemes. Second, a data-driven agent-based traffic assignment model (DA-TAM) is proposed to aggregate the atomic behaviors of EVs, which can present the sensitivity of the traffic flow and EV charging demand to the FCS planning schemes. Third, the DA-TAM is adapted to the proposed planning model to ensure the quality of service and prevent unbalanced traffic flow. Through the proposed method, microscopic behaviors of EVs can be reflected, and the impact of the planning scheme on the traffic condition can be revealed. The proposed methodologies are verified in the case studies. The presented agent-based planning strategy can serve more EV charging demands, cause less waiting time in FCSs to enhance the quality of service, and encounter less severe traffic unbalance problems.

Maritime transportation takes a major responsibility in public travel between islands while producing a large quantity of greenhouse gas (GHG) emissions. All-electric ships (AESs) can be applied to mitigate GHG emissions through energy storage systems (ESS), renewable integration, and cold ironing. In this paper, we propose a flexible voyage scheduling strategy for AESs based on the temporal-spatial dynamics (TSD) to satisfy the transportation demand while mitigating the burden of the AESs on the power grids during charging. The interaction between the AESs and island-based microgrids is modeled. Furthermore, the AESs are utilized to enhance the resilience of the power grids. The AESs can be dispatched to realize the load restoration under contingencies. The proposed methodology is verified on a three-island system. It can be concluded that under normal operation, the proposed voyage scheduling can reduce the total energy consumption cost of the AESs and the islands. Besides, the voltage violation can be improved. Under the emergency, the proposed method can help the grids restore more critical and normal loads.