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.

In renewable energy systems, fluctuating outputs from energy sources and variable power demand may deteriorate the voltage quality. In this paper, a model predictive control strategy without using any proportional-integral-derivative (PID) regulators is proposed. The proposed strategy consists of a model predictive current and power (MPCP) control scheme and a model predictive voltage and power (MPVP) control method. By controlling the bidirectional dc-dc converter of the battery energy storage system based on the MPCP algorithm, the fluctuating output from the renewable energy sources can be smoothed while stable dc-bus voltage can be maintained. Meanwhile, the ac/dc interlinking converter is controlled by using the MPVP scheme to ensure stable ac voltage supply and proper power flow between the microgrid and the utility grid. Then, a system-level energy management scheme is developed to ensure stable operation under different operation modes by considering fluctuating power generation, variable power demand, battery state of charge, and electricity price. Compared with the traditional cascade control, the proposed method is simpler and shows better performance, which is validated in simulation based on a 3.5-MW PV-wind-battery system with real-world solar and wind profiles.

This paper proposes a multi-source multi-product framework for coupled multi-carrier energy supplies with a biogas-solar-wind hybrid renewable system. In this framework, the biogas-solar-wind complementarities are fully exploited based on digesting thermodynamic effects for the synergetic interactions of electricity, gas and heating energy flows, and a coupling matrix is formulated for the modeling of production, conversion, storage, and consumption of different energy carriers. The multi-energy complementarity of biogas-solar-wind renewable portfolio can be utilized to facilitate the mitigation of renewable intermittency and the efficient utilization of batteries, and a multi-carrier generation scheduling scheme is further presented to dynamically optimize dispatch factors in the coupling matrix for energy-efficient con-version and storage, while different energy demands of end-users are satisfied. The proposed methodology has been fully tested and benchmarked on a stand-alone Microgrid over a 24-hour scheduling horizon. Comparative results demonstrate that the proposed scheme can lower the battery charging/discharging actions as well as the degradation cost, and also confirm its capability to accommodate high penetration of variable renewables.

Inverter-Based Resources (IBRs), including Wind turbine generators (WTGs), exhibit substantially different negative-sequence fault current characteristics compared to synchronous generators (SGs). These differences may cause misoperation of customary negative-sequence-based protective elements set under the assumption of a conventional SG dominated power system. The amplitude of the negative-sequence fault current of a WTG is smaller than that of an SG. This may lead to misoperation of the negative-sequence overcurrent elements 50Q/51Q. Moreover, the angular relation of the negative-sequence current and voltage is different under WTGs, which may result in the misoperation of directional negative-sequence overcurrent element 67Q. This paper first studies the key differences between the WTGs and SG by comparing their equivalent negative-sequence impedances with SG's. Then, simulation case studies are presented showing the misoperation of 50Q and 67Q due to wind generation and the corresponding impact on communication-assisted protection and fault identification scheme (FID). The impact on directional element is also experimentally validated in a hardware-in-the-loop real-time simulation set up using a physical relay. Finally, the paper studies the impact of various factors such as WTG type (Type-III/Type-IV) and Type-IV WTG control scheme (coupled/decoupled sequence) to determine the key features that need to be considered in practical protection studies. The objective is to show potential protection misoperation issues, identify the cause, and propose potential solutions.