With the emerging electric vehicle (EV) and fast charging technologies, EV load forecasting has become a concern for planners and operators of EV charging stations (CSs). Due to the nonstationary feature of the traffic flow (TF) and the erratic nature of the charging procedures, EV charging load is difficult to accurately forecast. In this article, TF is first predicted using a deep-learning-based convolutional neural network (CNN), and different forecast uncertainties are evaluated to formulate the TF prediction intervals (PIs). Then, the EV arrival rates are calculated according to the historical data and the proposed mixture model. Based on TF forecasting and arrival rate results, the EV charging process is studied to convert the TF to the charging load using a novel probabilistic queuing model that takes into consideration charging service limitations and driver behaviors. The proposed models are assessed using the actual TF data, and the results show that the uncertainties of the EV charging load can be learned comprehensively, indicating significant potential for practical applications.

A planning of integrated electricity–hydrogen–gas system to supply hydrogen for hydrogen vehicles (HVs) with the consideration of carbon emission flow (CEF) is proposed in this article. From the perspective of energy consumption, hydrogen production will inevitably produce carbon dioxide. This article innovatively presents the CEF model to quantify the distribution of carbon emission in each energy hub by tracing the energy flow in the coupled network. After solving the CEF model, a novel tri-level planning method is utilized to coordinate the plan of electricity–hydrogen–gas integrated network to highlight the role of hydrogen in reducing carbon emission of HVs. Moreover, two major hydrogen production methods, seawater electrolysis and steam methane reforming, are considered and analyzed simultaneously to provide hydrogen for HVs. Therefore, the proposed approach will satisfy the growing requirement of carbon reduction by installing new clean energy equipment in electricity–hydrogen–gas integrated network. A coupling system of 24-node power system, 9-node hydrogen energy network, and 7-node natural gas network is utilized in the case study. Simulation results show that the proposed model plays an effective role in reducing carbon emission of the coupled system with growing number of HVs.

Under the pressure of climate change and the energy crisis, electric vehicles (EVs) have experienced a marked growth in many countries recently. However, the environmental benefits of EVs cannot be achieved if the charging power from the electricity grid is still mainly provided by fossil-fueled power plants. How to construct a low-carbon fast-charging system has become an emerging research topic. In this context, a novel carbon-oriented expansion planning model of the integrated electricity and natural gas system (IEGS) with EV fast-charging stations (FCSs) is proposed to determine the optimal alternatives, locations, and sizes for ecofriendly candidate assets, including roof-top photovoltaic (PV) panels and fuel cell (FC) units in each FCS, as well as renewable energy units and carbon capture and storage (CCS) systems in IEGS. To guarantee the carbon reduction performance of the planning scheme, the carbon emission cap of the EV charging system is specified by the planner and optimally allocated among multiple FCSs according to their carbon reduction abilities. Furthermore, the proposed model can not only consider the initial investment and operation costs but also take into account the expected adaptation cost to future scenarios that are generated in line with three uncertainties, namely, the traffic flow levels, conventional load levels, and renewable-based power generation levels. Finally, the proposed expansion planning method is illustrated by conducting numerical experiments in case studies.

In this paper, a day-ahead optimal scheduling strategy for the DSO based on EV aggregation is proposed. Generally, the uncertain parameters of EVs, i.e. , arrival and departure times as well as the charging demands, can be effectively modeled by a Gaussian mixture model (GMM) with historical charging records. The empirical data of net load error, i.e. , mean values and covariance, can be obtained from historical data, while the explicit probability distribution function may not be fully known. Therefore, a distributionally robust optimization (DRO) model is formulated for the DSO to characterize the net load uncertainty. Then, an EV aggregation-based reservation capacity prearrangement method is proposed to specifically address the net load error. Next, with the reservation capacity from EVs, the DRO model is convexified by transforming into two subproblems with deterministic forms. Finally, simulation and comparison tests are conducted on the 33-bus distribution network to demonstrate that the proposed approach achieves a tradeoff between economic performance and computation efficiency compared with the scenario-based stochastic optimization method and achieves a tradeoff between operation costs and system reliability compared with the chance constraint method.

The optimal frequency control of autonomous microgrids (MGs), i.e., to achieve fast frequency recovery and dynamic power adjustment of the distributed generators in proportion to predefined participation factors, can be achieved in a fully distributed way based on the subgradient consensus protocol. However, such a distributively controlled MG is susceptible to different types of cyber attacks infiltrated from different locations. In this article, a robust and resilient distributed optimal frequency control scheme is proposed to address the threat of cyber attacks. It is facilitated by introducing an auxiliary networked system interconnecting with the original cooperative control system. On condition that the cyber attacks are within certain ranges, the robust design can maintain the functionalities by significantly attenuating the impact. Otherwise, the cyber attacks can be easily detected, and resilient reactions can be taken to mitigate their influences via isolation. Simulation results in a modified IEEE 34-bus MG validate the effectiveness of the proposed approach.

Electric vehicles (EVs) provide new options for energy balancing of power systems. One possible way to use EVs in energy balancing is to let each distribution system mitigate its forecast uncertainties through the flexibility of EVs. In consideration of the difficulties to directly govern a large number of EVs, it is more reasonable for distribution systems to dispatch electric vehicle aggregators (EVAs). Without influencing EVs driving activities in the next day, a model is established for distribution systems to make use of EVAs, whose contributions are delaying uncertainties through their temporal flexibility and thus creating opportunities for uncertainties from different hours to offset each other. In the established model, a scheme of uncertainty transferring is proposed to relieve interruption to EVAs and distributionally robust optimization is adopted to evaluate the operation plans' average performance with temporal and spatial uncertainty correlations considered. Comprehensive case studies are carried out based on charging demands of EVAs simulated from real traffic data to verify the effectiveness of the proposed model.