This paper proposes a scalable model-free multi-agent deep reinforcement learning (MADRL) framework for voltage regulation in grid-interactive efficient buildings. Unlike traditional methods that rely on reactive power control, the proposed approach utilizes active power adjustment through intelligent demand response (DR) scheduling. The architecture features a decentralized control structure, where customer agents optimize their appliance usage based on dynamic incentives from an aggregator agent. The optimization problem considers various constraints such as user comfort, electricity pricing, voltage deviation penalties, and the presence of distributed photovoltaic (PV) generation. A multi-objective function integrating dynamic price signals, user dissatisfaction, and voltage deviation is formulated. The aggregator leverages voltage-aware incentive signals to nudge consumers toward grid-supportive load behaviors. Simulation investigations are curried out to show that the MADRL framework reduces peak and mean load, improves voltage stability, and preserves user privacy. The paper aims to demonstrate the potential of decentralized, model-free DR systems in modern distribution grids.

As the global transition toward electrification accelerates across the transportation and stationary energy storage sectors, the critical need for accurate end-of-life (EoL) prediction of lithium-ion batteries (LIBs) has become increasingly apparent. Current battery failures impose substantial costs on manufacturers through warranty claims, while creating significant safety risks that threaten both electric vehicle (EV) adoption and grid-scale energy storage deployment. This paper examines the modeling approaches to predict the EoL and the remaining useful life (RUL) of LIBs in EVs. The paper includes data-driven models, physics-based approaches, and hybrid frameworks. Through systematic analysis of recent advances, the paper identifies that hybrid models demonstrate superior performance compared to single-approach methods, effectively addressing the inherent limitations of individual methodologies across diverse operating conditions. Key challenges remain in Battery Management System (BMS) integration complexity, data quality constraints, and real-time computational requirements. The proposed review establishes that next-generation prediction systems and incorporates transfer learning, digital twin technologies, and second-life battery strategies to support sustainable EV adoption and circular economy principles.

Model Predictive Control (MPC) has become a widely adopted control technique for Multilevel Inverters due to its ability to manage multi-objective optimization problems under system constraints. However, a key challenge in MPC implementation lies in selecting appropriate weighting factors for the cost function, as fixed values often lead to suboptimal performance under dynamic operating conditions. Thus, this paper presents a lightweight auto-tuning method for the voltage weighting factor in Finite Control Set MPC (FCS-MPC), applied to a single-phase grid-connected 9-level Crossover Switches Cell inverter. The proposed approach employs low computational complexity machine learning models, Linear Regression and Support Vector Machine, trained offline on a minimal dataset comprising the DC link voltage and reference current. These models are embedded into the control loop to enable real-time adjustment of the voltage weighting factor. The presented comparative simulation results confirm the effectiveness of the proposed technique across a wide range of operating conditions. Compared to more complex AI-based solutions, this work contributes a simple yet effective ML-based tuning strategy that improves control performance with minimal computational overhead.

This paper presents a multi-objective control strategy for a four-port solid-state transformer (SST) interfacing photovoltaic (PV) system, battery energy storage (BESS), utility grid, and a 48V data center load. The proposed control approach simultaneously optimizes multiple objectives, including power allocation and load voltage regulation. The main objectives of the proposed approach are to supply a constant 48V to the data center while maximizing renewable energy utilization, and balancing the demand and supply by dynamically injecting the extra available power into the grid or the battery and consuming it from the grid or the battery if there is no sufficient renewable energy availability. The proposed approach is experimentally implemented and its effectiveness is validated through a set of different case studies.

This paper presents a new three-level reduced switch-count three-phase F-type rectifier and its control without current sensing. Unlike the existing three-level three-phase rectifier topologies, which use twelve switching devices, the proposed topology uses only nine switches. This, in turn, reduces costs and enhances reliability. Moreover, the voltage stress on the switching devices is more evenly distributed across the components, leading to the use of lower voltage-rated devices. The control of the proposed rectifier is achieved by a control strategy that eliminates the need for current sensing which reduces control implementation costs. The functionality of the proposed topology and its control method is verified using MATLAB/Simulink simulations under different operating conditions, including steady-state operation, step changes in resistive load, and variations in the reference DC-link voltage. The simulation results verify that the DC-link voltage is effectively controlled, and the total harmonic distortion (THD) of the grid currents remains within the limits defined by international standards.

This paper proposes a DC plasma power supply topology with systematic design and robust voltage control approach. The proposed topology consists of a three-port solid-state transformer (SST), comprising one port for the input source, a second port to deliver high voltage (HV) required for plasma ignition, and a third port for supplying a constant low voltage (LV) for sustained plasma reactor operation. A comprehensive design methodology is presented to determine all passive elements, including the resonant tank components and transformer’s turns ratios. Moreover, a seamless transition algorithm between ignition and operation modes is presented. To achieve micro-scale dynamic response, an inverse model predictive control (IMPC) strategy is employed to identify the necessary switching frequency in feedforward fashion. In parallel, a PI controller is used to eliminate the deviation and steady state error of the output voltage and ensure asymptotic error convergence, even in the presence of parameter uncertainties. The proposed circuit, seamless transition algorithm, and the control technique are implemented on a lab-scale test-bed with voltage range between 0-1200 V.

Transient Stability (TS) remains a critical concern in ensuring secure operation of modern power systems, particularly with the growing complexity introduced by renewable energy integration, power electronic devices, and hybrid AC/DC infrastructures. Traditional methods for transient stability assessment (TSA) and transient stability-constrained optimal power flow (TSC-OPF), such as time-domain simulations and energy function-based techniques, face limitations in scalability, computational efficiency, and real-time applicability. This paper presents a holistic review of Machine Learning (ML) approaches adopted for TSA and TSC-OPF, covering a range of models from traditional learners (e.g., support vector machines, decision trees) to DL (e.g., convolutional neural networks, long short-term memory networks, graph neural networks) and Reinforcement Learning (RL) techniques. For each domain, methodologies will be categorized, highlighting key advancements, and discussing trade-offs in performance. Furthermore, existing challenges are identified and future research directions are proposed, emphasizing on hybrid modeling, uncertainty handling, real-time assessment and RL challenges. This review aims to serve as a timely reference for researchers and practitioners working on data-driven solutions for power system TS.

The rapid advancement in electric vehicle (EV) technology stimulated the development of fast chargers, with innovative solutions in power electronics (PEs), communication, protection, and control. This literature review explores the evolving landscape of fast EV charging infrastructures, and focuses on their topologies, advanced PEs solutions, and associated challenges. In addition, this article explores the multifaceted aspects of fast charging systems. In addition, the article discusses the protection systems, communication systems, and control techniques for facilitating seamless, safe, and reliable interaction between the grid, fast EV chargers (FEVCs), and the EV. Furthermore, the review investigates the interaction between the grid and FEVCs through ancillary services, and highlights their potential to enhance grid stability and reliability. Although a significant research is conducted in the literature and a significant progress is achieved, a notable gap persists between the available academic literature and commercially deployed solutions. Investigating this gap is crucial for accelerating the adoption of FEVC technologies and maximizing their benefits for both consumers and grid operators. Therefore, a reference design is synthesized that combines the recommended elements identified throughout the literature.

Integrating Deep Reinforcement Learning (DRL) into building energy management systems presents a transformative approach to enhancing grid stability and efficiency. Grid-Interactive Efficient Buildings (GEBs), equipped with advanced DRL algorithms, can dynamically optimize their energy consumption and production in response to real-time grid conditions. This paper explores the innovative applications of DRL in GEBs, highlighting its potential to autonomously optimize energy decisions, accommodate the stochastic nature of renewable energy sources, and effectively respond to variable building energy demands. Through a comprehensive analysis, this study not only sheds light on the successes to date but also maps out the significant challenges that must be overcome. By addressing these challenges, DRL for building energy management can fully realize its potential, leading to a more sustainable and efficient energy future.

Virtual impedance-based power sharing between several interconnected grid-forming inverters (GFMI)s in a power electronics-dominated grid (PEDG) does not consider the available power reserves. This non-optimal power-sharing may result in the overloading of nearby inverter-based resources (IBR)s exhibiting lower effective line impedances in the network. This paper proposes an optimal and controllable power-sharing scheme based on a modular artificial neural network (ANN) that can consider various factors like power reserve, line losses, incentives from the grid, etc., and increases the system's scalability. An optimum power allocator (OPA) mechanism that monitors the output active powers and line losses of all the IBRs is proposed to address the issue of uncontrolled power sharing. The OPA dynamically adjusts power allocation weights using linear programming to optimize the overall loss minimization cost function. Moreover, an artificial neural network-based virtual impedance optimizer (ANN-VIO) is also proposed, which estimates the corresponding virtual impedance (VI) value for each GFMI unit in the PEDG. The primary controller of GFMI uses these estimated VI values to adjust its point of common coupling (PCC) voltage reference and attain optimum proportionate power sharing within the overall PEDG. Multiple case studies are performed to validate the effectiveness of the proposed GFMIs power-sharing scheme.

Power electronics dominated grid (PEDG) consisting of multiple inter-connected distributed generation (DG) units are getting more popular because of their benefits in reducing the stress on main transmission lines, limiting line losses, and improving the efficiency and stability of the power system. Often a PEDG is comprised of several energy sources such as solar, wind, and energy storage devices. For that reason, optimal power sharing between all the sources is crucial to enhance the reliability and resiliency of the network. Hence, this paper provides comprehensive insights into the operation and working principles, limitations, and challenges of the power-sharing schemes employed in PEDG. More specifically, control laws of the droop, virtual synchronous generator (VSG), and Lienard and Consensus-based oscillators are briefly overviewed. In addition, the inherent drawbacks and challenges of existing power-sharing schemes are explained for inductive, resistive, and complex line impedances. Moreover, their performance under dynamic variations of the source availability, inverter power reserve, line losses, and generation costs are discussed in detail.

Digital twins (DTs) are emerging as effective tools for power electronic converters, addressing important objectives such as real-time monitoring, fault detection and predictive maintenance. Among these power converters, the dual active bridge (DAB) stands out for DC-DC conversion applications. However, the non-linear dynamics of DABs pose considerable challenges to their accurate modeling. To address these challenges, this paper investigates the use of machine learning (ML) and deep learning (DL) models for DT implementation in DABs. Specifically, XG-Boost and dense neural network (DNN) models are employed to construct DTs compatible with low-cost microcontrollers. These DTs are operated in parallel with the physical DAB converter to predict its output voltage in real-time. Experimental results demonstrate that both of the models perform well in terms of accuracy and applicability to low-cost microcontroller. However, the DNN is more reliable and the XGBoost is simpler.

This paper presents a grid voltage sensorless model predictive control (MPC) technique for a single-phase fivelevel active neutral point clamped flying capacitor (ANPC-FC) rectifier. The control strategy utilizes MPC to regulate the grid current and ensure equilibrium in the voltages of the flying and DC side capacitors, without measuring the grid voltage. A state feedback observer (SFO) is responsible to provide the grid voltage estimation to the MPC. Achieving this involves integrating all controlled parameters into a finite control set MPC (FCS-MPC). Moreover, the reference grid current is generated to regulate the DC link voltage based on a power equilibrium technique supported by a feed-forward loop. The proposed controller is implemented and evaluated using MATLAB/Simulink. Simulation results are carried out to show that the proposed MPC technique effectively achieves control objectives, while ensuring accurate tracking, and resisting voltage distortions.

Grid-forming inverters (GFMIs) are promising solutions for voltage and frequency support in upcoming power electronics-dominated grids (PEDG). However, current state-of-the-art decentralized control schemes for GFMIs are designed for operation under normal conditions. These decentralized control schemes could result in an adverse dynamic response if a cluster of GFMIs network disconnects from the rest of the grid due to a fault. The dynamic response of GFMIs could be improved with coordinated control schemes which require communication and make the system more complex. This paper proposes a decentralized Artificial Intelligence (AI)-based method for online fault detection and localization to enhance the dynamic response of GFMIs without using a communication layer. Each GFMI will detect and localize the line tripping based on its output active and reactive power measurement. Then, according to the topology of the grid after line tripping, each GFMI updates its nominal power and inertia to suppress the frequency transient caused by the line tripping. The inertia and damping factor of the GFMIs are then recalculated to ensure optimal operation of the network of inverters after line tripping. Several case studies are presented to validate the effectiveness of the proposed method in the timely detection, localization, and mitigation of adverse frequency transient after line tripping in a communication-less manner.

This paper proposes a multi-objective model predictive control (MPC) for a grid-connected ten-switch three-phase three-level inverter. The main objective of the proposed MPC method is to control the grid current. In addition, the voltage balancing of the DC capacitors in the ten-switch three-level inverter is essential to generate voltage levels properly. Therefore, two objectives need to be achieved simultaneously. The multi-objective cost function feature of the MPC method is used to achieve these objectives. The simulation studies are performed using MATLAB/Simulink environments to investigate the effectiveness of the proposed MPC method. The results show that the proposed control method exhibits accurate performance under steady-state conditions and fast dynamic responses under transient conditions. Moreover, the grid current spectrums show that the total harmonic distortion is 2.21% in normal operation, and it is under the desired limits, even with a 50% parameter mismatch in filter inductance.

Electric vehicle (EV) charging stations have been evolving to offer better and more efficient power delivery methods. A key area of research in this field involves predicting the power consumption of EV charging stations. Many researchers have addressed this issue using machine learning and deep learning methods, however, forecasting models struggle to predict individual charging sessions with high resolutions. In this paper, a deep neural network (DNN) is constructed to forecast the EV charging current profile and state of charge (SoC). Charging current and SoC are mathematically formalized and embedded into the DNN’s loss calculation to build a physics-informed neural network (PINN). The performance of the models is assessed through analysis of their prediction error, utilizing actual data from real EV charging sessions.

The integration of distributed energy resources (DERs) via three-phase inverters plays a significant role in supporting the stability and reliability of AC power systems. This paper focuses on meeting grid code (GC) requirements concerning the integration of renewable energy sources (RES) and electric vehicles (EVs). A novel model predictive control (MPC) algorithm is proposed to provide the required grid support for parallel inverters during disturbances. The proposed strategy is validated across various fault scenarios, including symmetrical and asymmetrical faults. The considered scenarios focused on different severity levels of voltage sags, demonstrating the algorithm's robustness and adaptability in maintaining grid stability under adverse conditions. The results showcase the effectiveness of the topology in eliminating double-line frequency disturbances in active power during fault conditions. Moreover, it ensures accurate active and reactive power injections into the grid while adhering to operational standards and meeting the required GC.

This paper presents an adaptive rate reaching law based sliding mode control method for a three-phase F-type converter. Unlike the existing reaching laws in which the gain is always constant, the proposed reaching law employs an adaptive gain that is continuously updated with the grid current errors. This means that the gain can reach to its smallest value when the grid current errors are zero. It is shown that the minimum gain reduces the reaching time. As a consequence of using adaptive gain, the dynamic response of system is improved considerably (i.e: the reaching time is reduced). Moreover, it is shown that the three-phase F-type converter can be operated as bidirectional battery charger as well as shunt active power filter (SAPF) for compensating the nonlinear loads connected at the point of common coupling. The proposed control and correct operation of system are validated by MATLAB/Simulink simulations under various operating conditions which include battery charging mode, battery discharging mode, and simultaneous operation as SAPF and battery charging/discharging.

Droop-based control is widely used in islanded microgrids to regulate power sharing among distributed generators (DGs) according to their ratings, without the need for communication. However, simultaneous and accurate active and reactive power sharing regulation represents a key challenge for droop-based techniques. This issue arises due to the inconsistent voltage drops in feeder lines and the distinct local loads distributed across the microgrid DGs. To this end, this paper introduces a new solution for droop-based approaches to enhance power sharing accuracy in the context of local loads, without relying on any communication means between DGs. The proposed solution effectiveness is demonstrated through MATLAB/Simulink and is validated for conventional, inverse, and angle droop control.

This study introduces a novel Boost Packed E-Cell Thirteen-Level Inverter (BPEC13), a boost multilevel inverter (MLI) with promising voltage boosting capability. This novel design features reduced count components consisting of nine power switches, a single four-quadrant switch, and two DC capacitors. A key feature of the BPEC13 inverter is its ability to boost the voltage, achieving a maximum voltage level that is 1.5 times the input DC voltage. To harness the capabilities of the BPEC13 inverter, a finite-control set model predictive control (FCS-MPC) algorithm has been developed. This control strategy is carefully designed to ensure superior grid current control and maintain balanced DC-link capacitor voltages. The effectiveness of the FCS-MPC with the BPEC13 inverter is thoroughly evaluated through numerical simulations. The results confirm that the novel BPEC13 inverter, utilizing FCS-MPC, delivers superior grid current quality with total harmonic distortions of just 0.42. Additionally, it maintains DC-link capacitor voltage balance while providing significant voltage boosting capabilities.

The increase in the penetration of renewable energy sources makes the future grid more vulnerable to frequency and voltage fluctuations. Electric vehicle (EV) charging stations can be used as a source of energy to support the grids’ voltage and frequency during load or generation disturbances. However, managing the contribution of each EV in the total energy injected into the grid, considering each EV capacity while maintaining minimal losses, is challenging. This paper presents an algorithm to distribute the total injected energy among the EVs based on their state of charge, which determines the optimal power injected by each EV inside the parking lot. Furthermore, the design procedure of the CLLC converter for the EV charging stations is elaborated to meet the requirements of each converter and DC charging station for grid integration. The control of the CLLC converter for both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) conditions is described. The simulation results for different scenarios for EV arrival and departure show the effectiveness of the proposed algorithm in minimizing the loss according to the SOC.

This paper proposes a novel sensorless control method for a simplified single-stage rectifier (SSSR) based electric vehicle (EV) charger. The proposed control strategy consists of three cascaded control loops, PI controller for DC link voltage regulation, another PI controller for grid current regulation, and a model predictive control (MPC) for inductor current regulation. Additionally, a novel state observer technique is proposed specifically for this topology, offering the advantages of simplicity, robustness, reduced number of tuned parameters, and compatibility with low-cost industrial microcontrollers. The effectiveness of the proposed control approach and observer technique is validated through extensive simulations across various scenarios and operating conditions, including boost operation, buck operation, and operation under uncertainties. The obtained results demonstrate the efficacy of the proposed method in achieving precise regulation of DC link voltage, grid current, and inductor current without the need for explicit inductor current sensor feedback.

This paper proposes a method for optimizing the charging process of electric vehicles (EVs) within the context of renewable energy integration. The proposed method continuously monitors the maximum available power from the renewable energy sources (RES), grid conditions, and EV’s requirements. Upon receiving reference charging or power signals from the connected EV, the system computes the surplus or required power and adjusts grid interaction accordingly. The proposed method offers several advantages. Firstly, it optimizes energy harvesting from RESs by dynamically adjusting grid and EV interactions based on real-time monitoring. Secondly, it enhances grid stability by intelligently controlling power flow, injecting or consuming surplus power in response to the grid condition. Thirdly, it leverages the growing number of EVs connected to the grid, effectively utilizing them as distributed energy storage systems. To validate the effectiveness and efficiency of the proposed method, extensive simulation tests are conducted under diverse scenarios.

The Controller Area Network (CAN) Bus is a commonly used communication protocol for different applications including vehicle controllers and power electronics converters which are the application focus in this paper. One drawback of CAN-bus is the lack of security measures in the protocol, which can be problematic if the communication line is exposed to attackers. This paper proposes a novel method to secure CAN-bus communication that is light-weight and applicable for power converters and microgrid applications. The proposed solution aims to protect against eavesdropping, false data injection, and replay attacks. The security scheme is implemented and deployed on a low-cost microcontroller that is used to control power sharing rectifiers. Experimental results are carried out to show that the proposed method effectively secures the channel against attacks with minimal time and space overhead.

This paper presents a hysteresis current control for single-phase single-stage buck-boost grid-connected inverters. The inverter topology employs four switches that are operated with the grid frequency and two bidirectional switches that are operated with high switching frequency. Hence, unlike the existing single-stage inverter topologies, the switching losses are minimized. Moreover, this inverter configuration has the capability to operate both in buck and boost modes and utilizes only a single inductor. The proposed hysteresis current control eliminates the need for designing a dedicated modulation technique for this inverter topology. An active damping technique is utilized to cope with the resonance damping problem without employing additional sensor. The reference current needed in the hysteresis controller is produced using a proportional-resonant (PR) controller whose input is the grid current error. The use of PR controller guarantees zero steady-state error in the grid current. The validity and superiority of the proposed control method are confirmed by MATLAB/Simulink simulations under various operating conditions which include buck mode, boost mode, transition from boost mode to buck mode, reference current change and virtual resistor variation. The simulation results show that the proposed control method is able to transfer the desired current into grid successfully under all operating conditions.

This paper proposes a hysteresis current control (HCC) method for a single-inductor buck-boost non-isolated onboard charger for electric vehicles. The charger is capable of working both in the boost and buck modes. The proposed HCC relies on the buck-boost inductor current and its reference which is generated using a proportional-resonant (PR) controller using grid current error. The reference current generated by PR controller is modified to suppress the oscillations in the inductor currents. An active damping by using a virtual resistor connected in series to filter inductor is used which does not require an additional sensor. A proportional-integral (PI) controller is used to generate the amplitude of grid current reference, which is utilized in constant current (CC) and constant voltage (CV) modes. The effectiveness of the proposed control strategy as well as the control method, is investigated by simulation studies by considering two different battery voltage levels (48V and 350V). The results show that the proposed method is able to charge the battery in CV and CC modes. Moreover, the grid current is maintained in unity power factor at a reasonably low total harmonic distortion (THD) which is smaller than the limits recognized by international standards.

This paper presents a model predictive control (MPC) technique for a three-phase five-level active neutral point clamped flying capacitor (ANPC-FC) rectifier. The proposed control algorithm depends on a single loop control to regulate the DC link voltage, grid currents, and to balance the voltages of the DC side capacitors. This is achieved by incorporating all controlled states into a multi-objective cost function (MOCF), which encompasses the tracking errors of AC currents, DC link voltage, and DC side capacitor voltage balance. The proposed controller is implemented and tested in MATLABI Simulink environment. Investigations are conducted to prove that the proposed MPC successfully meets the control objectives, enabling precise tracking and ability to deal with abnormal grid conditions, including uncertainties, load variations, voltage sags and swells, as well as unbalanced grid voltages.

In this paper, a novel multi-mode converter (MMC) for battery storage application is proposed. The MMC consists of a non-inverting Cuk converter to feed a load, and a bidirectional buck-boost converter to regulate the battery voltage during charging and discharging. The proposed MMC is used to control the power flow between an input source, a battery, and a load. Furthermore, an adaptive selection algorithm is proposed to enable battery charging or discharging based on the source voltage, the state of charge (SoC), and a reference SoC. The proposed converter has many advantages such as simplicity, and supporting many modes of operation. The proposed MMC is controlled using a sliding mode controller and its performance is described theoretically and validated through simulations. The final version of the paper will contain experimental results.

48th Conference of the Industrial Electronics Society-IECON-Annual -- OCT 17-20, 2022 -- Brussels, BELGIUM

This paper presents a passivity-based control approach for four-switch buck-boost (FSBB) DC-DC converters. The state variables are selected as the inductor current and the capacitor voltage errors. The passivity based control is formulated that targets to drive the inductor current and capacitor voltage to their reference values. The reference of the inductor current is produced by a proportional-integral controller that operates on the capacitor voltage error. Two control input equations are defined for buck and boost operations due to the fact that FSBB converter contains separate switches for buck and boost stages. As a consequence of controlling each stage by the dedicated control input, the passivity based control method eliminates the need for using a mode detection algorithm. The validity and superiority of the proposed approach has been studied by Matlab/Simulink simulations under load step, reference voltage step and operation mode variations for buck and boost modes. The results reveal that the proposed control approach can regulate the output voltage under all cases.

This paper proposes a self-tuning current-control algorithm for a three-phase three-level T-type controlled rectifier. The aim of this algorithm is to control the AC-side current without any previous knowledge about the parameters of the system. The dynamic model of the rectifier is assumed as a black box, where all internal parameters are considered unknowns. The proposed algorithm depends on Recursive Least Squares Algorithm (RLSA) to estimate the state-space model of the system. Then, the estimated model is used to derive the gains optimally for a Linear Quadratic Tracker controller (LQT). The obtained results prove the ability of the proposed algorithm to estimate the entire dynamic model accurately and to control the system with an accurate, smooth, and rapid response. In addition to zero tracking error and no overshot.

Vehicle mobility analysis is a critical input to make data-driven decisions to deploy electric vehicle (EV) charging infrastructures that are required for mass EV adoption. Traditionally, infrastructure planning is carried out by following manufacturer's specification on EV performance and using low-resolution data from national travel surveys. On the other hand, EV performance significantly degrades in countries like Qatar and other neighbouring Gulf States due to the hot desert climate. Moreover, the lack of public travel surveys require the creation of GPS-based mobility datasets to analyse spatio-temporal EV demand. To that end, this paper presents the first vehicle mobility dataset in the Gulf Cooperation Council (GCC) region by analysing data collected from seven vehicles (six petrol car and one EV) using telematics devices for nine months. The gathered data is processed using machine learning based clustering algorithms to reveal location analysis to examine daily activities, trip patterns, and impacts of weather on fuel efficiency. The results show that the EV driving experience is very risk-adverse due to reduced driving ranges in summer and lack of charging infrastructure. Also, home and workplace charging are well-suited for the recorded population, as the daily trip lengths are within the range of most EV models. We also report significant differences in fuel efficiency between summer and winter driving due to air-conditioning needs. The findings will shed light into GCC region's net-zero transition and decarbonise one of the most carbon-intensive economies of the world.

In this study, a current control strategy is proposed for nine-level packed-E-cell (PEC) rectifier without using current sensor. The proposed control strategy is based on mathematical model of rectifier. A modulation signal is obtained from the differential equation of the system. The modulation signal requires the inductor current reference whose amplitude is obtained by using a proportional-integral (PI) regulator. The input signal to the PI regulator is the dc load voltage error. Hence, while the grid current control is achieved by the proposed control, the regulation of load voltage is achieved by PI. The performance of the proposed control strategy is tested under load variations, reference load voltage change, and grid voltage variation. Moreover, the robustness of the proposed control method under inductance variations is also investigated. Computer simulations are conducted to show the steady-state and dynamic performances of the proposed control method.

Decentralized hierarchical control techniques do not require a communication layer in their secondary control level. These control techniques reduce the vulnerability of microgrids (MGs) to cyber-attacks, reduce data losses, lessen time delays, and mitigate costs for communication infrastructure. In the islanded AC MG, droop control is profusely used at the primary level to achieve accurate power-sharing. However, it may result in steady-state voltage and frequency (V/f) deviations with varying load conditions. The secondary control layer is required to restore these deviations while maintaining droop dictated power levels. The secondary layer supports V/f control at a slower time scale, while the primary droop-based layer supports V/f tracking at a faster time scale. This paper presents a review of the decentralized secondary control systems that interact with droop based primary controllers. The V/f restoration schemes by the secondary control layer are categorized as linear and non-linear technologies. The mathematical formulations for each control scheme are presented, and a tabulated summary of all the control systems. Finally, simulations are conducted for a selected control technique from each category, and the results are presented and discussed.

In this paper, a sliding mode control (SMC) strategy is proposed for a three-phase two-leg grid-connected T-type quasi-Z-source inverter with LCL filter. The large dc input voltage requirement of three-phase two-leg inverter has been resolved by employing a quasi-Z-source network. The proposed SMC can achieve the control of both dc- and ac-side variables. Furthermore, the proposed SMC does not require additional active damping to remove the resonance existing due the LCL filter. The chattering is eliminated by smoothing the sliding surface functions through boundary layers. The validity of the proposed SMC strategy is investigated by computer simulations. It is shown that the dc capacitor voltages are regulated and grid currents track their references.

This paper presents a weighting factor free Lyapunov-function-based model predictive control (MPC) for single-phase T-type rectifiers. The stability of the rectifier can be guaranteed if the derivative of Lyapunov function is negative. Motivated from this fact, the design of cost function (Lyapunov function) is based on stability of the rectifier. Since the coefficient used in the formulation of the Lyapunov function does not have any effect on the performance, the cost function becomes weighting factor free. The weighting factor free based MPC brings simplicity in the practical implementation. The performance of the proposed control technique has been investigated under normal and distorted grid voltage conditions during steady-state and transients caused by the load change. In all cases, the output and capacitor voltages are regulated at desired voltage levels, grid current tracks its reference and achieves unity power factor operation.

Electrical power systems with high solar generation experience a phenomena called "duck curve" which require conventional power generators to quickly ramp-up their output, thus resulting in financial losses. In this paper, we propose an online model (OLM) for scheduling the charging of electric vehicles (EV) located at park-and-ride facilities for flattening solar "duck curves". This model provides a significant improvement to existing ones for similar systems in the sense that the availability of information is related to the time period for which the optimization is done. In addition, a procedure for finding the schedules for EV charging that significantly decreases the ramping requirements is introduced. Proposed procedure includes a combination of a heuristic function and a neural network (NN) to make a decision on which EVs will be charged at each time period. The training of the NN is done based on optimal solutions for problem instances corresponding to the full information model (FIM). The computational experiments have been performed for instances reflecting different levels of solar generation and EV adoptions and prove highly promising. They show that the OLM manages to find schedules of similar quality as the FIM, while having some more desirable properties.

The special section presents the recent advances on sliding mode control and its applications in modern industrial systems. With the increased utilization of modern equipment in wide industry applications such as renewable energy systems, distributed generation, smart grid, transportation, robot manipulators, power systems and automotive, emphasis on better performance in terms of the robustness, optimization, reliability and implementation simplicity has become an important requirement. Sliding mode control is recognized as one of the popular and powerful tools in achieving these requirements in industrial systems. The robustness feature eliminates the burden of the necessity of system parameters required for accurate modelling in most applications. In spite of these attractive advantages, the sliding mode control method suffers from chattering existing due to unmodelled dynamics and switching time delays. The effort of the researchers and industry has led to a rapid development of different sliding mode control methods in terms of sliding surface design, sliding surface coefficient selection, sliding-mode observers, chattering reduction methods, and modulation techniques. Especially, the observer and disturbance estimation techniques are widely studied and substantial new observation and estimation techniques have been proposed in the literature. Hence, the objective of this special section is to share the new ideas of researchers and industry on sliding mode control and its applications in modern industrial systems. The summaries of thirteen papers accepted for publication are given below.

In this paper, a sliding mode control strategy is proposed for single-phase grid-connected three-level neutral-point-clamped quasi-z-source inverters with constant switching frequency. While dc-side variables are controlled by using simple boost control, ac-side variables are controlled via SMC method. The sliding surface function is passed through a boundary layer so as to eliminate chattering and achieve a continuous modulating signal. The switching signals are then obtained by using traditional sinusoidal pulse width modulation which leads to constant switching frequency. The proposed SMC strategy offers many advantages such as fast dynamic response, zero grid current error, simple implementation, robustness to parameter variations and constant switching frequency. In addition, it does not need an active damping method for damping the oscillations arising due the LCL filter resonance. The effectiveness of the proposed strategy is verified by computer simulations under steady-state and transient conditions.

This paper presents a sliding mode control (SMC) strategy for three-phase three-level T-type rectifiers with capacitor voltage imbalance compensation capability. The proposed SMC strategy which is formulated in the natural frame is based on generating a modulating signal from the difference of line currents and their references. The amplitude of line current references is generated by regulating the output voltage using a proportional-integral (PI) regulator. In order to satisfy the unity power factor requirement, the generated amplitude is multiplied by the corresponding unity sinusoidal waveforms for each phase. For the sake of eliminating the imbalance in the capacitor voltages, a compensation term involving the capacitor voltage feedback through a suitable gain is added into the line current reference obtained for each leg. The performance of the proposed control strategy is investigated by simulation study during steady-state, transients caused by load change, and unbalanced grid. It is shown that the dc output voltage is regulated at desired level and line currents track their references in all cases.

This paper introduces a proportional-integral (PI) and sliding mode based control strategy for three-phase grid-tied three-level T-type quasi-Z-source inverter with LCL filter. The proposed control strategy has two parts. While the PI controller regulates the dc capacitor voltages and inductor currents in the quasi-Z-source network, the sliding mode control (SMC) achieves the grid current control. The proposed SMC removes the filter capacitor voltage requirement of phase c leading to simplicity in the implementation. In addition, for the sake of eliminating chattering, the sliding surface functions are passed through a boundary layer (smoothing) which removes the discontinuity and leads to fixed switching frequency operation. The validity of the presented control strategy is investigated by computer simulations during steady-state and transients caused by the reference change. It is shown that the dc capacitor voltage regulation is achieved and grid currents track their references.

In this paper, a sliding mode control (SMC) strategy is proposed for three-phase three-level T-type shunt active power filters (SAPF). The proposed control strategy has the ability to balance the capacitor voltages with respect to the neutral-point. The proposed SMC strategy is formulated in the natural frame which eliminates abc/dq transformation and two PI controllers compared to the design in the dq frame. In natural frame, only one PI controller is needed to generate the amplitude of grid current reference. The output of the PI controller is multiplied by the unity sinusoidal waveforms, obtained from the grid voltages, so as to obtain the grid current references. The filter current references are obtained by subtracting the measured load currents from grid current references. The performance of the proposed control method is investigated by simulation study during steady-state and transients caused by load change. It is shown that the grid currents are almost sinusoidal with small THD, grid currents and dc-link voltage track their references and capacitor voltages are balanced with respect to the neutral-point.

This paper presents a Lyapunov stability theory-based control technique for a single-phase quasi-impedance source neutral-point-clamped (qZS-NPC) inverter with LCL filter. The proposed control technique handles two control objectives, which are the grid current control and the dc-link voltage control. The dynamic and steady-state performances of the proposed control method have been demonstrated by simulations using Matlab/Simulink.

A high-frequency (HF) ac linked universal converter is proposed for distributed generations interfacing three-phase ac with single-phase ac or dc. The primary and secondary of the HF isolation transformer are, respectively, formed by a three-to-single-phase matrix converter and a cyclo converter. Based on the operating principle, a general and simple to be implemented space vector modulation (SVM) is developed from that for the conventional indirect matrix converter and constantly operated virtual dual-active-bridge converter. Simulation studies carried out on different amplitudes and frequencies of single-phase ac output and dc output demonstrate effectiveness and simplicity of the proposed converter with the SVM solution.

This paper presents a comprehensive overview of power converters and their control techniques for AC microgrids. The aim is to give an insight and direction for researchers and applications on promising topologies, control, and application within future smart grid. The paper first focuses on presenting various power converter topologies used in AC microgrids. Then, suitable control techniques for grid-connected and islanded operation modes are critically reviewed and analyzed. Finally, challenges of these converter topologies and control techniques are identified along with their potential solutions.

Two control strategies, a finite-state predictive torque control and a linear control with space-vector PWM, are developed and experimentally assessed in this paper. Both strategies are used to control a permanent magnet synchronous motor supplied by a three-level neutral-point-clamped inverter. The high complexity of the drive system and the different nature of the control objectives make the development and implementation of the two strategies a challenge. In addition to the high computation resources that are required, the large number of the inverter voltage vectors further increases the computation load on the processor. This hinders the implementation into conventional hardware means and necessitates the use of advanced controller boards. Therefore, when controlling drives with multilevel inverters, the development of both control techniques requires special care. In this study, the operation of the two controllers is experimentally investigated, and their performances are assessed by considering several parameters under steady state and transient conditions.

A control strategy based on finite-state model predictive control (FS-MPC) for uniform switching transitions distribution and reduced calculation requirements is presented in this paper. FS-MPC evaluates all the available switching states of the controlled topology to find the combination that meets the control objectives. However, the number of switches, along with the computation burden, is significantly increased in multilevel inverters, whereas the switching transitions are not evenly distributed among the switches. By preselecting the evaluated switching states into the cost function of the developed MPC, it is possible to minimize the computational requirements while decreasing the difference in the switching transitions. The performance of the proposed strategy is compared with the classical predictive control and a field-oriented control with space-vector modulation. The methods are applied to an electric drive with a PMSM supplied by a neutral-point-clamped inverter, and the effectiveness of the developed switching-state strategy is validated by examining steady state and transient performances.

This paper describes the application of a simplified indirect model predictive current control strategy for a direct Matrix Converter. The direct matrix converter has a large number of available switching states which implies that the implementation of predictive control high computational cost. In this paper, a predictive current control strategy is proposed in order to simplify the computational cost while avoiding the use of weighting factors. The method is based on the fictitious dc-link concept, which has been used in the past for the classical modulation and control techniques of the direct matrix converter. The proposed controller is enhanced with a fixed switching predictive strategy in order to improve the performance of the full system. Simulated results confirm the feasibility of the proposed controller demonstrating that it is an alternative to classical predictive control strategies for the direct matrix converter.

This paper presents a model predictive control (MPC) of five-level H-bridge neutral-point-clamped (NPC) quasi-impedance source inverter (qZSI). The proposed control technique is designed to handle three control objectives with simple and effective approach. The output current, the input current and the capacitor voltage are the control objectives of the proposed MPC algorithm. To fulfill these control objectives, multi-objective based cost function is employed. Furthermore, qZSI has been combined with the 5-Level H-bridge NPC inverter so as to obtain power converter topology with buck/boost and dc/ac conversion functionality in a single stage for high power PV applications. Simulation results verify the proposed multi-objective MPC algorithm and inverter topology.

This paper presents a grid-connected five-level neutral-point-clamped (NPC)/H-Bridge quasi-impedance source inverter (qZSI) and its control scheme for use in PV applications. The proposed topology is combination of a three-phase qZS network and NPC/H-Bridge inverter that is suitable for high power applications. The proposed inverter transfers dc power into the ac power without dc/dc boost converter. To control the proposed topology, PI based control scheme is developed. A modified level shifted sinusoidal pulse width modulation (LS-SPWM) technique is employed to generate gate signals for inverter. The effective and simple control algorithm is also employed to regulate the inverter output current. The performance of the proposed system has been validated by simulation results. These results show excellent performance in terms of steady-state and dynamic response.

In this paper, the control of a single-phase cascaded H-bridge neutral-point clamped inverter is investigated. The grid-tied inverter is employed to interface Photovoltaic (PV) panels to the utility grid. A MPC and a classical controller are presented and their effectiveness is illustrated by being compared in terms of several quantitative and qualitative performance indices. The computation burden of the microprocessor, dynamic response and THD compliance with the international standards are some of the considered quantitative characteristics. On the other hand, ease of control development, adaptability to different operating conditions, neutral-point potential balancing along with independent control of each inverter cell are some of the qualitative indices to be investigated. The performances of both controllers are validated by extracting power from two PV strings.

This paper presents a model predictive control (MPC) scheme for quasi-Z source (qZS) three-phase four-leg inverters. In order to cope with drawbacks of the traditional voltage source inverters (VSI)s, the qZS three-phase four-leg inverter topology is proposed in this study. To improve control capability of the controller, the MPC scheme is used. The proposed MPC is based mainly on the discrete-time model of the whole system. The proposed controller handles each phase current independently. As a result of this, the proposed qZS four-leg inverter has fault tolerant capability, for example if one leg fails the others can work normally. Simulation studies were performed to verify the steady-state and transient-state performances of the proposed control strategy under balanced/unbalanced reference currents and load conditions.

This paper presents a model predictive control technique for standalone doubly fed induction generators (DFIGs) without using position sensor. The technique uses a discrete-time model of the system to predict the future value of the rotor current for all possible voltage vectors generated by the rotor side converter (RSC). In this study, due to computational simplicity, the absolute error is selected as a quality function. Also, rotor position phase locked loop (RP-PLL) algorithm is used to achieve sensorless operation. The proposed MPC with RP-PLL sensorless algorithm is designed and simulated in Matlab &Simulink. Simulation results, including constant speed and load changes, and also variable rotor speed and constant load, are presented. The simulation results have proven excellent performance of the proposed MPC with RP-PLL sensorless algorithm, both of load and speed changes conditions.