One of the most widely used techniques for controlling the dc microgrid is the droop control method. The associated problems of the droop based systems such as the current sharing errors and the voltage deviation are solved using current sharing loops and secondary control loop, respectively. This paper presents an adaptive droop scheme for dc microgrids to overcome the non-linearity of the system. The droop resistance is adjusted using the adaptive PI controller to eliminate the current sharing error of each unit in the microgrid. In addition, another adaptive PI controller is dedicated for the secondary loop to regulate the dc bus voltage of the microgrid by shifting the droop lines. In the proposed scheme, only the current and voltage at the dc bus of the microgrid need to transmit through low-bandwidth communication channels to individual units. Moreover, the Sliding Mode Control (SMC), which is distinguished by robustness and fast dynamic response, is utilized to manipulate the output voltage and the input current of each converter, instantaneously. The dynamic performance of the proposed adaptive droop scheme is evaluated using the PSCAD/EMTDC simulation package.

This paper proposes a new approach for optimal operation of an Electric Vehicle (EV) battery-swapping station (BSS) based on Rolling-Horizon optimization (RHO). The BSS has several swapping bays such that each can accommodate an EV for swapping single or multiple battery units. The proposed BSS model considers serving different types of EVs using a heterogeneous battery stock. The charging of the depleted batteries (DBs) is performed using continuously controlled variable chargers which makes it more flexible for providing grid services. While previous studies focused on day-ahead modeling of BSSs, our study considers BSS dynamic scheduling. The goal is to maximize the daily profit using an RHO mechanism to provide optimal swapping and charging/discharging processes. The problem is defined as mixed-integer nonlinear programming (MINLP), then it’s linearized into a mixed-integer linear problem (MILP) to reduce the computational complexity. To predict the EV’s swapping demand, a long short-term memory (LSTM) recurrent neural network is utilized as a time series forecasting engine. The proposed model is validated through a set of case studies comparing the LSTM-based RHO mechanism versus unscheduled operation and day-ahead scheduling. Simulation results demonstrate that the proposed dynamic scheduling mechanism increases the profit between 10% and 25.7% compared to the day-ahead scheduling. Furthermore, the number of EVs served using the proposed approach increases between 11% and 14% compared to the day-ahead model.

The frequency of power systems is very sensitive to load variations. Additionally, with the increased penetration of renewable energy sources in electrical grids, stabilizing the system frequency becomes more challenging. Therefore, Load Frequency Control (LFC) is used to keep the frequency within its acceptable limits. In this paper, an adaptive controller is proposed to enhance the system performance under load variations. Moreover, the proposed controller overcomes the disturbances resulting from the natural operation of the renewable energy sources such as Wave Energy Conversion System (WECS) and Photovoltaic (PV) system. The superiority of the proposed controller compared to the classical LFC schemes is that it has auto tuned parameters. The validation of the proposed controller is carried out through four case studies. The first case study is dedicated to a two-area LFC system under load variations. The WECS is considered as a disturbance for the second case study. Moreover, to demonstrate the superiority of the proposed controller, the dynamic performance is compared with previous work based on an optimized controller in the third case study. Finally in the fourth case study, a sensitivity analysis is carried out through parameters variations in the nonlinear PV-thermal hybrid system. The novel application of the adaptive controller into the LFC leads to enhance the system performance under disturbance of different sources of renewable energy. Moreover, a robustness test is presented to validate the reliability of the proposed controller.

The utilization of fuel cells (FC) in automotive technology has experienced significant growth in recent years. Fuel cell hybrid electric vehicles (FCHEVs) are powered by a combination of fuel cells, batteries, and/or ultracapacitors (UCs). By integrating power converters with these power sources, the FCHEV system can overcome the limitations of using them separately. The performance of an FCHEV is influenced by the efficiency of the power electronics converter controller, as well as the technical efficiency of the power sources. FCHEVs need intricate energy management systems (EMSs) to function effectively. Poor EMS can lead to low efficiency and accelerated fuel cell and battery degradation. The literature discusses various types of EMSs such as equivalent consumption minimization strategy, classical PI controller, fuzzy logic controller, and mutative fuzzy logic controller (MFLC). It also discusses a systematic categorization of FCHEV topologies and delves into the unique characteristics of these topologies. Furthermore, it provides an in-depth comparative study of EMSs applied in FCHEVs, encompassing rule-based, optimization-based, and advanced learning-based approaches. However, comparing different EMSs can be challenging due to the varying vehicle and system parameters, which might lead to false claims being made regarding system performance. This review aims to categorize and discuss the various topologies of FCHEVs, highlighting their pros and cons, and comparing several EMSs based on performance metrics such as state of charge (SOC) and FC deterioration. This paper seeks a deeper comprehension of the recent advancements in EMSs for FCHEVs. It offers insights that can facilitate a more comprehensive grasp of the current state of research in this field, aiding researchers in staying up to date with the latest developments.

The use of communication links between microgrid components has made the transmitted data vulnerable to cyber-attacks. This paper presents distributed cyber-attack detection and mitigation layers integrated with the primary and secondary control loops of a meshed DC microgrid. Voltage and current cyber-attacks mitigation layers based on the Kalman Filter (KF), as a robust state estimator, are proposed. The KF is utilized to estimate accurately the actual authentic terminal voltage and current from the measurable system states. Using the discrepancies between the estimated data and the measurements under cyber-attack, controllers are assigned to set proper corrective actions. These actions secure the primary and secondary loops to achieve accurate terminal voltage regulation and current sharing, respectively, for all Distributed Generator Units (DGUs) in the microgrid. Not only accurate state estimation is required for perfect cyber-attack mitigation, but fast control response is crucial. Therefore, Adaptive Proportional Integral (API) and Fractional API (FAPI) controllers are proposed for cyber-attack mitigation layers. Simulation results are provided to evaluate the dynamic performance of the proposed strategy. Moreover, experimental work is conducted to verify the effectiveness of the proposed distributed layers and the superiority of the FAPI controller in terms of perfect compensation for different types of cyber-attacks.