Power Management Strategy Research Articles

A Power‐Managed Tribovoltaic Nanogenerator and Self‐Powered Wireless Temperature Monitoring System

AbstractThe emerging tribovoltaic nanogenerators (TVNGs) has garnered considerable attention in recent years owing to its higher power density, but it is still limited by unstable output, mismatched impedance, and lower charging speed. Herein, a power‐managed GaN‐based TVNG is first proposed and a self‐powered wireless temperature monitoring system (SWTMS) is developed. The energy backflow from transition capacitor to TVNG is effectively suppressed by applying a high‐voltage‐resistant diode. Benefiting from the combination of the maximum power point tracking (MPPT and the buck converter, an autonomous power management unit (PMU) is designed. The internal impedance of TVNG with PMU decreases by ten times and the charging speed of capacitor increases by 7.18 times compared to those without PMU. Moreover, 100 LEDs and six parallel thermohygrometers can be constantly powered. The wireless temperature monitoring in industrial environments without external power supply is demonstrated through the developed SWTMS. This work proposes an effective power management strategy for TVNG with high output voltage, which exhibits great potential to promote the development of self‐powered sensors and Industrial Internet of Things (IIoT).

A STC-DAB converter for PV–EV battery-based hybrid system with a unified power management scheme in a grid-integrated and islanded condition

A STC-DAB converter for PV–EV battery-based hybrid system with a unified power management scheme in a grid-integrated and islanded condition

Design of a Power Management Strategy in Smart Distribution Networks with Wind Turbines and EV Charging Stations to Reduce Loss, Improve Voltage Profile, and Increase Hosting Capacity of the Network

Design of a Power Management Strategy in Smart Distribution Networks with Wind Turbines and EV Charging Stations to Reduce Loss, Improve Voltage Profile, and Increase Hosting Capacity of the Network

Dual-level design for cost-effective sizing and power management of hybrid energy storage in photovoltaic systems

Integration of hybrid energy storage systems (HESS) into photovoltaic (PV) applications has been a hot topic due to their versatility. However, the proper allocation and power management schemes of HESS are challenges under diverse mission profiles. In this paper, a cost-effectiveness-oriented two-level scheme is proposed as a guideline for the PV-HESS system (i.e., PV, Li-ion battery and supercapacitor), to size the system configuration and extend battery lifespan while considering the power ramp-rate constraint. On the first level, a sizing methodology is proposed to balance the self-sufficiency and the energy throughput between the PV system and the grid to achieve the most cost-effectiveness. On the second level, an improved adaptive ramp-rate control strategy is implemented that dynamically distributes the power between the battery and supercapacitor to reduce the battery cycles. The case study presents the whole two-level design process in detail, and verifies the effectiveness of the proposed strategy, where the results show that the battery cycles are reduced by up to 13% over one year without affecting the self-sufficiency of the PV system.

A Hybrid Energy Storage System Integrated with a Wave Energy Converter: Data-Driven Stochastic Power Management for Output Power Smoothing

Beyond solar and wind energy, wave energy is gaining great interest due to its very high theoretical potential, although its stochastic nature causes intermittent and fluctuating power production. Energy storage system (ESS) integration to wave energy converter (WEC) plants represents a promising solution to mitigate this issue. To overcome the technological limits of the single storage devices, the hybridization of complementary ESSs represents an effective solution, extending the operating range over different timeframes. This paper analyzes the benefits of Li-ion battery–supercapacitor hybrid ESS integration into a grid-connected WEC, aiming at smoothing the produced power oscillations. The hybridization concept involves coupling a power-intensive technology, such as a supercapacitor devoted to managing fluctuations at higher frequency, with a battery technology exploited to manage power variations over longer timeframes to mitigate degradation issues. In this study, a multi-objective data-driven power management strategy, based on the simultaneous perturbation stochastic approximation (SPSA) algorithm, is implemented to minimize power fluctuations in terms of power ramp (representing the power variation between two consecutive values with a 1 s time step), both at the Point of Common Coupling (PCC) and the Li-ion battery terminals, thanks to the supercapacitor peak-shaving function. The SPSA management strategy, together with a suitable sizing procedure, allows a reduction of more than 70% in the power oscillations at the PCC with respect to those at the WEC terminals, while decreasing battery stress by more than 25% if compared to a non-hybrid ESS consisting of a Li-ion battery. This shows how supercapacitor features can extend battery lifespan when integrated in a hybrid ESS.

In‐depth analysis of the power management strategies in electric vehicles

AbstractIn electric vehicles, the battery is a key component that calculates vehicle performance. Due to their greater efficiency and the lower cost of power, charging an electric vehicle is more affordable than purchasing gasoline or diesel for travel needs. To increase the battery's lifespan, the accuracy of the battery model for electric vehicles must be enhanced. To operate at their peak efficiency, batteries must be managed properly by a battery management system. This research illustrates the functioning of a rechargeable electric vehicle battery's charging system. There are several types of rechargeable batteries used, including lead acid, lithium‐ion, and nickel‐cadmium batteries. In recent trends, are used in the majority of cars use rechargeable lithium‐ion batteries. In general, accumulated heat, rapid utilization, and total energy throughput have an impact on the battery life of electric vehicles. In this study, 50 papers were analyzed about battery charging in an electric vehicle, which utilized different measures, as well as achievements attained by various methods. This survey reviewed the information from a different journal, along with their advantage, disadvantages, and challenges. The review lays the groundwork for future researchers to gain a deeper understanding of electric vehicles by offering a thorough interpretation of the methods currently in use.

Power Management Strategy of a PEM Fuel Cell-Battery Powered Electric Vehicle

A MATLAB-Simulink based mathematical vehicle simulation model is developed in this investigation to analyze power management strategy of a proton exchange membrane (PEM) fuel cell and battery powered electric vehicle. The vehicle simulation model incorporated a real-world commercial passenger vehicle operator’s drive cycle inputs and associated vehicle dynamics to determine accurate estimation of total onboard power requirements and withdrawal of power from the fuel cell and battery sources to meet the demand. The power management strategy is designed to meet the vehicle’s onboard power demand based on the availability of hybrid combination of fuel cell and battery power sources. The fuel cell stack is the primary power source of the vehicle. The fuel cell is also charging the battery with excess power produced onboard and hence controls the state-of-charge of the battery. Battery is used as a supplemental power source for meeting the vehicle’s peak power demands. The parameters are optimized to implement the power management strategy by considering the battery state-of-the charge, the drive torque and the vehicle drive performance. The model simulation results with optimized parameters showed that the power requirement of the electric vehicle was significantly affected by the combination of fuel cell and battery power management system as well as the operating behaviors of the end user (driver).

Flight-Path Optimization for a Hybrid-Electric Aircraft

Abstract This study aims to illustrate a sequence that optimizes the flight-path trajectory for a hybrid-electric aircraft at mission level, in addition to identifying the respective optimum power management strategy. An in-house framework for hybrid-electric propulsion system modeling is utilized. A hybrid-electric commuter aircraft serves as a virtual test-bench. Vectorized calculations, decision variable count, and optimization algorithms are considered for reducing the computational time of the framework. Performance improvements are evaluated for the aircraft's design mission profile. Total energy consumption is set as the objective function. Emphasis lies on minimizing the average value and standard deviation of the energy consumption and timeframe metrics. The best performing application decreases computational time by two orders of magnitude, while retaining equal accuracy and consistency as the original model. It is employed for creating a dataset for training an artificial neural network (ANN) against random mission patterns. The trained network is integrated into a surrogate model. The latter part of the analysis evaluates optimized mission profile characteristics with respect to energy consumption, against a benchmark flight-path. The combined optimization process decreases the multihour-scale timeframe by two orders of magnitude to a 3-min sequence. Using the novel framework, a 12% average energy consumption benefit is calculated for short, medium, and long regional missions, against equivalent benchmark profiles.

An Ensemble Approach to Short-Term Wind Speed Predictions Using Stochastic Methods, Wavelets and Gradient Boosting Decision Trees

Considering that wind power is proportional to the cube of the wind speed variable, which is highly random, complex power grid management tasks have arisen as a result. Wind speed prediction in the short term is crucial for load dispatch planning and load increment/decrement decisions. The chaotic intermittency of speed is often characterised by inherent linear and nonlinear patterns, as well as nonstationary behaviour; thus, it is generally difficult to predict it accurately and efficiently using a single linear or nonlinear model. In this study, wavelet transform (WT), autoregressive integrated moving average (ARIMA), extreme gradient boosting trees (XGBoost), and support vector regression (SVR) are combined to predict high-resolution short-term wind speeds obtained from three Southern African Universities Radiometric Network (SAURAN) stations: Richtersveld (RVD); Central University of Technology (CUT); and University of Pretoria (UPR). This hybrid model is termed WT-ARIMA-XGBoost-SVR. In the proposed hybrid, the ARIMA component is employed to capture linearity, while XGBoost captures nonlinearity using the wavelet decomposed subseries from the residuals as input features. Finally, the SVR model reconciles linear and nonlinear predictions. We evaluated the WT-ARIMA-XGBoost-SVR’s efficacy against ARIMA and two other hybrid models that substitute XGBoost with a light gradient boosting machine (LGB) component to form a WT-ARIMA-LGB-SVR hybrid model and a stochastic gradient boosting machine (SGB) to form a WT-ARIMA-SGB-SVR hybrid model. Based on mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination (R2), and prediction interval normalised average width (PINAW), the proposed hybrid model provided more accurate and reliable predictions with less uncertainty for all three datasets. This study is critical for improving wind speed prediction reliability to ensure the development of effective wind power management strategies.

Virtual-inertia-based power management scheme in fuel cell-battery-supercapacitor-based DC microgrid

Virtual-inertia-based power management scheme in fuel cell-battery-supercapacitor-based DC microgrid

Fuzzy Logic Based Power Management Strategy for Plug-in Hybrid Electric Vehicles with Parallel Configuration

In this paper, a new power management strategy for parallel plug-in hybrid electric vehicles is introduced. Using the proposed strategy the fuel consumption of the vehicle would be reduced. The proposed power management strategy is simple, and the required computational burden is not heavy. Hence, it can be used in real time applications. Estimating, the trip distance using GPS technology, the controller can manage the battery state of charge. The affectivity of the introduced approach is investigated through conducted simulations in ADVISOR environment. The obtained results are validated using a simple rule base power management strategy.

Design and real-time implementation of wind–photovoltaic driven low voltage direct current microgrid integrated with hybrid energy storage system

Low-voltage direct current (LVDC) microgrid has emerged as a new trend and smart solution for the seamless integration of distributed energy resources (DERs) and energy storage systems (ESS). This paper presents a coordinated controlled power management scheme (PMS) for wind–solar fed LVDC microgrid equipped with an actively configured hybrid energy storage system (HESS). The comprehensive design, development, implementation and thorough result analysis is presented to validate the proposed PMS performance. It dynamically adapts to different operating modes based on the availability of DERs and load demand. By incorporating the HESS, the scheme effectively handles sudden and gradual power surges, leading to swift regulation of the DC bus voltage, effective power balance and reduced stress on the battery. A laboratory test bench is developed comprising a wind power generating unit (WPGU), solar power generating unit (SPGU), battery energy storage system (BESS), and supercapacitor energy storage system (SCESS). Moreover, the detailed design and testing of various converters, gate drivers, filters, signal conditioning along with calibration of sensors are discussed. The developed PMS is executed using a DS1103 digital controller confirming its effectiveness in facilitating smooth mode transition and proper power sharing among all DERs and loads.

Control a Photovoltaic/Wind Turbine/Diesel generator with storage battery

Wind/Photovoltaic energy systems are among the most used configurations in multi-sources energy systems because their reliability, robustness and power sources complementarily. For heavy loads, it is more convenient to add backup systems (diesel generator) for an enhanced power availability. In order to protect power sources and increase their lifespan, several power management strategies are such as fuzzy logic control, artificial neural networks and metaheuristic algorithms are proposed and used in literature. This paper presents a power supervisory control (PMC) based on fuzzy logic control dedicated for photovoltaic/wind turbine/ diesel generator/battery systems. The application of the aforementioned technique makes it possible to predict how the hybrid system various operating processes would change in response to solar irradiation and wind speed variations. The study was implemented under Matlab/Simulink. To obtain more realistic results, measured weather values have been incorporated in the simulation. The application is made in a coastal town which has a good potential values of solar irradiance and wind speeds. The obtained results show that the load have been supplied while the batteries were protected due to the PMC.

Power management strategy for photovoltaic system with hybrid storage (Batteries/Supercapacitors)

This paper is presented in regard to the interesting continuous interest attributed to photovoltaic energy systems (PESs). To this aim, a study of an isolated photovoltaic system with hybrid storage (HS) is presented. Supercapacitors (SCs) are typically used because of their high specific power density.. Combining batteries and supercapacitors is a solution to maximize their benefits. In this work, the HS is a combinaison of batteries and supercapacitors. The goal of this work is to build a new management algorithm. This aforementioned technique will ensure the control of energy flow between system power sources. Furthermore, it will regulate the charge/discharge cycles of the proposed HS by maintaining their state of charge (SOC) between a desired minimum and maximum values. The proposed method is simple, efficient and it is not computationally heavy. The obtained simulation results are satisfactory and show the interest and effectiveness of the proposed management strategy.

Preparation of Alumina Thin Films by Reactive Modulated Pulsed Power Magnetron Sputtering with Millisecond Pulses

This paper aims to investigate the quality of thin alumina films deposited on glass samples using magnetron sputtering in the reactive modulated pulsed power mode (MPPMS) and evaluate the process productivity. The aluminum target was sputtered in Ar/O2 gas mixtures with different fractions of oxygen in the total gas flow, in the fixed pulsed voltage mode. The pulse-on duration was varied between 5 and 10 ms, while the pulse-off time was 100 or 200 ms. The dependences of mass deposition rate and discharge current on the oxygen flow were measured, and the specific deposition rate values were calculated. Prepared coatings had a thicknesses of 100–400 nm. Their quality was assessed by scratch testing and by measuring density, refractory index, and extinction coefficient for different power management strategies. The strong influence of pulse parameters on the coating properties was observed, resulting in a maximum density of 3.6 g/cm3 and a refractive index of 1.68 for deposition modes with higher duty cycle values. Therefore, adjusting the pulse-on and pulse-off periods in MPPMS can be used not only to optimize the deposition rate but also as a tool to tune the optical characteristics of the films. The performance of the studied deposition method was evaluated by comparing the specific growth rates of alumina coatings with the relevant data for other magnetron discharge modes. In MPPMS, a specific deposition rate of 200 nm/min/kW was obtained for highly transparent Al2O3, without using any dedicated feedback loop system for oxygen pressure stabilization, which makes MPPMS superior to short-pulse high-power impulse magnetron sputtering (HiPIMS) modes.

Implementation of battery storage system in a solar PV-based EV charging station

Commercial production of solar-powered battery charging stations for electric vehicles has recently started largely as a result of the growing popularity of electric vehicle (EV) technology as well as the falling cost of photovoltaic (PV) systems. The rise in popularity of EVs may be attributed to both the progression of technology and rising awareness of environmental issues. In this work, a 400V DC bus voltage-based EV charging station is designed which is powered by both a PV system and a utility grid. Also, battery energy storage units are used to overcome the unpredictable behavior of the environment. An Energy management algorithm (EMA) has been presented to balance the power flow among PV, EVs, BESS, grid, and residential load. A sharing coefficient-based power sharing is done between the grid and battery to reduce the stress from the battery units. Then, a multi-step constant current charging method is used to charge the EVs in the charging station to maintain the proper state of charge (SOC) of EV batteries. During this study, different operating conditions are considered according to available PV power and load to validate the power management scheme in the EV charging station. The model is run using MATLAB simulation and opal-RT simulation. The obtained results have been demonstrated with an explanation and system performance verification.

The reactive power and voltage control management strategy based on virtual reactance cloud control

The reactive power and voltage control management strategy based on virtual reactance cloud control

A new power management strategy for PV-FC-based autonomous DC microgrid

Solar energy is widely available in nature and electricity can be easily extracted using solar PV cells. A fuel cell being reliable and environment friendly becomes a good choice for the backup so as to compensate for continuously varying solar irradiation. This paper presents simple control schemes for power management of the DC microgrid consisting of PV modules and fuel cell as energy sources and a hydrogen electrolyzer system for storing the excess power generated. The supercapacitor bank is used as a short term energy storage device for providing the energy buffer whenever sudden fluctuations occur in the input power and the load demand. A new power control strategy is developed for a hydrogen storage system. The performance of the system is assessed with and without the supercapacitor bank and the results are compared. A comparative study of the voltage regulation of the microgrid is presented with the controller of the supercapacitor bank, realized using a traditional PI controller and an intelligent fuzzy logic controller.

A Decentralized Power Management and Sliding Mode Control Strategy for Hybrid AC/DC Microgrids including Renewable Energy Resources

This paper presents a new decentralized robust strategy to improve small and large-signal stability and power-sharing of hybrid AC/DC microgrids and improve its performance for nonlinear and unbalanced loads. In addition to the sliding mode controller for DC/DC converters, for the sake of improving power sharing and regulating active and reactive powers injected by distributed energy resources, and moreover, controlling harmonic and negative-sequence current in the presence of nonlinear and unbalanced loads, two separate controllers for positive sequence power control and negative sequence current control are designed based on the sliding mode control and Lyapunov function theory, respectively. The theoretical concept of the proposed robust control strategy, including mathematical modeling of microgrid components, basic theorems, controller design procedure, and robustness and closed loop stability analysis are outlined. Also, this direct power/current/voltage control scheme is governed by a new hybrid AC/DC hierarchical control scheme that exploits a harmonic virtual impedance loop and voltage compensation scheme. To show the effectiveness of the proposed robust control scheme, offline time-domain simulations are done on a hybrid AC/DC wind/photovoltaic/fuel-cell microgrid with nonlinear and unbalanced loads in MATLAB/Simulink environment, and the results are experimentally verified by OPAL-RT real-time digital simulator.

A power management strategy for PV and hybrid energy storage system

Renewable energy sources are sporadic in nature, they supplement conventional energy sources. The stability of the distribution system is impacted by the inclusion of these traditional sources. The power quality of the system is also impacted by random fluctuations. Systems for storing energy are therefore created. Therefore, a battery and capacitor integrated power management method for grid-connected photovoltaic systems is devised. Battery and supercapacitor work together to stabilize the system by grabbing both steady and sporadic power and regulating the DC link voltage. The purpose of supercapacitor is to reduce battery’s current stress.