Hybrid Energy Sources Research Articles

Power management for the compensation of unbalanced grids using interphase power controller 240 and hybrid renewable energy sources

Ensuring an uninterrupted power supply is essential for industrial, commercial, and residential users. One effective method to achieve this is by implementing asymmetrical operation on transmission lines. This study introduces a power compensation strategy designed to maintain a continuous power supply, even during permanent single-phase faults on the transmission line. The proposed method leverages the compensating properties of the IPC 240 and the hybrid wind-photovoltaic technologies. The study focused on a 3 MW, 30 kV transmission line equipped with dual three-branch Interphase Power Controllers 240, which experienced a 33 % power loss due to a permanent single-phase fault. A hybrid wind-photovoltaic system, supplemented with battery storage, was modeled and sized to compensate for the power loss and integrated into the line. The power management strategy operates in two main modes. In mode 1, the line operates without faults. In mode 2, when a permanent single-phase fault occurred, the transmitted power dropped to 2 MW, necessitating the integration of the hybrid energy source to compensate for the reduced power. Weather fluctuations are factored in the management strategy, guaranteeing a stable power supply from the hybrid energy source. Simulations conducted using MATLAB/Simulink demonstrated the system's capability to maintain an uninterrupted power supply to the load. Even under permanent single-phase fault conditions and adverse weather, the system restored power to nearly 2.96 MW, achieving a 99 % compensation rate, which is the highest compared to previous studies.

Modelling of small signals in MISO DC-DC converters for hybrid energy sources

Modelling of small signals in MISO DC-DC converters for hybrid energy sources

A Hybrid Energy Sources Based System for Powertrain of Electric Vehicles

A Hybrid Energy Sources Based System for Powertrain of Electric Vehicles

Optimal Control Strategy for Power Management Control of an Independent Photovoltaic, Wind Turbine, Battery System with Diesel Generator

The need for a greater supply of energy from sustainable sources is growing because of increasing energy prices, concerns about nuclear power, climate change, and power grid disruptions. This research offers a method for the balance of power management of a combination of multi-source DC and AC supplier systems that enables sources of clean energy based on an independent grid to function economically and with the highest levels of system predictability and stability possible. The DC microgrid's hybrid generation source consists of a diesel power source, wind, photovoltaic (PV) power, and a battery bank. The energy system can fulfill the load demand for electricity at any moment by connecting various renewable sources. It can function both off and on the grid. The microgrid may occasionally not be able to provide sufficient electricity, while every green energy source's electricity contribution is based on how its supply varies and how much power is needed to meet demand. As a result, a diesel generator is required as additional backup power, particularly while operating off-grid. This paper designs and implements an MPPT technique for a PV system based on the GWO algorithm. By creating PWM pulses in response to variations in the PV panel voltage, this method modifies the converter's duty cycle, while wind turbines using MPPT based on P&O, to get the most out of hybrid energy sources that are renewable while simultaneously enhancing the quality of power. The priority sources of electricity for the grid are photovoltaics and wind power. Based on the results of simulations and experiments, the proposed control method for DC, which uses the MPPT approach, can dynamically switch between all of the system's various modes of operation, independent of the battery's condition or environment, ensuring safe operation and constant bus voltage. An analysis was conducted on the suggested system's performance. It has been noted that compared to the conventional approaches, the suggested GWO-based MPPT methodology is quicker and produces fewer MPP oscillations. It offers a more effective reaction to quickly shifting atmospheric conditions. Results of simulation for the recommended control scheme with MATLAB/Simulink.

Optimization of Microgrid fed from Hybrid Energy Sources using the Swarm Intelligence

To combat the widening gap between demand and supply, it is crucial to include renewableenergy sources as a Distributed Generation newline (DG). Despite their usefulness in reducing the gap, REsources place a heavy pressure on generation scheduling, particularly wind power. Utilities are compelledto use wind curtailment due to fluctuations in wind energy output, which reduces the incentive to switch torenewable sources of power. Demand Side Management (DSM) is one approach to this problem, since itadjusts demand in real time to match generation while the grid is operating. Conventional DemandResponse (DR) systems were created to minimise consumption during peak hours by arranging userloads in exchange for incentives. The Smart Grid (SG) concept provides a high-tech means ofcommunication for keeping tabs on and managing the power grid. This motivated the creation of asophisticated DR technique known as Demand Dispatch (DD), in which consumption is timesynchronizedwith electricity production. In order to deal with the fluctuations in wind power production,this thesis proposes a DD method. The entities involved in DD and the way in which data is sharedbetween them are laid out in an operational framework. In order to find and evaluate all of the studiesthat have been published on a certain topic, researchers often undertake systematic literature reviews.

Parasitism-predation optimization algorithm-based energy management system for hybrid electric power system

Efficient management of hybrid energy sources, including fuel cells, batteries, and supercapacitors, is crucial for optimizing fuel consumption during emergency aircraft landings. Researchers are increasingly recognizing metaheuristic optimization algorithms for their effective convergence properties. This paper introduces a Parasitism-Predation Optimization Algorithm (PPOA)-based Energy Management System (EMS) hybridized with an Equivalent Consumption Minimization Strategy (ECMS) and an External Energy Maximization Strategy (EEMS) to reduce fuel consumption in Hybrid Energy Sources (HES). The proposed PPOA-EMS is developed using MATLAB/SIMULINK and validated on the Opal-RT 5700 real-time Hardware-in-the-Loop (HIL) simulator. The PPOA-ECMS configuration achieved fuel consumption of 17.5 g in simulations and 17.44 g in real-time simulation, with an efficiency of 88.61%. Similarly, the PPOA-EEMS configuration further reduces the fuel consumption to 15.2 g in simulations and 15.36 g in real-time simulations, with an efficiency of 93.17%. This work highlights the PPOA-EMS optimization in hydrogen fuel consumption when compared to conventional and metaheuristic algorithms, including State Machine Control (SMC), External Energy Maximization Strategy (EEMS), Equivalent Consumption Minimization Strategy (ECMS), Classical PI Controller (CPI), Gray Wolf Algorithm (GWO), Mine Blast Algorithm (MBA), and Slaps Swarm Algorithm (SSA). Finally, the findings demonstrate the effectiveness of PPOA-EMS in enhancing fuel efficiency and ensuring robust energy management.

Dynamic computation scheduling for hybrid energy mobile edge computing networks

The rapid expansion of Mobile Edge Computing (MEC) is driven by the growing demand for resource-intensive applications within the Internet of Things. Computation offloading allows these applications to be executed at the network edge, but it leads to significant electricity expenses for network operators. To mitigate these costs, one promising solution is to power base stations (BSs) with a hybrid energy supply that combines unpredictable harvested energy with stable energy from the smart grid. This paper investigates joint computation scheduling for mobile devices (MDs) and resource allocation in a MEC network incorporating hybrid energy sources. Our objective is to maximize long-term time-averaged service utility by optimizing parameters such as BS battery supply, harvestable energy, CPU frequency, transmission power, task-partition factor, and MD-BS associations. To tackle this complex problem, we exploit the Lyapunov optimization framework to decompose it into deterministic subproblems for each time slot and propose an online network service utility maximization scheduling (NSUMS) algorithm. Experimental results show that our algorithm outperforms benchmark schemes in service utility and energy expenditure, improving the completion ratio by 32%, reducing the failure rate by 80%, and decreasing MD energy consumption by 28%.

A modular multiport Landsman converter-driven hybrid EV charging station with adaptive power management system

This article presents the implementation of a hybrid charging station that utilizes renewable energy sources to power its operations. The core of this system is a modular multiport Landsman converter, which serves as the interface between diverse renewable sources, including photovoltaic panels, wind turbines and fuel cells. A sophisticated multi-agent-based energy management system is employed to address the intermittency of renewable energy supply throughout the day. Additionally, a fuzzy logic controller (FLC) facilitates the seamless charging of electric vehicles within the system. The FLC detects the state of charge (SoC) of the vehicle's battery and the battery present in the station, enabling efficient EV charging. The multi-agent energy management system with load shedding controller (MAEMLS) and FLC continuously monitor the power availability, power demand, charging time, and charging duration of the charging station. This approach reduces the charging rate for EV owners during peak demand periods. The proposed system is modelled and tested using MATLAB/SIMULINK software, and a laboratory prototype has been constructed. The results demonstrate that the anticipated converter is highly suitable for integrating hybrid energy sources and charging electric vehicles (EVs). Notably, this converter exhibits an efficiency ranging from 85 %-95.05 %, based on the availability of input DC sources.

Artificial Neural Network-Based Real-Time Power Management for a Hybrid Renewable Source Applied for a Water Desalination System

Water desalination systems integrated with stand-alone hybrid energy sources offer a remarkable solution to the water–energy challenge. Given the complexity of these systems, selecting an appropriate energy management system is crucial. In this regard, employing artificial intelligence techniques to develop and validate an energy management system can be an effective approach for handling such intricate systems. Therefore, this paper presents an ANN-based energy management system (ANNEMS) for a pumping and desalination system connected to an isolated hybrid renewable energy source. Thus, a parametric sensitivity algorithm was developed to identify the optimal neural network architecture. The water–energy management-based supervised multi-layer perceptron neural network demonstrated effective power sharing within a short time frame, achieving accuracy criteria of RMSE, R, and R² between the actual and estimated electrical power of the three motor pumps. The ANNEMS is defined to facilitate real-time power sharing distribution among the various system motor pumps on the test bench, considering the generated power profile and water tank levels. The proposed strategy employs power field oriented control to maintain DC bus voltage stability. Experimental results from the implementation of the proposed ANNEMS are provided. Therein, the power levels of the three motor pumps demonstrated consistent adherence to their reference values. In summary, this study highlights the significance of selecting appropriate energy management for real-time experimental validation.

Short Circuit Analysis due to Reconfiguration of AC to DC Electric Power System on Tanker Ship with Hybrid Energy Source

Short Circuit Analysis due to Reconfiguration of AC to DC Electric Power System on Tanker Ship with Hybrid Energy Source

A Novel Assimilate Power Flow Control Technique Based Multiport Converter For Hybrid Power Generation System With Grid-Connected Application

Clean, sufficient energy from renewable resources such as the Sun, Wind, and other natural resources is provided via renewable energy technology. Electricity failure also increases with the electricity demand. Thus, consistent loads may be provided by renewable energy sources. A multiport conversion architecture has been developed for hybrid energy sources such as wind and solar sources. A practical method of producing power is to combine wind and sun resources. This study presents a coordinated control method and dynamic modelling tools for an integrated microgrid scheme that combines fuel cell technology, backup diesel generation, photovoltaic systems, battery generators, and assimilated power flow control (APFC). A unique vibrant filter compensator based on dynamics is employed to achieve a fully balanced integrated system. This ensures minimal inrush current, load excursions, and regulated DC bus voltage while optimizing the utilization of the diesel generator set. Our primary area is to achieve the most efficient operation of the integrated renewable energy sources, including PV panels and fuel cells. Photovoltaic Power System with Backup Fuel Cell Source for Hybrid Power System: The control unit in this hybrid system manages the power from both the fuel and the PV cells. When the control power is activated, it triggers the fuel cell and photovoltaic cell powers. The control device first checks if the photovoltaic cell is generating power. If the PV energy is sufficient to meet the load requirements, the control power links the photovoltaic energy to the load. In cases where the load cannot be fulfilled by photovoltaic power alone, the control system disconnects the photovoltaic cell power and switches to the backup fuel cell power. Afterwards, when the power generated from the photovoltaic cells reaches a sufficient level, the control system promptly switches from the fuel cell to the photovoltaic cells. The fuel cell is integrated with a photovoltaic system and a DC-DC boost conversion in conjunction with the resistive workload. The controller outputs have been fine-tuned, and PWM is generated and subsequently supplied to the converter's switching. The converter's design considers conversion efficiency, power sharing, system stability, and responsiveness in various operational scenarios to ensure successful operation. The effectiveness of the simulated output APFC techniques is evaluated for each parameter, such as steady-state error, THD, and the system's efficiency.

Efficiency Study Analysis of Electrical Energy in Container Stacking Equipment Rubber Tyred Gantry Crane (RTGC) Using Hybrid Energy Source Method

This research to perform Efficiency Study Analysis Of electrical energy In Container Stacking Equipment Rubber Tyred Gantry Crane (RTGC) Using Hybrid Energy Source Method. RTGC gets power supply from Engine Diesel Generator with capacity of 350 kW. However, Genset power source in RTGC has drawbacks, such as when RTGC condition does not move (stationary) can lead to waste of fuel on diesel engine drivers. the Hybrid Energy Source is one of the technology which combines electrical energy source from generators and batteries, The battery of hybrid system can obtain electric energy saving until 32%, where for the Hoist system 11,72 %, Trolley system 33,63 % dan Gantry system 7,58 %.

A Wide Voltage Range Bidirectional High Voltage Transfer Ratio Quadratic Boost DC-DC Converter for EVs With Hybrid Energy Sources

A Wide Voltage Range Bidirectional High Voltage Transfer Ratio Quadratic Boost DC-DC Converter for EVs With Hybrid Energy Sources

Control energy management system for photovoltaic with bidirectional converter using deep neural network

Rapid population growth propels technological advancement, heightening electricity demand. Obsolete fossil fuel-based power facilities necessitate alternative energy sources. Photovoltaic (PV) energy relies on weather conditions, posing challenges for constant energy consumption. This hybrid energy source system (HESS) prototype employs extreme learning machine (ELM) power management to oversee PV, fossil fuel, and battery sources. ELM optimally selects power sources, adapting to varying conditions. A bidirectional converter (BDC) efficiently manages battery charging, discharging, and secondary power distribution. HESS ensures continuous load supply and swift response for system reliability. The optimal HESS design incorporates a single renewable source (PV), conventional energy (PNL and genset), and energy storage (battery). Supported by a BDC with over 80% efficiency in buck and boost modes, it stabilizes voltage and supplies power through flawless ELM-free logic verification. Google Colab online testing and hardware implementation with Arduino demonstrate ELM's reliability, maintaining a direct current (DC) 24 V interface voltage and ensuring its applicability for optimal HESS.

AVIOANE ELECTRICE: PROVOCĂRI, OPORTUNITĂȚI, REALIZĂRI, TENDINȚE ACTUALE, PERSPECTIVE DE VIITOR

Electric vehicles are widely used in various transportation applications: automobiles, buses, ships, trains, unmanned aircraft vehicles (UAV), and airplanes. Rising greenhouse gas emissions and the risk of fossil fuel depletion have driven the transition to electric transport, which the European Union aims to reduce substantially by 2035 through renewable energies. In air transport, this transition is more difficult, especially due to the weight of the equipment on board. Without exception, they all use the energy stored in batteries to drive electric motors. In recent years, various hybrid drive systems have developed in aviation, systems based on fuel cells and environmentally friendly energy sources par excellence. Their main advantages are high efficiency, lightweight, modularity, and lack of moving parts. When fueled with hydrogen, they do not produce greenhouse gases, the only reaction products being water and heat. Axial magnetic flux motors have replaced the classic radial magnetic flux electric motors with low weight and volume, efficiency, power, and high torque previously developed for electric vehicles with in-wheel motors and are used without modification. This paper presents an overview of the main problems raised by electric propulsion of aircraft. Also, important achievements and development trends in the field are presented. Finally, a light, electrically powered aircraft configuration is proposed with a hybrid energy source composed of the hydrogen fuel cell, LiIon Polymer battery, and axial magnetic flux motor.

Comparative assessment and improvement of the powertrain supplied by hybrid energy source in fuel cell vehicles

The energy distribution and conversion between the fuel cell (FC) and the battery are realized by the powertrain in fuel cell vehicles (FCVs). In this study, the cost and efficiency of a novel powertrain based on the dual winding motor are assessed and compared with the mainstream powertrain. To shorten the energy transfer chain from FC to motor, a dual winding motor is employed and supplied by the energy source (ES) directly through inverters. The DC/DC converter is eliminated from the powertrain to avoid its cost and energy loss. The powertrain based on the dual winding motor is further optimized by multiple objectives, including FCV economy and durability. The double-loop optimization is proposed to solve the coupling between hybrid energy source sizing (HESS) and energy management strategy (EMS). Particle swarm optimization is adopted in outer-loop optimization to get the optimal size of the ES. Dynamic programming is used in inner-loop optimization to get the optimal energy distribution between ESs. Compared with the prevalent DC/DC converter-based powertrain, the optimized powertrain based on the dual winding motor is lower in manufacturing cost and fuel consumption.

Comparative Analysis of Dual Source DC/DC converter Topologies for Improved Performance in Integration of Hybrid Energy Sources

Comparative Analysis of Dual Source DC/DC converter Topologies for Improved Performance in Integration of Hybrid Energy Sources

Experimental research regarding the autonomy of electric cars in real driving conditions

Currently, almost all car manufacturers have in the production scheme at least one vehicle model with a hybrid or electric energy source. This fact is generated by the high interest in the market related to the subsidies offered to the purchase by the states but also by the high performance in terms of pollution, noise, and economy, which they can achieve. The only performance indicator which for the moment is not at a satisfactory level is represented by autonomy. At this point, the electric cars have an autonomy between 250 and 800 km driven after a single charge of the traction battery. It should be mentioned though that, the autonomy of electric vehicles declared by European manufacturers is obtained in laboratory conditions, these being carried out in accordance with the provisions of the European Union regulations. Obviously, there are significant differences between the electric autonomy obtained in laboratory conditions and the autonomy obtained in real driving conditions. These differences are related to the ideal test conditions achieved at the laboratory level and which cannot be fully found in real driving conditions. Starting from these premises, in a first part of the paper, are presented the theoretical arguments regarding the determination of autonomy in real driving conditions with a driving profile specific to the RDE testing and are described the technical details of the electric vehicle under investigation. In the second part of the paper, are presented the results of the experimental research for the three driving zones specific to the RDE testing: driving in the urban zone, driving in the rural zone, and driving on the motorway. The downward evolution of the state of charge of the traction battery for each driving zone is shown graphically. Following the processing, analysis and interpretation of the data acquired from the car electronic control units, are formulated conclusions regarding the causes that influence the electric autonomy.

Double‐input triple‐output non‐isolated DC–DC converter based on coupled inductor with high step‐up output voltages

SummaryThis paper introduces a new non‐isolated double‐input triple‐output (DITO) high voltage gain DC–DC converter with a wide range of applications, including hybrid voltage source systems, solar home appliances, DC‐bus power distribution systems, and electrical vehicles (EVs). The proposed DITO converter interfaces two hybrid voltage sources to supply three different output loads with varying voltage and power levels. Compared to conventional converters of the same type, the proposed DITO converter offers several advantages, including operation for all duty cycles, integration of hybrid energy sources to transfer power to multiple loads, high voltage conversion ratio for all three‐output ports, a higher ratio of total voltage gain to total components number, common grounded input and output DC ports, simultaneous DC voltage regulation of three‐output ports by tuning separate controlling parameters of duty cycles, an additional turns ratio of coupled inductors to increase the voltage gain of output ports, and medium voltage stress on switches. The proposed DITO converter utilizes a new single‐switch single‐coupled‐inductor DC–DC structure, which is analyzed and verified through a prototype implementation for 25 and 30 V input voltages and 210, 330, and 400 V output voltages with a total power of 480 W. The experimental results confirm the simulation results and theoretical analysis.

Photovoltaic-wind-battery and diesel generator-based hybrid energy system for residential buildings in smart city Coimbatore.

The building consumes almost 40% of the energy generated in the building. Investigating the photovoltaic system, wind, battery, and diesel generators for residential buildings can reduce energy utilization. In this work, various energy sources are combined to form hybrid energy sources, which are designed based on the load of the residential building. The Hybrid Optimization of Multiple Energy Resources tool optimizes various energy sources such as photovoltaic (PV), wind, diesel generator (DG), and battery. An investigation on the residential load in the smart city of Coimbatore, Tamil Nadu, India, is being carried out. This article examined the technological and economic feasibility of solar photovoltaic, wind, diesel generators, and batteries combined to form a hybrid energy source (HES). With 2kW of photovoltaic, 1kW wind, 1kW of DG, 1kW of the power converter, and five batteries, system case 1 (photovoltaic/wind/diesel generator/battery model) had the best results in the simulation and was recommended for use in the proposed residential building. As a result, it has a minimum net present value of $14,568 and an energy cost of $0.312/kWh, which is about 39% cheaper than system base cases. The sensitivity and environmental analysis are carried out to analyze the system's feasibility.