Hierarchical User-Driven Trajectory Planning and Charging Scheduling of Autonomous Electric Vehicles

Autonomous electric vehicles (A-EVs), regarded as one of the innovations to accelerate transportation electrification, have sparked a flurry of interest in trajectory planning and charging scheduling. In this regard, this work employs mobile edge computing (MEC) to design a decentralized hierarchical algorithm for finding an optimal path to the nearby A-EV parking lots (PL), selecting the best PL, and executing an optimal charging scheduling. The proposed model makes use of unmanned aerial vehicles (UAVs) to assist edge servers in trajectory planning by surveying road traffic flow in real-time. Further, the target PLs are selected using a user-driven multi-objective problem to minimize the cost and waiting time of A-EVs. To tackle the complexity of the optimization problem, a greedy-based algorithm has been developed. Finally, charging/discharging power is scheduled using a local optimizer based on the PLs’ real-time loads which minimizes the deviation of the charging/discharging power from the average load. The obtained results show that the proposed model can handle charging/discharging requests of on-move A-EVs and bring fiscal and non-fiscal benefits for A-EVs and the power grid, respectively. Moreover, it observed that user satisfaction in terms of traveling time and traveling distance are increased by using the edge-UAV model

Robust decentralized optimization of Multi-Microgrids integrated with Power-to-X technologies

Nowadays, the enormous rising demand for hydrogen fuel cell vehicles (HFCVs) and electric vehicles (EVs) in the transportation sector has a significant contribution in growing of multi-energy microgrids (MEMGs) accompanied by hydrogen refueling stations (HRSs), EV parking lots (EVPLs) and power-to-hydrogen (P2H2) technologies. The competency to enhance the efficiency and the reliability in MEMG systems leads to form a networked structure called multi-microgrids (MMG). In this paper, a robust decentralized energy management framework is proposed for the optimal day-ahead scheduling of a set of interconnected hydrogen, heat, and power-based microgrids (MGs) in the presence of HRSs and EVPLs. The proposed MMG is a collaborative structure of hydrogen provider company (HPC) and electricity markets with novel technologies such as power-to-heat (P2H), power-to-hydrogen (P2H2), combined heat and power (CHP) units, multiple energy storages and demand response to improve the system flexibility in meeting multi-energy demands. The necessity of data privacy preservation methods for MGs has emerged when the interconnected MGs are operated as an MMG to satisfy different energy demands with minimum cost. Therefore, an iterative-based algorithm called the alternating direction method of multipliers (ADMM) is utilized to decompose the structure of the scheduling problem to minimize the total daily cost of the MMG system while protecting the data privacy of MEMGs. In the proposed structure, the robust optimization model is able to manage the uncertainty by considering the worst-case scenario for electricity price in different conservativeness levels as MEMGs are sensitive to electricity price fluctuations. Finally, the simulation results represent the effectiveness of the proposed decentralized model under the worst case of electricity market price to meet the demand for electricity, heat, and hydrogen