Power Management Approach Research Articles

Energy‐Aware Power Control Scheme For Water Quality Monitoring Systems

ABSTRACTThis research introduces an innovative power management approach for wireless sensor networks in aquatic environmental monitoring. The study presents a dynamic algorithm that optimizes energy consumption in long range (LoRa) communication nodes by adaptively adjusting transmission power based on sensor–gateway distances. Leveraging GPS data and a log‐normal shadowing model, the method enables efficient power allocation. Field testing in an aquaculture setting demonstrates the energy‐aware power control (EAPC) algorithm's efficacy. Nodes at 500 m from gateways achieve up to 39% reduction in power consumption compared to fixed‐power systems. At 1250 m, savings decrease to 22%, and at 1500 m, to 11%, while maintaining reliable communication. This research advances sustainable aquatic resource management, offering applications in aquaculture, environmental conservation, and water resource management. By optimizing power usage, the approach contributes to more effective long‐term monitoring of aquatic environments.

Efficient power management strategies for AC/DC microgrids with multiple voltage buses for sustainable renewable energy integration

This study proposes a distinct coordination control and power management approach for hybrid residential microgrids (MGs). The method enhances the feasibility of hybrid MGs by reducing power loss on ILBCs. The MG has been modeled with solar and wind generators. The MG comprises multiple direct current (DC) and alternating current (AC) sub-microgrids (SMGs) with varying voltage levels. The coordination control and power management strategies for autonomous hybrid MGs with primary and secondary control levels. A novel technique is proposed to ensure seamless and precise power transfer among SMGs while minimizing the constant operation of ILBCs in islanded mode, with a focus on the secondary control level. The study uses MATLAB/Simulink to analyze on-grid, off-grid, and transient mode power transfer among MG. The MG has been operative during transient/faulty conditions. The results indicate that the proposed method demonstrates excellent adaptability in managing power flow.

Multi-zero pole dynamically compensated LDO with low quiescent current

This paper presents an LDO design tailored to the various power domains of CPUs. In contemporary CPU designs, different functional modules typically necessitate distinct voltage levels, thus requiring a flexible and adjustable power management approach. An LDO with an internally compensated super source follower (SSF) and a multi-zero-pole compensated loop stability is proposed. The SSF is used to reduce the output impedance of the LDO, enhancing its carry-under-load capability. The loop is then transformed into a multi-zero-pole system through Miller compensation, feed-forward compensation, and zero-pole tracking compensation techniques, enabling the LDO to achieve stability and reliability under different loads. The LDO proposed in this study was designed using a 110 nm CMOS process. The LDO can operate within a temperature range of −40–150 °C, with a low quiescent current of 57 μA. The power supply rejection ratio of the circuit is −22 dB at 1 MHz. When the load current jumps from 5 mA to 300 mA within 40 μs, the output voltage experiences an undershoot of 271 mV, an overshoot of 174 mV, and a maximum adjustment time of 100 μs. The load regulation is 0.00267 mV/mA, and the line regulation is 1 mV/V.

Estimating the Impact of a Recuperative Approach on the Efficiency of Thermoelectric Cooling

Thermoelectric cooling is a prospective technology that has a lot of advantages; however, its main drawback is its low efficiency compared to other technologies. A lot of scientific research is aimed at the improvement of the efficiency of thermoelectric cooling, including the development of new thermoelectric materials, innovative structures, and better power management strategies. The present work further explores a self-developed recuperative power management approach, which takes advantage of the thermoelectric element’s ability to work as an electrical generator. This study relied on the thermal–electrical analogy method to develop a model that is capable of describing the impact of recuperation on the cooling performance while preserving the simplest configuration possible. The influence of different variables was estimated by three suggested quantities for evaluating the gains, losses, and rationality of the recuperative approach. A recovery of up to 10% of the electrical energy supplied to the thermoelectric element was observed experimentally. The ratio between the recovered energy and induced heat losses did not exceed a factor of 0.9. It is concluded that the recuperation process is reasonable only in the case of unavoidable interruption of the cooling process when average-performance thermoelectric elements are used.

Novel adaptive observer for HVDC transmission line: A new power management approach for renewable energy sources involving Vienna rectifier

This paper proposes a novel approach to control and manage energy production based on grid requirements. The main challenge is the distance between the load and production area, making it difficult to quantify energy requirements in real time at the production area. To overcome this challenge, the proposed approach relies on an adaptive observer design that provides accurate and reliable estimates of multiple signals without expensive and unreliable sensors. In this study, a renewable energy source that consists of a wind turbine coupled to a permanent magnet synchronous generator is actuated with a Vienna power converter. However, it is crucial to emphasize that this approach can be implemented at any production site, regardless of its nature. The paper’s main contribution is the design of a novel adaptive high-gain observer that estimates the grid energy requirement based on the voltage value at the endpoint of the high-voltage direct current line. Moreover, the system load parameters are unknown and come non-linear in the system model. Simulations and analysis demonstrate the effectiveness of the proposed observer, showing convergence to the origin under the well-established condition of persistent excitation (PE).

A Review of Power Management Approaches for Mobile Ad Hoc Networks

A Review of Power Management Approaches for Mobile Ad Hoc Networks

Frequency Based Supervisory Power Management Approach for Battery-Supercapacitor Electric Vehicle Considering Road Slope

This article proposes a real time strategy based on frequency separation for energy management of battery/Supercapacitor (SC) based Hybrid Energy Storage System (HESS) in Electric Vehicle (EV). The performance and life cycle of battery are important parameters in determining the efficiency of EVs. With the aim of improving the effectiveness of battery in battery/SC based EVs, a supervisory Symlets wavelet based Energy Management System (EMS) using Fuzzy Controller (FC) is proposed which considers real time road slope and vehicle condition. Actual road grade values are computed from Contour Positioning System (CPS) using Google Earth software. Symlets wavelet control technique is utilised to separate the high frequency transients from the required load power. After separation of high frequency components, a FC is developed to provide an additional reference power for battery and SC. Then, the high frequency transients along with additional power obtained from FC are allocated to SC and the rest is handled by the battery. Three road slope conditions viz. city tour, downhill and uphill and two drive cycles viz. NYCC and UDDS are applied to examine the capability of proposed EMS. The proposed Fuzzy Symlets Wavelet based controller is observed to improve the battery Ampere hour (Ah) throughput and Root Mean Square (RMS) value of battery current in comparison to only Symlets wavelet based controller for city tour, uphill and downhill journeys. The results indicate the robustness and superiority of the designed Fuzzy Symlets Wavelet based control technique as compared to Symlets Wavelet technique in handling practical conditions and improving vehicle performance.

An adaptive power management approach for hybrid PV-wind desalination plant using recurrent neural networks

In hybrid Photovoltaic (PV)-wind desalination plants, power management is necessary because renewable energy sources such as PV and wind are highly variable. An optimal power management system also helps extend the equipment's life by preventing overloading or underloading of the system. This paper proposes a recurrent neural network (RNN) based power management for a desalination plant. In the proposed work, renewable energy sources like solar and wind are utilized to power the reverse osmosis (RO) desalination unit. The developed RNN model optimizes renewable energy sources while maintaining a stable function of the desalination process. The RNN power management module utilizes historical data to predict the power generation of the PV and wind sources and then adjusts the system's output to meet the power demand of the desalination plant while considering the limitations of the RO unit and the water profile requirements. The developed model was implemented in MATLAB tool, and the results are estimated. Simulation outcomes demonstrate that the developed model improves the hybrid system's performance in terms of resource and battery storage utilization and minimizes energy loss.

An adaptive dynamic power management approach for enhancing operation of microgrid with grid ancillary services

The increasing prevalence of photovoltaic (PV)-wind-battery-based microgrids and their integration into distribution networks have brought new challenges in grid ancillary services, such as poor power quality (PQ), grid instability, voltage and current fluctuations, and low efficiency of microgrids. These adverse effects are due to the intermittent nature of PV and wind power generation. The present work proposes an adaptive dynamic power management (ADPM) approach and enhanced instantaneous symmetrical component theory (EISCT) to improve the microgrid operation and grid ancillary services. The proposed ADPM-based EISCT not only utilizes a single grid side converter (GSC) but also reduces the sensor requirement and control structure complexity and thereby eliminates communication mismatch. The control structure, proposed in this research, has two major segments. The first segment focuses on active power management control through ADPM to facilitate efficient power flow between the utility grid and microgrid sources which helps in ensuring efficient power sharing. The second component is dedicated to synchronizing the renewable energy sources of the microgrid with the utility grid and simultaneously maintaining PQ compliance as per IEEE 519–2022 standards. The efficacy and adaptability of the proposed structure are validated through MATLAB®/Simulink® environment and the practical superiority of the system has been tested by using MicroLabBox® DS1202-based real-time hardware set-up. The experimental result shows improved performance of the developed structure under various microgrid operating conditions including dynamic loading situations, and battery system state-of-charge (SOC). The simulation and experimental results suggested that the DC-link ripples have been reduced to 2%, peak overshoot to 1.8%, settling time during the transition to 1.2s, battery response time to 0.3s, and the total harmonic distortion (THD) to less than 5%. These results demonstrated the superiority of the ADPM-based EISCT over conventional strategies.

Optimized Power Management Approach for Photovoltaic Systems with Hybrid Battery-Supercapacitor Storage

The paper addresses the ongoing and continuous interest in photovoltaic energy systems (PESs). In this context, the study focuses on an isolated photovoltaic system with hybrid battery-supercapacitor storage (HBSS). The integration of supercapacitors (SCs) in this system is particularly important because of their high specific power density. In photovoltaic (PV) systems, multi-storage systems use two or more energy storage technologies to enhance system performance and flexibility. When batteries and supercapacitors are combined in a PV system, their benefits are maximized and offer a more reliable, efficient, cost-effective energy storage option. In addition, effective multi-storage power management in a PV system needs a solid grasp of the energy storage technologies, load power demand profiles, and the whole system architecture. This work establishes a battery-supercapacitor storage system (HBSS) by combining batteries and supercapacitors. The primary objective is to devise a novel management algorithm that effectively controls the different power sources. The algorithm is designed to manage the charge and discharge cycles of the hybrid battery-supercapacitor energy storage system (HBSS), thereby guaranteeing that the state of charge (SOC) for both batteries and supercapacitors is maintained within the specified range. The proposed management algorithm is designed to be simple, efficient, and light on computational resources. It efficiently handles the energy flow within the HBSS, optimizing the usage of both batteries and supercapacitors based on real-time conditions and energy demands. The proposed method ensures their longevity and maximizes their performance by maintaining the SOC of these energy storage components within the specified limits. Simulation results obtained from applying the management strategy are found to be satisfactory. These results show that the proposed algorithm maintains the SOC of batteries and supercapacitors within the desired range, leading to improved energy management and enhanced system efficiency.

A deep reinforcement learning approach for power management of battery-assisted fast-charging EV hubs participating in day-ahead and real-time electricity markets

Publicly available electric vehicle charging hubs are expected to grow, to meet the increasing charging demand of EVs. A dominant class of these will be fast-charging hubs where the EVs will arrive for charging at all hours of the day, get the requested charge, and leave promptly. The profitability of these fast-charging hubs will be highly dependent on the variation of the day-ahead prices of electricity, volatility of the real-time power market, and the randomness of EV charging demand. The hubs can hedge against these uncertainties by committing power purchases in the day-ahead electricity market and by adopting dynamic real-time power management strategies. We develop a novel two-step methodology. The first step entails a mixed integer linear program (MILP) that assists the hubs in their day-ahead power commitment. The second step employs a Markov decision process (MDP) model that derives the real-time power management control actions. The MILP is solved using a commercial solver and the MDP is solved using a deep reinforcement learning algorithm. We demonstrate the effectiveness of our methodology for a fast-charging hub, housing 150 charging stations and a battery storage system, that operates in the Pennsylvania-New Jersey- Maryland interconnection (PJM) power grid.

A Novel Modular, Reconfigurable Battery Energy Storage System: Design, Control, and Experimentation

This paper presents a novel modular, reconfigurable battery energy storage system. The proposed design is characterized by a tight integration of reconfigurable power switches and DC/DC converters. This characteristic enables isolation of faulty cells from the system and allows fine power control for individual cells toward optimal system-level performance. An optimal power management approach is developed to extensively exploit the merits of the proposed design. Based on receding-horizon convex optimization, this approach aims to minimize the total power losses in charging/discharging while allocating the power in line with each cell's condition to achieve state-of-charge (SoC) and temperature balancing. By appropriate design, the approach manages to regulate the power of a cell across its full SoC range and guarantees the feasibility of the optimization problem. We perform extensive simulations and further develop a lab-scale prototype to validate the proposed system design and power management approach.

A study of power management approach in a grid connected hybrid renewable generation system

<p>Where sun irradiation is not continuous throughout the day, photovoltaic (PV) cells are used to supply solar power to the grid. A suitable power management plan is required to provide a steady power supply to the grid. The hybrid renewable generation system (HRGS) interconnected to the grid is the subject of this study, which provides a revolutionary power management method and control strategy. PV devices, a battery storage unit (BSU) charged by electricity generated from solar energy, and an auxiliary unit make up the HRGS concept (AU). Maximum power point tracking (MPPT) uses an improved perturb and observe (P&O) method to track maximal energy from solar irradiation. The peculiarity of this approach is that it uses a modified MPPT algorithm to handle power management throughout the process, allowing it to manage continuous power delivery to the grid based on load demand. It is capable of working under any load situation. The action of an L-C filter is utilized to decrease the total harmonic distortion (THD). The HRGS scheme's performance appraisals work correctly, achieve maximum productivity, and continually managing the power provided to the grid in a satisfactory way.</p>

Multimodal Power Management Based on Decision Tree for Internet of Wearable Things Systems

Precision medicine is now evolving to include internet-of-wearable-things (IoWT) applications. This trend requires the development of novel systems and digital signal processing algorithms to process large amounts of data in real time. However, performing continuous measurements and complex computational algorithms in IoWT systems demands more power consumption. A novel solution to this problem consists in developing energy-aware techniques based on low-power machine learning (ML) algorithms to efficiently manage energy consumption. This paper proposes a multimodal dynamic power management strategy (DPMS) based on the ML-decision tree algorithm to implement an autonomous IoWT system. The multimodal approach analyzes the supercapacitor storage level and the incoming biosignal statistics to efficiently manage the energy of the wearable device. A photoplethysmography (PPG) sensing prototype was developed to evaluate the proposed ML-DPMS programmed in a Nordic nRF52840 processor. The experimental results demonstrate an IoWT system’s low consumption of 25.74 J, and a photovoltaic solar power generation capacity of 380 mW. The proposed ML-DPMS demonstrates a battery life extension of 3.87×, i.e., 99.72 J of energy harvested, which represents the possibility to achieve at least 2.4× more data transmissions, in comparison with the widely used uniform power management approach. In addition, when the supercapacitor’s energy is compromised, the decision tree technique achieves a good energy conservation balance consuming in the same period of time 39.6% less energy than the uniform power approach.

Performance analysis of voltage sensorless based controller for two-stage grid-connected solar PV system

<a name="_Hlk98601430"></a><span lang="EN-US">In this research, a sensor free DC-link voltage controller based multifunctional inverter is proposed for a two-stage three-phase grid-connected solar photovoltaic (PV) system. First, the proposed scheme consists of an outer voltage controller requiring DC-link voltage knowledge, has the ability to manage power-sharing between the PV system and the utility grid, and second, has the ability of the VSI to act as a compensator for harmonics in the grid current polluted by non-linear loads. The suggested active power controlling grid reference current signal scheme replaces the standard controller, requiring the high voltage sensor to sense DC-link voltage, which is pricey, increasing the hardware complexity and reducing reliability. The performance of the proposed active power management approach is comparable to that of a conventional voltage sensor-based voltage controller. The effectiveness of the proposed method for two stage three-phase grid-connected solar PV system under fixed and variable irradiance feeding nonlinear loads is rigorously verified using MATLAB/Simulink software</span><span lang="EN-US">.</span>

A new power management strategy for plug-in hybrid electric vehicles based on an intelligent controller integrated with CIGPSO algorithm

This paper proposes an improved power management strategy for plug-in hybrid electric vehicles (PHEVs). This strategy consists of a long-term power management approach and a short-term intelligent controller. In the long-term power management, a chaotic improved generalized particle swarm optimization technique (CIGPSO) is used to optimize the motor and diesel engine torques. In order to reduce the computation time, a five-mode rule-based control system is employed, where the CIGPSO estimates the optimal values of the motor and engine torques in a hybrid mode, which manages the power between the motor and engine in accordance with a multi objective cost function. This cost function reduces fuel usage as well as the drawn current from the battery with taking into account the process of the battery aging. Moreover, the CIGPSO is able to obtain the state of charge (SoC) curve of the battery during the charging and discharging of the battery throughout the trip. The short-term controller is designed using an interval type-2 Takagi-Sugeno-Kang fuzzy (IT2TSK) algorithm which depends on human experts to overcome the uncertainties of the diverse driving conditions. Lyapunov stability theory for the online controller is achieved. The proposed strategy reduces the energy consumption compared to other strategies such as generalized PSO and improved multi-objective PSO algorithms. The simulation results are performed using real data for the engine, motor, and battery to demonstrate the flexibility, viability and effectiveness of the proposed approach with comparative results. Index terms—Plug-in hybrid electric vehicle, power management strategy, chaotic improved generalized particle swarm optimization algorithm, rule-based control, interval type-2 Takagi-Sugeno-Kang fuzzy algorithm.

Intelligent wild geese algorithm with deep learning driven short term load forecasting for sustainable energy management in microgrids

Energy management in power grids becomes essential to reduce the cost for the consumer and improve the power supply reliability. The microgrid is a vital part of the smart grid and it requires intelligent power management approach for effective functioning. Presently, delivering demand load and sustaining energy are two major challenges that exist in the power system. To resolve these problems, short-term load forecasting (STLF) models have been presented as an effective management and energy supply mode in power systems. The recently developed deep learning (DL) and machine learning (ML) models can be employed for accurate STLF in microgrids. In this view, this study presents an intelligent wild geese algorithm with deep learning driven short term load forecasting (IWGADL-STLF) model for sustainable energy management in microgrids. The proposed IWGADL-STLF model intends to accurately and rapidly predict the STLF in the microgrids. To accomplish this, the IWGADL-STLF model uses attention based Bi-directional long short term memory (ABiLSTM) model which involves the input parameters as formation of household and commercial load profiles with commercial load profile of the microgrid as output. The proposed IWGADL-STLF model identifies the behavioural patterns of parameters and models the behaviour in short time period for effective prediction process. Since hyperparameters play a vital role in the DL models, in this study, WGA is applied as a hyperparameter optimizer of the ABiLSTM model. The IWGADL-STLF approach has shown effective results with low MAE, MAPE, and R2 values. A comprehensive experimental analysis reported the enhanced performance of the presented model over the other existing approaches under several aspects.

Contribution to energy management of fuel cell/battery hybrid electric vehicles

The fuel cell electric vehicle is one of the promising prospects in this modern technical world for green transportation. It can really make our life easier and more economical if used with the same performance as that in recently used vehicles. This paper presents a techno-economic optimization model to perform the optimal sizing of fuel cell/battery hybrid electric vehicles. Optimum sizing has been applied to an electric vehicle consisting of a permanent magnetic synchronous machine, a proton-exchange membrane fuel cell, and lead acid battery storage. The optimal cost design and the new suitable power management approach are the main objectives. The optimal sizing of the proton-exchange membrane fuel cell/battery hybrid power for the vehicle is assessed on the basis of minimizing the cost of supplying energy from the two sources. An improved energy management algorithm is employed to decide the operating modes of the electric vehicles using the instantaneous values of the vehicle power, the pressure of the hydrogen tank, and the state of charge of the battery. The speed control of the system is accomplished by an proportional integral controller and also evaluates its efficiency. The obtained results are verified by carrying out MATLAB simulation using the New European Driving Cycle showing all the seven operating modes of the management algorithm. The simulation studies are conducted to verify the performance of the system under different conditions (acceleration and braking). The simulation results show the effectiveness and excellent performances of the proposed algorithm. These results show that this proposal is justified and examined more deeply and realized by a practical implementation.

Towards net-zero smart system: An power synergy management approach of hydrogen and battery hybrid system with hydrogen safety consideration

The building system is one of key energy consumption sector in the market, and low-carbon building will make a significant contribution for the worldwide carbon emission reduction. The multiple energy systems including renewable generations, hydrogen energy and energy storage is the perspective answer to the net-zero building system. However, the research gap lies in the synergy power management among the renewable, flexible loads, batteries and hydrogen energy systems, and at the same time, taking the unique characteristic of different energy sectors into account by power managing. This paper proposed the power management approach based on the game theory, by which, the different characteristics of the energy players are described via creating the competing relationship against net-zero emission objective so that to achieve the power synergy. Under the proposed power management method, the hydrogen and battery hybrid system including the fuel cell, electrolyzer and battery is designed and investigated as to unlock the power management regions and control constraints within the building system. Particularly, for the hydrogen system within the hybrid system, the safe and long-lifetime operation is considered respectively by high-efficiency and pressure constraints within the power management. Simulation results show that, providing the same energy storage services for the building system, the fuel cell with the proposed power management method sustains for 9.9 years, much longer than that of equivalent consumption minimization (4.98), model predictive control (4.61) and rule-based method (7.69). Moreover, the maximum tank temperature of the hydrogen tank is reduced by 3.4 K and 2.9 K compared with consumption minimization strategy and model predictive control. Also, the real-time of the proposed power management is verified by a scaled-down experiment platform.

Armenia in a Triangle of Great Power Management: Regional competition on the Nagorno-Karabakh conflict

The aim of the article is to examine the foreign policy approaches and interests of three regional powers – Iran, Russia and Turkey before the Second Karabakh war in 2020. The study argues that dissatisfaction with the results of the conflict, forced these countries to further advance their interests in the region through cooperation and regional influence by soft power rather than waging an unpredictable geopolitical confrontation. The study is based on the methods of foreign policy analysis and comparative analysis of political discourse of the three regional powers in application to the Nagorno-Karabakh conflict using the Great Power Management (GPM) approach of the English School of International Relations. The study puts forward the idea that the predominance of the national interests of Russia, Turkey and Iran, disguised with ideational concepts in the framework of the GPM is fundamentally different from the principles of multilateralism, driven by the European Union and the West within the liberal paradigm of resolving the Nagorno-Karabakh conflict.