To reduce the carbon footprint, electric vehicles (EVs) are considered an alternative transportation choice. However, increased use of EVs could lead to overloading the existing power network when accounting for all installed chargers. With the increasing deployment of EV chargers, universities are potential locations for the oversized power network issue. This paper applies reinforcement learning (RL) to optimize for EV charging infrastructure at the university scale using real-world data, directly contributing to sustainable energy management by reducing grid burden and increasing renewable energy utilization. The RL-based charger aims to reduce the burden on the grid while increasing renewable energy utilization. This study investigated practical relevance in real-world systems, considering three demand scenarios: random, stochastic historical demand from Qatar University, and actual online data from Caltech University. Three RL algorithms-Deep Q-Network (DQN), Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO)-are applied. While training, the historical stochastic data requires more tuning of the RL framework than the random demand, emphasizing the importance of realistic demand profiles. The performance of the RL approach depends on the type of demand. The results show that the proposed RL approach can efficiently mitigate the peak charging currents. For the Qatar University historical demand scenario, the PPO algorithm minimized the peak charging currents by 50% relative to uncontrolled charging (160 A to 80 A) and Model Predictive Control maintained the energy transfer capability at 99.710%. For the random demand type, the peak charging currents are minimized by 38.3% as compared to uncontrolled charging (128 A to 79 A), with a nominal reduction in energy transfer capability to 95.89%. Scalability is tested by integrating the model into the IEEE-33 bus network. Without solar integration, the proposed RL-based EV charging management model improves the voltage drop by 0.05 p.u., leading to reduction in the line losses by 17% as compared to the MPC benchmark method and by 32% as compared to the uncontrolled charging scheme. Further, the proposed RL approach leads to a 9% reduction in line current during peak hours in the IEEE-33 bus system. With solar integration into the IEEE-bus system, the proposed framework of the RL approach improved the sustainability of the charging infrastructures by enhancing solar energy utilization by 42.5%. These findings validate the applicability of the proposed model used for optimizing the sustainable EV charging infrastructure while managing the charging coordination problem.

Given the global drive toward sustainable agricultural practices, semi-transparent photovoltaic (STPV) technology offers a dual benefit of generating renewable energy while still permitting a portion of sunlight essential for plant growth. Unlike traditional photovoltaic installations limited to roof surfaces, this work investigates the innovative use of STPV panels on vertical wall surfaces to maximize solar harvesting. By conducting an hourly irradiance analysis for a full calendar year, we evaluated the solar energy potential of different greenhouse sections (roof and walls) in Qatar's climatic conditions. The results reveal a significant contribution from wall-mounted STPV installations, which generated 83.77% of the total annual energy compared to roof-mounted systems. Among the walls, the East Wall (EW) contributed consistently, achieving an annual average of 0.35 kWh/m², while the South Wall (SW) and West Wall (WW) also provided meaningful outputs of 0.19 kWh/m² and 0.22 kWh/m² respectively. In contrast, the North Roof (NR) and North Wall (NW) sections demonstrated the lowest energy outputs, with annual averages of 0.01 kWh/m² and 0.05 kWh/m², underscoring limited solar access due to their orientation. Sensitivity analysis further indicated that panel efficiency plays a crucial role in energy generation, with potential production reaching 18,904 kWh annually at a 20% efficiency rate, significantly higher than the baseline 7% efficiency considered in this study.

In this paper, a model predictive based minimum DC-link voltage control (MP-mDVC) method is proposed to enhance both the reliability and dynamic performance of grid-connected converters (GSCs). Unlike conventional voltage oriented control (VOC) methods, which typically regulate the DC-link voltage at a fixed rated value, the minimum DC-link voltage (mDVC) method introduced in the literature dynamically adjusts the DC-link voltage reference to the minimum required level based on the system's operating conditions. While the mDVC strategy can effectively enhance reliability, it may also degrade the dynamic performance of the grid-side converter. Reducing the DC-link voltage to alleviate voltage stress on components limits the available stored energy, which in turn slows the converter's response to operating changes and makes the system more susceptible to instability. To address this, the proposed method leverages the dynamic characteristics of upstream controllers in a cascaded VOC structure and introduces a predictive strategy in the outer loop to predict reference current changes and accordingly adjust the DC-link voltage setpoint. Furthermore, the inner current controller is replaced with a finite-set model predictive direct current control (MPDCC) method to eliminate the response lag associated with the current control loop. A discrete space vector modulation (DSVM) technique is also incorporated into the MPDCC to ensure constant switching frequency and maintain the quality of the grid-side current. The effectiveness of the proposed strategy is demonstrated through extensive simulations and validated via experimental testing. The results confirm that the method not only minimizes the DC-link voltage to enhance component reliability but also improves the dynamic performance of the converter.

With the significant growth in the industry of energy storage units (ESUs) and plug-in electric vehicles, charging coordination becomes critical to avoid electric grid overload. However, without effective methods to secure the charging requests, malicious users can eavesdrop on the communication between the ESUs and the grid to extract sensitive users’ information. Therefore, privacy preservation is a must in the design of charging coordination schemes. To address this critical issue, in this paper, we propose a privacy-preserving charging coordination scheme that allows the aggregator to allocate optimized amounts of charging power to all available ESUs in the community without knowing their individual charging requests details. In particular, based on aggregated secret key communication, best-effort, and cooperative power allocation schemes, the proposed coordination scheme totally hides the sensitive data from all the nodes in the network and performs the power allocation in a semi-blind fashion. Using extensive simulations, we show that our proposed scheme can achieve almost the same performance as that of charging coordination schemes in the literature based on full knowledge of the charging requests while outperforming them by an enhanced level of security. In particular, numerical results confirm that the proposed scheme can allocate 95% of what traditional schemes allocate while keeping ESU information private. Despite the anonymity and collaborative nature of the algorithm, the average convergence time is around 2.7 iterations, with 50% to 100% of cases converging in one iteration.

This paper presents a new expendable boost DC-DC converter design with continuous input and output currents. The voltage gain and voltage-current stresses on the semiconductors are the same as those in the conventional boost converter. The proposed converter has lower conduction loss in the inductors compared to the traditional boost converter, resulting in greater efficiency across all duty cycle ranges and under identical conditions. Additionally, the maximum voltage gain in the new topology surpasses that of the conventional boost converter. The continuity of the output current eliminates the non-minimum phase behavior of the traditional topology of boost, making it faster than the conventional boost converter. A new family of converters is derived from the proposed topology. These newly suggested topologies have higher gains than the basic topology. The paper analyzes the proposed converter in both ideal and non-ideal modes, outlines the requirements for continuous conduction mode (CCM) operation, and investigates non-ideal voltage gain and efficiency sensitivity. The potential extensions of the proposed topology are discussed. Notably, all these extensions have a higher voltage gain while retaining all the advantages of the proposed primary topology. Simulation and experimental tests are carried out for the main topology. Moreover, the voltage gain tests based on experimental results are discussed for the extensions of the main topology and compared with their corresponding theoretical predictions. The results confirm the feasibility of the proposed converter.

In a DC Microgrid, to minimise the effect of unequal cable resistances, distributed secondary controllers are included in addition to droop controllers. An accurate Current Sharing based Secondary Controller (ACSSC) is proposed in this paper, which is used to ensure proportional or accurate sharing of power demanded by loads among sources in a DC microgrid. However, ACSSC is a model approach and imposes a computational burden on digital signal processors (DSPs) in the case of a DC microgrid having a large number of sources and loads. To address this issue, a distributed Artificial Neural Network-based ACSSC (ANN-ACSSC) is proposed, which includes a learning framework of the accurate current estimator, droop gain estimator and voltage estimator to ensure accurate current sharing in the DC microgrid and maintain the voltage regulation across the converter within the specified limit. The performance of ANN-based estimators is compared to the state-of-the-art machine learning-based secondary controllers like bagged ensemble, Reinforcement-learning-based Integrated Control (RLIC) and Reinforcement Learning-based Approximate Dynamic Programming (RLADP-based estimators using performance metrics like MAE, MSE and R-2 score. The inference time of various estimators included in ACSSC is evaluated using the 'tic-toc' functionality included in Matlab. The Levenberg-Marquardt algorithm is used for training the various estimators included in ANN-ACSSC. The performance of the proposed ANN-ACSSC is compared with that of the proposed ACSSC. Further, the performances of ANN-ACSSC and ACSSC for a nonzero value of communication delay are studied. The viability of the ANN-ACSSC is validated using the experimental results captured using a lab prototype of a DC microgrid.

The spread of electric vehicles (EVs) and the increasing integration of renewable energy sources have emerged as driving forces behind the development of smart grid-interactive systems. This review paper explores the role of artificial intelligence (AI) techniques in enhancing the Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) functionalities by focusing on smart charging/discharging coordination, energy management systems, and grid services. Machine learning (ML), deep learning (DL), reinforcement learning (RL), and multi-agent systems (MAS) are AI techniques relevant to predictive control, optimization, and privacy-aware scheduling. This review summarizes recent advancements in the field, identifies research challenges, and highlights emerging opportunities in AI-enabled bidirectional EV-grid interactions.

The growing popularity of electric vehicles (EVs) has accelerated the demand for fast-charging systems with high efficiency and the ability to handle battery stress efficiently and deliver the maximum energy transfer. The paper outlines the design and analysis of a Dual Active Bridge (DAB) converter, which is operated in the Dual Phase Shift (DPS) modulation method to achieve a 3-stage Multi-Stage Constant Current (MSCC) charging profile. The proposed system couples the DAB directly with the EV battery, bypassing intermediate gridtied stages. The 3 stages of MSCC operating at different charging currents, where it operates on high (2C), medium (1.5C), and low (1C) charging currents are adapted, depending on the battery State of Charge (SoC) bands sensed, to create a balance between the fast charging, thermal management, and long battery life. The design of control design is created to control the power flow across charging stages and ensure softswitching and minimal RMS current stress. The THD analysis found to be 4.53%, validating the effectiveness of the DPScontrolled architecture. The simulation confirms that the DPSbased DAB has high efficiency, current and behavior stability, and efficient shaping at each stage, and it is a proper architecture when applied to scalable EV fast-charging systems.

A novel seven-level switched-capacitor inverter (7L-SCI) topology has been proposed in this article, which is being applied to a grid-connected photovoltaic (PV) system with dc superconducting cable. The proposed topology has the additional benefit of inherent fault tolerance (FT) to the switch faults. A comparative analysis is provided between the proposed topology and different seven-level MLI topologies. Additionally, dc superconducting cable is utilized for its energy storage properties to smooth out fluctuations in PV power. A superconducting cable can conduct high-speed charges and discharges, allowing the cable to deal with output PV power fluctuations that are difficult to control using conventional technologies. The application of the superconducting cable reduces the spikes in the inverter's source current which is generally a drawback of using a switched capacitor multilevel inverter (MLI). Through the use of a laboratory prototype, the performance of the presented topology is examined. Finally, simulation results of MATLAB/Simulink are presented to show the grid-connected system's performance with and without superconducting cables.

Greenhouses provide controlled environments for crop cultivation, and integrating semi transparent photovoltaic (STPV) panels offers the dual benefits of generating renewable energy while facilitating natural light penetration for photosynthesis. This study conducts a feasibility analysis of integrating Battery Energy Storage Systems (BESSs) with STPV systems in greenhouse agriculture, considering the Daily Light Integral (DLI) requirement for different crops as the primary constraint. Employing an enhanced Firefly Algorithm (FA) to optimize the PV cover ratio and BESS capacity, the analysis aims to maximize the Net Present Value (NPV) over a 25-year period, serving as the primary economic parameter. By incorporating DLI requirements for various crop types, the study ensures optimal crop growth while maximizing electricity generation. To ensure realistic long-term projections, the analysis incorporates BESS degradation over the 25-year period, accounting for capacity loss and efficiency reduction in energy storage. The results reveal the significant impact of crop type, with various required DLI , and transparency factor on optimized BESS and consequently the NPV of the project. Simulation results show that for crops with high DLI requirements, the feasible range of PVR% in the greenhouse varies from 42 % to 91 %, depending on the STPV's transmittance factor. Additionally, the study reveals that initial negative revenue is common across all cases, with the highest NPV achieved at $1,331,340 for crops with low DLI requirements and a BESS capacity of 216 kW.

The integration of battery storage based electric vehicle (EV) chargers in the distribution network may lead to various power quality issues like voltage fluctuation, production of harmonics in line current, and phase imbalance. The production of harmonics produces various undesirable effects in the distribution system like deterioration of battery health, accelerated ageing of the battery, power losses in the distribution system, and affecting the hosting capacity to accommodate EV chargers in the distribution system. The harmonic issues become more severe with an increase in the number of battery storage- based EV chargers included in the system. In the worst case, the generation of harmonics may lead to harmonic instability. In this paper, the various converter topologies either uncontrolled or uncontrolled and used in on-board and off-board EV chargers are discussed in detail and the quantitative analysis for THD contributed by these topologies at various power levels is also included. The causes of harmonics, the quantitative assessment and the limits of harmonics in practical circuits are discussed. Further, the harmonics leads to deterioration in hosting capacity of electrical network to host EVs. The methods used to evaluate the hosting capacity of the system including the THD profile of grid current and the voltage at the point of common coupling (PCC) are discussed in detail. The quantitative analysis for propagation of harmonics in a distribution system caused due to EVs connected across EV charging stations (EVCSs) is also discussed. The effect of battery charging profiles of EV chargers on the THD profile of the grid current of the distribution system is highlighted. Further, the state-of-the art techniques used to evaluate harmonic propagation in power system including multiple EVCSs and nonlinear loads are included. The state-of-the-art techniques used to detect and compensate harmonics in power system network are included and their respective advantages, limitations and THD contributed these techniques at various power levels are highlighted. The various services provided by the pool of energy storage created due to distributed connection of EVs at various locations in distribution system is discussed in detail. The effects of harmonics on various elements of the power system and possible solutions to minimize the harmonic impacts are discussed. Further, the techniques that improve the power quality of the system are discussed as the scope of future work.

This paper presents a Dual Active Bridge (DAB) converter-based design and control solution to a bidirectional electric vehicle (EV) charging system. The system has the potential for effective transmission and transfer of the power back and forth between the vehicle and the grid in the Grid-to-Vehicle (G2V) or the Vehicle-to-Grid (V2G) by applying the phase-shift modulation. The automated G2V to V2G change at $\mathbf{8 0 \%}$ State of Charge (SOC) is a major hallmark of the control algorithm since it maintains the safety of the battery by avoiding overcharge of batteries, and it allows feedback of energy to the grid. Simulation results confirm precise phase-shift regulation, smooth current transitions, and bidirectional power control. Furthermore, the system complies with IEEE 519 harmonics requirements, having the grid side current total harmonic distortion (THD) less than 5% in total. The architecture offers EV charging infrastructure that complies with the grid and is safe, efficient, and future-friendly with V2G capability to support smart grid applications and demand-side energy management.

This article proposes a family of modified quadratic boost converter (QBC) topologies designed to alleviate voltage stress on the output capacitor while achieving higher voltage gains compared to conventional converters. The output voltage is synthesized from the combined voltages of two capacitors, effectively mitigating stress on the output capacitor. Additionally, the voltage of the source contributes to the formation of the output voltage. Ten topologies are presented, with nine demonstrating enhanced voltage gain relative to the QBC topology. The voltage gains of all proposed topologies are analyzed. However, only the primary topology of the converter family is examined in detail and confirmed through experimentation. The main topology is evaluated under both ideal and non-ideal operating conditions, including sensitivity analyses of voltage gain and efficiency. An 80 W prototype is constructed, and practical implementation validate the introduced family theory and objectives. The primary converter's efficiency exceeds 95 % for a rated power of 80 W.

Linear switched reluctance motors (LSRMs) are highly popular due to their simple and robust structure, affordable pricing, and ability to operate at high efficiency. However, a major drawback of these motors is the high ripple force. In this paper, a novel structure for a linear motor with an exceptionally lightweight rotor and a stator with separate poles is presented, making it suitable for use in electric train systems. The proposed system not only significantly reduces force fluctuations but also enables the distribution of force along the length of the moving vehicle, ensuring smooth motion. Moreover, the reliability of the system is enhanced, as the LSRM can continue its motion even in the presence of a fault in one of the phases. In applications with high speeds, the use of a fast controller seems essential, but conventional fast regulators often lack sufficient precision. To address this issue, a new control system is introduced, utilizing a new 12-vector voltage switching table and a current controller. Unlike conventional control methods, the phases firing angle in the proposed system is not fixed. As a result, acceptable performance at different speeds is achieved through an adaptive real-time turn-on position control.

Transformerless inverters are cost-effective and power-effective but require a high-gain step-up DC-DC converter for solar energy applications. On the other hand, using transformerless microinverters can lead to safety issues due to the parasitic capacitance of the solar panel, which causes leakage current that leads to performance problems. This paper introduces a new model predictive control (MPC) to mitigate PV leakage current. The proposed controller is designed to control a two-stage transformerless grid-connected PV system with PV parasitic capacitances. The control part is divided into three main controllers: 1) the maximum power point tracking (MPPT) perturb and observe (P&O), which will control the Quadratic Boost Converter (QBC), a single switch high gain DC-DC converter; this control part will control the single switch to track the PV maximum power point optimally; 2) A PI controller that controls the DC-Link capacitor and guarantees the power transfer from the PV side to the grid side; 3) the proposed MPC that will control the nine-level crossover switch cell (CSC) inverter to reduce the leakage current across the parasitic capacitance while maintaining the unity power factor, low grid current Total Harmonic Distortion (THD), and maintaining fixed voltage for the CSC flying capacitor. A simulation was conducted to verify the performance of the proposed controller and ensure its effectiveness. A grid current THD of less than 5% is achieved, and a stable operation is validated. Additionally, leakage current mitigation from 0.35 to 0.18 A(rms) is achieved.

This paper proposes a novel high-gain DC-DC converter that combines a boost converter with various voltage multiplier cells. This non-isolated topology can be optimized to achieve higher voltage gains, which is crucial for applications such as pulse power water electrolyzers, water refineries, and high-voltage testing. Both ideal and non-ideal analyses have been conducted, and the requirements for the converter’s operation in continuous conduction mode are discussed. A key advantage of the proposed topology is its capability to be extended for even higher voltage gains compared to recently suggested topologies. To verify and validate the operating principle of the proposed topology, experimental results from a 200-W prototype are presented.

Ahstract-New approaches must be taken due to the rapid growth of societies globally and the urgent need for more energy, climate change, and global warming. One of these approaches is supporting and expanding new and efficient renewable energy resources (RERs). Therefore, this paper is dedicated to the energy planning and management of the proposed structure in which novel renewable-based technologies such as INVELOX wind turbines, Bi-facial photovoltaic (PV) panels, and tidal turbines (TT), in addition to the demand response programs (DRP) especially time of use (TOU), are investigated. The purpose is to simultaneously reducing the presented system's overall cost and environmental pollution. The problem is modeled as mixed-integer linear programming (MILP) in GAMS software and solved using a CPLEX solver. The final results show that novel technologies recognized as efficient can significantly reduce contamination, expenses, and greenhouse gas (GHG) emissions while supplying our rapidly growing demand.

Linear motors have garnered significant attention in various industrial applications due to their simple structure and capability for direct motion. However, traditional designs of linear motors often face challenges in achieving optimal system performance. This paper introduces a modern structural framework that optimizes force distribution in a segmented linear switched reluctance motor, addressing some of the limitations associated with traditional designs. These types of motors are the most cost-effective electric motors that have gained considerable attention in recent years. The proposed structure leads to improved force distribution and force quality while significantly reducing force ripple effects, resulting in smoother motion. With the enhanced quality of propulsion force and a considerable reduction in force ripple, the proposed structure can be highly beneficial in industrial systems such as electric trains and elevators, and it can compete with other conventional electric motors. The proposed system not only achieves optimal force distribution but also enhances system reliability, allowing the motor to continue functioning in the presence of a fault in one of its phases. Simulation and experimental results confirm the superior performance of the proposed system compared to conventional systems.

A reinforcement learning (RL) based EV charging management system is developed for the charger coordination problem. RL can handle system uncertainties, requires no historical data, and is not affected by future changes to the system. The EV charging coordination reduced peak demand, required charging infrastructure and increased solar power utilization. The proximal policy optimization (PPO) is used to train the RL to reach targets and the results are compared with other benchmark algorithms.