As with rapid development of the electric vehicle (EV) industry, charging infrastructures are built fast. However, the unreasonable deployments with increasing EVs contribute to a long queuing time for charging demand of EVs, especially in the peak hours. How to navigate a specific EV to economically satisfy its charging demand, while relieve the traffic burden, is an urgent problem. To address that, a price incentive based charging navigation strategy for electric vehicles is proposed. Unlike previous charging navigation studies that mainly focus on the EVs-transportation-power systems modeling, it considers the spatial-temporal influence of EVs' charging decision, especially the simultaneous charging requests. Specifically, the charging navigation framework with the collaborative working mode of EV- charging station-information exchange center (IEC)-intelligent transportation system (ITS) is established first. Following this, spatio-temporal distribution of the charging demand is obtained through the origin-destination (OD) analysis. After this, an event-driven dynamic queue model is constructed. It contributes to the modeling of the charging strategy, together with the proposed reservation opportunity cost mechanism. Finally, the simulation results indicate that the presented charging navigation strategy cannot only reduce the EV's charging cost but also improve the utilization rate of charging facilities, which verify its effectiveness.

Proper selection of electric vehicles (EVs) path will be helpful to improving the travel efficiency of EV users and alleviate their difficulties in charging during peak hours. In this context, a personalized fast-charging navigation strategy based on mutual effect of dynamic queuing is proposed. Firstly, an effective charging navigation system is presented to enable the integration of EV information terminals, fast-charging stations (FCSs), intelligent transportation system (ITS), and information processing center (IPC). Next, by incorporating the real-time mutual effect of dynamic queuing as well as considering the priority reservation cost (PRC) of the EV drivers who reserve charging priority before others, a dynamic reservation-waiting queue model is established. After that, on the basis of the above model, the coupling relations of the criterions that may affect the charging experience are investigated for the first time and the charging navigation plans in IPC are optimized for diverse EVs. Finally, the validity of the method is verified by conducting the simulation under the participation of 2250 EVs in the urban areas of a city. Results show that the proposed charging strategy will increase charging satisfaction on average by more than 10.9% in all analyzed cases. Additionally, the navigation can save the charging cost of EV owners while also improve the operation efficiency of FCSs.

The high penetration rate of electric vehicles (EVs) will have impacts on the operation of power system in the near future. One of the most significant impacts is the extreme load valley caused by “consistency” in off-grid actions of EVs automatically finishing their charging, which normally occurs at night. The dramatic load reduction at limited period not only leads to security problem of power system due to the deficiency of downward reserve in generation side, but also forces the thermal units to operate in the extremely uneconomical deep peak regulation (DPR) state. To address this issue, a coordinated optimization strategy considering nighttime DPR state of units and uncertain EV demand response (DR) is proposed in this paper. Firstly, unlike traditional studies, a detailed unit commitment (UC) model considering life loss and oil cost is established in this paper. After that, on the basis of elastic pricing method and day-ahead contract, price-based demand response (PDR) and incentive-based demand response (IDR) model are proposed to avoid units operating in the DPR state. The uncertainty of both IDR and PDR progress are considered as a whole in the form of opportunity constrain, and then transformed into deterministic linear constraints for solving by adopting sequence operations. Finally, various cases are simulated in the improved IEEE 30-bus system, the results indicate that the presented coordinated optimization strategy can reduce the units’ operating cost effectively, as well as bring benefits to EV owners.

With the continuously increasing penetration of electric vehicles (EVs), the mutual match between the distribution of charging resources and the spatial–temporal distribution of EV charging demands is becoming increasingly important. To address this, this paper proposes a novel two‐stage customized EV charging–navigation strategy. Building on previous research on the real‐time information from dynamic traffic networks, a personalized dynamic road impedance (PDRI) model is built to transform three main criteria (distance, time, and finance) affecting charging–navigation into comprehensive road impedance. In the first navigation stage, fast‐charging stations (FCSs) with the lowest overall objective are selected. In the second navigation stage, an improved Floyd–Warshall algorithm is utilized to identify the routes with the lowest personalized weight to the selected FCS in the PDRI model. Notably, the personalized preferences of EV drivers for the three primary criteria are considered in both stages of the navigation process. Finally, simulation results demonstrate a significant improvement in the degree of matching between charging navigation plans and drivers' personalized requirements, and a more balanced spatial–temporal distribution of EV charging demands among FCSs, which verifies the effectiveness of the proposed strategy.