Bidirectional Power Flow Research Articles

Optimizing Power Quality in EV Chargers Using Advanced Quadrature Signal Generators and AI‐Driven Adaptive Filtering

ABSTRACTAs electric vehicle (EV) adoption grows, vehicle‐to‐grid (V2G) technology enables bidirectional power flow in grid‐interactive EV chargers. However, maintaining consistent power quality under nonideal grid conditions remains a challenge. Traditional PI controllers struggle to reduce total harmonic distortion (THD) and adjust to dynamic grid variations. This study explores machine learning techniques, including decision trees, artificial neural networks (ANN), and linear regression, as alternatives to conventional PI controllers. Decision trees emerge as the most advantageous due to their simplicity, interpretability, and ability to handle complex, nonlinear relationships with minimal data preprocessing. While ANN captures intricate patterns, it demands more computational resources and lacks transparency. Linear regression, though efficient, struggles with complex grid behaviors. The decision tree approach allows real‐time adaptive control, improving THD reduction and grid stability. Additionally, a CNISOGI filter is implemented to enhance harmonic attenuation and DC‐offset rejection. The system's effectiveness is validated through Matlab/Simulink simulations and a 1.1 kW hardware prototype. The results show that integrating decision tree‐based controllers with advanced filtering techniques can significantly enhance power quality, grid stability, and operational efficiency in future smart grids.

Improved Controller for Interlinking Converter in Hybrid AC/DC Microgrids

The AC/DC hybrid microgrids are a feasible solution to provide both AC and DC power to electrical equipment. However, the control problem to maintain voltage and frequency stability in AC/DC hybrid microgrids is essential. This paper proposes a control method for the converter to maintain voltage and frequency stability for AC/DC hybrid microgrids. The power converter will operate bidirectionally to transfer power back and forth between AC and DC subgrids in the AC/DC hybrid microgrid operating in standalone mode. The proposed control method not only controls the bidirectional power flow between AC and DC subgrids to stabilize voltage and frequency as well as balance active power and reactive power. In addition, the proposed method can restore voltage and frequency for the microgrid in the event of sudden load surges or power failures in AC and DC subgrids. This method is established based on the relationship between active power and frequency, because active power is present in both AC and DC subgrids, while frequency is only present in AC subgrid. However, in AC subgrid, frequency depends on active power, while in DC subgrid, DC bus voltage depends on active power. Therefore, the proposed method is designed based on adaptive frequency shifting to adjust the power flow exchange between the two subgrids in order to maintain the stability of the frequency and voltage of the buses. The suitability and feasibility of the method are demonstrated by simulating the AC/DC hybrid microgrid using Matlab/simulink software.

Optimization of Active Distribution Network Operation with SOP Considering Reverse Power Flow

As the penetration of distributed renewable energy increases, the phenomenon of bidirectional power flow in distribution networks becomes increasingly severe. Traditional regulation devices like OLTC (on-load tap changer) and CB (capacitor bank) cannot effectively mitigate reverse power flow in distribution networks due to their limitations. The transmission capacity of the distribution network under reverse power flow is approximately 50% of the rated capacity of the OLTC, leading to issues such as voltage limit violations and high wind and solar curtailment rates. This paper proposes a method for calculating the reverse power flow delivery capacity of distribution networks, quantitatively describing the distribution network’s delivery limits for reverse power flow. Based on this, a joint optimization model for multiple distribution networks with an SOP is established. The SOP is utilized to share reverse power flow delivery capacity among multiple distribution networks, enhancing operational economy and increasing the accommodation of the DG. Finally, the method’s effectiveness and correctness are verified in the IEEE 33-node system. The results validate that while joint operation does not enhance the reverse flow transmission capacity of a single distribution network, it can, through the shared reverse flow transmission capacity approach, elevate the reverse flow transmission capacity to approximately 70% during the majority of time periods.

Novel Quasi-Z-Source Inverter with High-Frequency AC Link of High-Proportion Renewable-Energy Power System

Z-source/quasi-z-source inverters can make up for some limitations of traditional voltage-/current-source inverters. In recent years, more and more research has been carried on z-source/quasi-z-source inverters, but most of them are unable to realize input/output galvanic isolation. The proposal of high-frequency isolated z-source/quasi-z-source inverters greatly enriches the topological family of this type of converter but places relatively high voltage stress on the capacitors. In this paper, a novel circuit topology of a quasi-z-source inverter with a high-frequency AC link of a new high-proportion power system is proposed. The operating principle and abnormal operating states, such as discontinuous-conduction mode (DCM) operation and abnormal states caused by component failures, are analyzed. The double closed-loop control strategy is analyzed and designed, and a grid-connected photovoltaic system based on the inverter is designed. The experimental results verify that the presented inverter has advantages such as high-frequency electrical isolation, bi-directional power flow, lower voltage stress on the capacitors, etc.

Self-Powered Actively Controlled Lateral Suspensions of High-Speed Trains Using Energy-Regenerative Electromagnetic Damper and Model Predictive Control

Although traditional active suspension can offer superior riding comfort and maneuverability over its semiactive and passive counterparts, its reliance on an external power supply has hindered its widespread applications in vehicles. To overcome this deficiency, this paper proposes an innovative self-powered active suspension design for a high-speed train (HST), by leveraging the recently emerging H-bridge circuit-based electromagnetic damper (HB-EMD), allowing bidirectional power flow between the damper and controlled system. The capability of HB-EBD to achieve unique self-powered active skyhook control was previously proved in a simplified single degree-of-freedom (SDOF) structure under harmonic excitations; however, the feasibility of employing HB-EMD to realize active vibration control for more complex structural systems under stochastic excitations remains an unanswered question. One main challenge is designing a novel control algorithm that can simultaneously realize vibration control and self-powering objectives, which is unattainable by traditional active control algorithms. In this study, an ad hoc model predictive controller (MPC) is designed to guarantee the fulfillment of these dual objectives. To evaluate the performance of the proposed active suspension design, two separate HB-EMDs are implemented on the front and rear sides of the secondary lateral suspensions of an HST model subjected to stochastic track irregularities. At a speed of 200[Formula: see text]km/h, the proposed HB-EMDs with MPC could achieve a 55% reduction in the lateral acceleration of the car body in comparison with passive suspension, meanwhile maintaining energy harvesting performance with an average output power of 25.0[Formula: see text]W. In contrast, a traditional active linear quadratic Gaussian (LQG) controller consumes 72.7[Formula: see text]W power when performing comparable vibration reduction. This study, for the first time, validates the feasibility of designing a self-powered, actively controlled secondary lateral HST suspension system without relying on an external power source, which will potentially pave the way for a new active vibration control paradigm for other generic structures.

A waveform similarity based current-only differential protection technique for inverter interfaced microgrids

Developing a reliable protection system is crucial to facilitate the integration of renewable energy sources (RESs) into modern power grids, paving the way for reaching net-zero carbon emissions. The bidirectional power flow and limited fault current contribution of inverter interfaced RES units make designing an efficient protection system for inverter based microgrids challenging. To address these problems, this paper presents a differential protection technique for smart microgrids where the different behavior of fault current at two ends of a microgrid faulty line is quantified using a short-time correlation transform (STCT) based index. It has promising robustness to measurement noise, fault type, fault location, fault resistance, fault inception angle, communication delay, non-linear load, and microgrid configuration and operating mode with the need for a low sampling frequency. Test results on the Canadian urban benchmark distribution system indicate the viability of the developed STCT based differential protection technique.

An induction machine‐based drive system and supervisory control for application to smart water‐pumped storage and networks

AbstractThe authors present an effective induction machine (IM) drive system and intelligent supervisory control for application to smart water‐pumped storage and networks. The main IM drive unit comprises reduced voltage starter, regenerative clutch/variable speed mode fluid coupling and reversible pump to allow bidirectional power flow. Under the wide umbrella of supervisory control, the following objectives are achieved: (1) Recurrent neural network (RNN) and RNN‐aided Kalman filter are proposed for wired and wireless sensor fault‐tolerant control of the plant based on Wald's likelihood ratio test. (2) Cheap fuzzy logic‐based availability assessment scheme is presented and deployed on cloud to arrange for preventive maintenance of the starter. (3) Robust energy saving mode decision‐making algorithm is developed for best energetic cost reduction. (4) Remote synchronisation method of multi‐motor starting with a mechanically switched capacitor is introduced to minimise voltage dip. Further, an auxiliary inverter fed pump unit is adopted to improve system availability and revenue. The validity and superiority of the proposed drive system and control are confirmed through theoretical analysis, computer simulations, cost study, efficiency evaluation and application example to a single‐pump water network during motor and generator operational modes using preliminary test results.

Development of GaN-Based, 6.6 kW, 450 V, Bi-Directional On-Board Charger with Integrated 1 kW, 12 V Auxiliary DC-DC Converter with High Power Density.

Automotive-grade GaN power switches have recently been made available in the market from a growing number of semiconductor suppliers. The exploitation of this technology enables the development of very efficient power converters operating at much higher switching frequencies with respect to components implemented with silicon power devices. Thus, a new generation of automotive power components with an increased power density is expected to replace silicon-based products in the development of higher-performance electric and hybrid vehicles. 650 V GaN-on-silicon power switches are particularly suitable for the development of 3-7 kW on-board battery chargers (OBCs) for electric cars and motorcycles with a 400 V nominal voltage battery pack. This paper describes the design and implementation of a 6.6 kW OBC for electric vehicles using automotive-grade, 650 V, 25 mΩ, discrete GaN switches. The OBC allows bi-directional power flow, since it is composed of a bridgeless, interleaved, totem-pole PFC AC/DC active front end, followed by a dual active bridge (DAB) DC-DC converter. The OBC can operate from a single-phase 90-264 Vrms AC grid to a 200-450 V high-voltage (HV) battery and also integrates an auxiliary 1 kW DC-DC converter to connect the HV battery to the 12 V battery of the vehicle. The auxiliary DC-DC converter is a center-tapped phase-shifted full-bridge (PSFB) converter with synchronous rectification. At the low-voltage side of the auxiliary converter, 100 V GaN power switches are used. The entire OBC is liquid-cooled. The first prototype of the OBC exhibited a 96% efficiency and 2.2 kW/L power density (including the cooling system) at a 60 °C ambient temperature.

Vector Reconfiguration on a Bidirectional Multilevel LCL-T Resonant Converter

With the development of distributed energy technology, the establishment of the energy internet has become a general trend, and relevant research about the core component, energy router, has also become a hotspot. Therefore, the bidirectional isolated DC–DC converter (BIDC) is widely used in AC–DC–AC energy router systems, because it can flexibly support the DC bus voltage ratio and achieve bidirectional power flow. This paper proposes a novel vector reconfiguration on a bidirectional multilevel LCL-T resonant converter in which an NPC (neutral-point clamped) multilevel structure with a flying capacitor is introduced to form a novel active bridge, and a coupling transformer is specially added into the active bridge to achieve multilevel voltage output under hybrid modulation. In addition, an LCL-T two-port vector analysis is adopted to elaborate bidirectional power flow which can generate some reactive power to realize zero-voltage switching (ZVS) on active bridges to improve the efficiency of the converter. Meanwhile, due to the symmetry of the LCL-T structure, the difficulty of the bidirectional operation analysis of the power flow is reduced. Finally, a simulation study is designed with a rated voltage of 200 V on front and rear input sources which has a rated power of 450 W with an operational efficiency of 93.8%. Then, the feasibility of the proposed converter is verified.

Analysis of multidimensional impacts of electric vehicles penetration in distribution networks

Moving towards a cleaner, greener, and more sustainable future, expanding electric vehicles (EVs) adoption is inevitable. However, uncontrolled charging of EVs, especially with their increased penetration among the utility grid, imposes several negative technical impacts, including grid instability and deteriorated power quality in addition to overloading conditions. Hence, smart and coordinated charging is crucial in EV electrification, where Vehicle-to-Grid (V2G) technology is gaining much interest. Owing to its inherited capability of bi-directional power flow, V2G is capable of enhancing grid stability and resilience, load balancing, and congestion alleviation, as well as supporting renewable energy sources (RESs) integration. However, as with most emerging technologies, there are still technical research gaps that need to be addressed. In addition to these technical impacts, other multidisciplinary factors must be investigated to promote EVs adoption and V2G implementation. This paper provides a detailed demonstration of the technical problems associated with EVs penetration in distribution networks along with quantifiable insights into these limitations and the corresponding mitigation schemes. In addition, it discusses V2G benefits for power systems and consumers, as well as explores their technical barriers and research directions to adequately regulate their services and encourage EV’s owners to its embracement. Moreover, other factors, including regulatory, social, economic and environmental ones that affect EV market penetration are being studied and related challenges are analyzed to draw recommendations that aid market growth.

High gain Bi-directional KY converter for low power EV applications

In electric vehicles (EVs), the type of electric motor and converter technology have a significant impact on regulating the operational characteristics of the vehicle. Therefore, in this work, the modified bi-directional KY converter (BKYC) is proposed for EV applications. The main contributions of the proposed converter are high step-up/step-down conversion gain, bi-directional power flow, simplified control structure, continuous current, common ground, low volume, and high efficiency. An inductor on either side of the converter ensures continuous current flow and passive components are arranged to operate in series to offer high step-up/step-down conversion. The charging and discharging operations, steady-state analysis, and design process of the proposed converter are discussed in detail and compared with similar bi-directional converter topologies. Further, the efficiency analysis of the proposed converter is presented and found that the efficacy of 95.51 % in charging operation and 96.52 % in discharging operation of operation. The simulations are carried out using MATLAB/Simulink environment. Further, a prototype of a modified bi-directional KY converter is implemented with a TMS320F28335 processor and validated with theoretical and simulation counterparts.

Demand management of plug-in electric vehicle charging station considering bidirectional power flow using deep reinforcement learning

The recent development in plug-in electric vehicle technology has increased its popularity. Plug-in electric vehicles are widely used for their environment-friendly nature and they contribute to the reduction in global warming. With the increasing number of plug-in electric vehicles, charging coordination becomes essential for managing the charging station demand. The random charging behaviour of plug-in electric vehicles makes it a difficult task. This paper proposes a novel demand management strategy of charging stations. The proposed strategy supports the grid and reduces its burden during peak load. Deep-Q network based deep reinforcement learning is used in the proposed strategy. It is a value based deep reinforcement learning algorithm that approximates the Q value function using deep neural network. The deep reinforcement schedules the charging and discharging of plug-in electric vehicles to optimize the cost and manage the charging station load. Deep reinforcement learning enhances charging coordination by dynamically optimizing charging schedules according to real-time conditions and user preferences, thereby increasing efficiency and better integration with the grid. The reward function in deep reinforcement learning is designed based on the power price and demand of the charging station. A discount factor is introduced based on the service time to make the charging coordination efficient. A case study using dynamic pricing is carried out to validate the proposed strategy. The results prove that the proposed strategy optimizes charging and discharging costs and manages charging station demand efficiently. It is also observed that the proposed strategy is fast and incurs less computational cost.

Analysis and control of grid-interactive PV-fed BLDC water pumping system with optimized MPPT for DC-DC converter

In this study, a novel water pumping module fed by grid interactive Photo-Voltaic with a bidirectional Power Flow Control was proposed. In addition to improving the pumping system’s reliability, a water pump is powered by a brushless DC motor drive. This method enables the pump to work at its maximum capacity for the entirety of that day, regardless of the weather. The entire system becomes more reliable as a result of the motor’s increased use of photovoltaic (PV) generated power for pumping applications. Maximum Power Point Tracking (MPPT) controller incorporating Machine Learning algorithm drives bridgeless greater static gain DCDC converter to achieve higher power generation point and increment PV efficiency. The PV array’s operation would be managed using the ML back propagation technology to capture the most electricity under any ecological circumstance. A BLDC motor is fed by a Voltage Source Inverter (VSI) that includes a DC bus controlled in both directions by a unit vector template (UVT) approach incorporated in a single-phase voltage source converter (VSC). Additionally, utilizing a PI controller to manage the DC capacitor voltage in the UVT controller at a particular level is not appropriate for the increased PQ capabilities. However, due to tuning problems with the current controller, this controller is unpopular. The aforementioned problems are resolved by employing a unique intelligent-based fuzzy logic controller that achieves good performance features. In this technique, the function of a PV array at its Maximum Power Point (MPP), as well as power quality enhancements and a decrease in Total Harmonic Distortion (THD) of the grid are accomplished. The proposed PI controller attains a significant voltage THD of 3.736. The PI controller, on the other hand, managed to achieve a load voltage THD of 2.629%. The ANFIS method, whose value is 1.739%, is discovered to have a lower THD than all remotes with improved features, it lessens abrupt swings while maintaining steady DC-link voltage.

Demand management and pricing in renewable integrated plug-in electric vehicle charging station using reinforcement learning

ABSTRACT The increasing adaption of Plug-in Electric Vehicles (PEVs) has increased the power demand for charging. Renewable generation can significantly help to meet this demand. The uncertainty of renewable generation and randomness in PEV charging makes the demand management of charging stations challenging. To address these challenges, this study proposes a novel demand management pricing strategy for renewable integrated charging stations. The proposed strategy uses reinforcement learning for charging coordination of PEVs. The renewable generation is estimated using weather data and accordingly PEVs are scheduled for demand management. The strategy decides the PEV power price based on real-time price, renewable tariff and PEV load. The strategy uses the bidirectional power flow with battery degradation cost. The distribution transformer operating cost and loading constraints are included to resemble the real-time environment. The PEV randomness and uncertainty of renewable generations are incorporated in the study. The results of the numerical case study show that the proposed strategy can manage the charging station load efficiently. The proposed strategy has optimized the cost of charging, discharging and charging station profit and increased the service capability of the charging station.

Improving energy efficiency for suburban railways: A two-stage scheduling optimization in a rail-EV smart hub

As the scale of suburban rail and electric vehicles (EVs) continues to expand with the revolution of electrification of transportation, park and ride (P&R) facilities are increasingly recognized as critical energy coupling points between suburban rail traction transformers and EV charging stations. However, flexible coordination of the energy distribution among the bidirectional power flow of multiple trains and EVs’ charging demand becomes an urgent issue. In this paper, we establish a rail-EV Smart Energy Hub (SEH) framework integrating trains, ultra-capacitors (UC), and battery-based EVs. An emendable two-stage optimization model is proposed, enabling railways to provide R2X (railway-to-anything) services. The first stage determines the optimal train trajectory and adjusts timetables to minimize the energy consumption of multiple trains. In the second stage, the charging strategy of the EV is coordinated with the charging/discharging scheme of the UC, which takes the train power flow determined in the first stage as input. Meanwhile, the voltage unbalance caused by the railway is constrained to comply with the limits set by IEC/TR 61000-3-13. Case studies based on actual suburban railway lines in China demonstrate that the proposed scheduling optimization approach can significantly reduce the energy consumption of both railways and EVs.

Fast Charging System of Electric Vehicle Using Optimized Isolated Multi-Port DC-DC Converter based on Modified Coati Optimization Algorithm

Once clean, renewable energy sources are used to charge the batteries in electric vehicles (EVs), the vehicles can produce zero gas emissions, greatly improving the environment. EVs and other distributed energy storage devices can be used in a smart microgrid to deliver energy to the loads throughout highest times, reducing the impact of load shading and improving the quality of the electricity. To achieve these goals of energy balance between EVs, the grid, and renewable energy sources, an isolated hybrid multiport converter is required. This paper develops an optimized isolated multi-port DC-DC converter for controlling power flow in multiple directions in an EV. This converter contains a dc-dc unidirectional converter, a bidirectional dc-dc converter, a triple active bridge (TAB), and a multi-port dual active bridge converter. Additionally, the Optimal Controller (OC) is developed to manage the power between the EV and the battery. The power flow is achieved by using the Modified Coati Optimization Algorithm (MCOA). In the MCOA, the optimal gain parameter of the converter is selected. In the planned technique, a bidirectional DC-DC converter can be considered to interconnect the EV battery towards deliver bidirectional power flow ability with the battery. The proposed approach is executed in MATLAB, and presentations are assessed by considering performance measures. Moreover, it is contrasted with the traditional approaches of particle swarm optimization (PSO) and grey wolf optimization (GWO).

Designing a Bidirectional Power Flow Control Mechanism for Integrated EVs in PV-Based Grid Systems Supporting Onboard AC Charging

This paper investigates the potential use of Electric Vehicles (EVs) to enhance power grid stability through their energy storage and grid-support capabilities. By providing auxiliary services such as spinning reserves and voltage control, EVs can significantly impact power quality metrics. The increasing energy consumption and the global imperative to address climate change have positioned EVs as a viable solution for sustainable transportation. Despite the challenges posed by their variable energy demands and rising numbers, the integration of a smart grid environment with smart charging and discharging protocols presents a promising avenue. Such an environment could seamlessly integrate a large fleet of EVs into the national grid, thereby optimizing load profiles, balancing supply and demand, regulating voltage, and reducing energy generation costs. This study examines the large-scale adoption of EVs and its implications for the power grid, with a focus on State of Charge (SOC) estimation, charging times, station availability, and various charging methods. Through simulations of integrated EV–PV charging profiles, the paper presents a lookup-table-based data estimation approach to assess the impact on power demand and voltage profiles. The findings include multiple charging scenarios and the development of an optimal control unit designed to mitigate the potential adverse effects of widespread EV adoption.

Analysis and Design of a Battery and Supercapacitor‐Based Propulsion System for Electric Vehicles

ABSTRACTThis work deals with the design and development of a dual source based propulsion architecture for electric vehicles. The converter utilized for the propulsion system is derived from a conventional CuK converter, which operates in a bidirectional power flow mode. The proposed propulsion system has two energy sources: a battery and a supercapacitor (SC). The proposed system has continuous input and output currents at low and high voltage sides, which improves the cycle life of hybrid energy storage (SC and battery) and the DC‐link capacitor. Further, the proposed converter has magnetic reduction compared to a conventional two‐input bidirectional CuK converter. Moreover, an effective and simpler control strategy (only one switch is pulse width modulated (PWM) operated at a time) has been developed for energy management between sources during charging (regenerative braking) and discharging (propulsion), which is effectively verified by simulation and experimental results. Further, a frequency domain (bode plot)‐based technique is used for controller design and stability analysis of the proposed system.

Mathematical Analysis of Novel Method to Solve Protection Issues Pertaining To Solar PV Integration

The integration of Solar Photovoltaic (PV) systems into modern power grids presents protection challenges, such as voltage fluctuations, fault detection complexities, and bidirectional power flow issues. These challenges compromise grid reliability, requiring advanced methods to address them. This study introduces a novel mathematical analysis approach to solve protection issues in solar PV integration, focusing on developing an adaptive protection scheme using advanced mathematical models. The proposed approach utilizes differential equations, optimization techniques, and machine learning algorithms to create dynamic protection settings responsive to varying grid conditions. The method integrates fault detection and classification algorithms based on wavelet transform and support vector machines (SVM), allowing for rapid and accurate identification of fault types and locations. The findings demonstrate that the proposed adaptive protection scheme significantly enhances fault detection accuracy, reducing false trip rates by over 30% compared to conventional protection systems. Moreover, the method efficiently distinguishes between transient and permanent faults, ensuring swift isolation and minimizing disruption to solar PV operations. The developed model also exhibits robustness in handling variations in solar irradiance and load fluctuations, making it suitable for real-world grid applications. This research provides a substantial contribution to the field of solar PV integration by offering a mathematically grounded, adaptive protection solution that ensures improved reliability and resilience in power systems. The proposed method paves the way for the development of more advanced protection schemes, ensuring the seamless integration of renewable energy sources into modern grids while maintaining system stability and security.

AIFIS controlled BLDC Motor Drive for Agricultural Application with Solar System

In India, it is of utmost significance to ensure that the agricultural fields receive supplies of water. Agricultural applications are the focus of this paper, which depicts the implementation of an advanced BLDC motor connected drive that is controlled by an adaptive neuro fuzzy inference system (ANFIS). In this work, the implementation of the bidirectional power flow among the load and the renewable energy source (RES) is carried out. In order to power the water pump, renewable energy sources (RES) are generated through the use of solar photovoltaic (PV) cells. Additionally, a BLDC motor load is connected to the water pump through a single phase bi-directional converter and a voltage source inverter (VSI) inside the design. The PV system is able to get the maximum amount of power while simultaneously lowering the main grid and enabling consumers to run their motors and loads at their maximum efficiency throughout the entire day. This is made possible by the integration of fusing into the solar generating framework. Using the suggested controller, the PV system is able to reduce conversion costs and total harmonic content of the grid, all while maintaining the power factor, good quality in power, and stability of the system.