Nowadays, the infrastructure and technologies in conventional distribution networks (CDNs) limit its further development to smart distribution networks. One way to overcome this is to develop CDNs using interconnected virtual microgrids (VMs); however, there is no consensus for an explicit definition of VMs. Therefore, in this paper, a comprehensive discussion on VMs is conducted, and a general framework for developing VMs is proposed. The core issue for developing VMs is to identify boundaries of VMs, and most of the suggested methods are based on analyzing operating states. In this paper, a different partitioning method, which is based on the analysis of structural characteristics, is proposed. The electrical coupling strength is defined as a composite weight coefficient for the analysis of electrical connection, and the electrical modularity is used to assess the quality of the partition so as to identify boundaries of VMs. This partitioning method is applied to the IEEE 33-bus distribution network and the PG&E 69-bus distribution network, and it can quickly evaluate the partitioning quality and effectively identify the boundaries of VMs.

Carbon reduction policies and the increasing trend toward transportation electrification have spurred the rapid development of electric vehicles (EVs). However, the uncertainties of charging demands and facility failures, resulting from factors such as power disruptions, equipment damage, and inefficient operation, pose challenges in balancing the economic efficiency and service reliability of electric vehicle charging stations (EVCSs). This paper proposes a robust planning model in the coupled traffic-power network, aiming to minimize the investment costs of EVCSs and the operation costs, including detour costs for EV users, penalty costs for unmet demands, and generation costs while maintaining a controllable risk level. To solve the model, we employ the column-and-constraint generation algorithm along with the alternative directions method (C&CG-AD). Furthermore, a second-order cone programming (SOCP)-based branch flow model (BFR) is incorporated to repair the duality gap of BFR and ensure the convergence of the C&CG-AD algorithm. A comprehensive case study is conducted to validate the effectiveness of the proposed model and solution methods. The findings demonstrate that the robust solution considering both uncertainties offers superior cost-effectiveness, operational reliability, and resilience against various failures compared to strategies that either average out uncertainties or overlook facility failures.

This paper proposes an Electric Vehicle (EV) aggregator bidding strategy in the reserve market. The EV aggregator determines the charging/discharging operations of EVs in providing reserve service for profits maximization. In the Day‐Ahead Market (DAM), the EV aggregator submits a bidding plan to the Independent Systems Operator (ISO) including base‐load and reserve up/down capacities plans. In the Real‐Time Market (RTM), the EV aggregator should deploy reserve based on the ISO's requirements, and the EV aggregator could receive income by deploying reserve or penalty for reserve shortage. The stochastic programming method is applied to address the uncertain reserve deployment requirements in RTM. In addition, Energy Storage Systems (ESS) are utilized by the EV aggregator to enhance the ability in providing reserve service. The aggregator–owner contract is designed to guarantee EV owners' economic benefits. Case studies show the expected profits of the EV aggregator are maximized and the risk of the reserve shortage is well managed, i.e., penalty is minimized. With the utilization of ESS, the performance of the EV aggregator in making response to the ISO's requirements is improved. That is, the required reserve percentage increases from 5.68% to 7.85%, and the deployed reserve percentage increases from 69.71% to 88.47%.

Electric vehicles (EV) are growing fast in recent years with the widespread concern about carbon neutrality. The development of charging infrastructures needs to be in phase with EV both in terms of quantity and charging time to decrease the range anxiety of EV users and resource waste. This paper proposed a multistage and dynamic layout optimization model based on mixed integer linear programming (MILP) for EV charging stations (CSs) to minimize the total social costs (TSC) consisting of the detour cost of EV users and the construction, relocation, and operating cost of CSs. The charging satisfaction coefficient and M/M/S/K model of queuing theory has been introduced to determine the desirable charging supply. The spatial-temporal distribution of charging demand was modeled based on the behavior analysis of travelers and over the discrete-time intervals for a day. Comparison studies based on the Sioux Falls network reveal that TSC with a multistage optimization strategy will drop 8.79% from that with a one-time optimization strategy. Charging service quality, relocation cost, and road network scales have a significant impact on the optimization results according to the sensitivity analysis.

The electric vehicle (EV) market is expanding rapidly to achieve the future goal of eco-friendly transportation. The scientific planning of energy supplement infrastructures (ESIs), with appropriate locations and capacity, is imperative to develop the EV industry. In this research, a mixed integer linear programming (MILP) model is proposed to optimize the location and capacity of ESIs, including vehicle charging stations (VCSs), battery swapping stations (BSSs), and battery charging stations (BCSs), in highway networks. The objective of this model is to minimize the total cost with the average waiting time for EVs being constrained. In this model, battery swapping and transportation behaviors are optimized such that the EV average waiting time can be reduced, and the average queue and service process waiting time is estimated by the M/M/1 model. Real-world data, i.e., from the London M25 highway network system, are used as a case study to test the effectiveness of the proposed method. The results show that considering battery transportation behaviors is more cost efficient, and the results are sensitive to the EV average waiting time tolerance, battery cost, and charging demand.