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The miniaturization of electronic devices without compromising their heat dissipation capacities is the main concern due to the rapid evolution in power industries and engineering fields. The conventional methods of cooling or heating the devices are changed and old tactics of using conventional fluids for heat dissipation are replaced with nanofluids of strong thermal efficiency. In the present context, the experimental as well as theoretical studies of nanofluids (Cu–H2O/Al2O3–H2O) flow inside the wavy and microchannels are elucidated and discussed for different physical conditions. It is found that the use of Cu–H2O/Al2O3–H2O nanofluid improves the thermal efficiency of heat exchangers. The complex shapes and sizes of heat exchangers such as multilayer heat exchangers, heat exchangers with twisted and square shapes and multijet heat exchangers are considered effective coolants as compared with straight microchannel heat exchangers. The use of Cu–H2O/Al2O3–H2O nanofluids improves the overall heat transfer efficacy of electronic devices, and it is considered a promising coolant for various applications including aerospace (spacecraft and satellites), automobile (cooling the engines and power management in electric vehicles), renewable energy (solar plants), microelectronic devices (heat dissipation through the microprocessor and cooling the other components of devices) and modern heat exchangers of engineering domains. [ABSTRACT FROM AUTHOR]

This paper reviews and summarizes recent progress in blue energy harvesting based on a triboelectric nanogenerator (TENG). This review covers TENG-based blue energy harvesters (BEHs) with different inertial units in spherical structures, derivative spherical structures, buoy structures, and liquid–solid contact structures. These research works have paved the way for TENG-based BEHs working under low-frequency waves and harvesting wave energy efficiently. The TENG-based BEH unit design and networking strategy are also discussed, along with highlighted research works. The advantages and disadvantages of different TENG structures with other inertial units are explored and discussed. Meanwhile, power management strategies are also mentioned in this paper. Thus, as a promising blue energy harvesting technology, the TENG is expected to significantly contribute to developing low-cost, lightweight, and high-performance BEHs supporting more frequent marine activities. [ABSTRACT FROM AUTHOR]

Returning monarch butterflies (Danaus plexippus) to sustainable levels of abundance will require an array of contributors to protect and restore habitat over broad areas. Due to the diversity and scale of land managed by electric power companies across the monarch range, plus an additional 32 million hectares needed for new solar arrays by 2050 to meet renewable energy goals, the industry may have potential to contribute to monarch conservation. However, it is challenging to clearly understand an individual company's potential for monarch conservation because of the scale and distribution of their specific land assets (ranging from 4,800 to 240,000 hectares in this study alone), the complexity of monarch science, and the lack of a science-based approach for evaluating large land assets for monarch habitat. With monarchs potentially being protected under the United States Endangered Species Act in the future and thereby limiting land management approaches, there is interest from electric power companies to understand how their lands relate to monarchs. In collaboration with companies, we developed a GIS-based model to identify company landholdings that contain high-quality monarch habitat and applied the model to specific landholdings of eight power companies in the United States. We then facilitated discussions with company teams to balance conservation goals, corporate risk, and social opinion. This paper describes non-confidential results for developing a national GIS-based monarch habitat model and applying it to electric power companies who are considering monarch conservation while simultaneously transitioning to a new clean energy future. The model and applied experience may be useful for other organizations working across large landscapes to manage monarchs. [ABSTRACT FROM AUTHOR]

This paper presents a cutting-edge Sustainable Power Management System for Light Electric Vehicles (LEVs) using a Hybrid Energy Storage Solution (HESS) integrated with Machine Learning (ML)-enhanced control. The system's central feature is its ability to harness renewable energy sources, such as Photovoltaic (PV) panels and supercapacitors, which overcome traditional battery-dependent constraints. The proposed control algorithm orchestrates power sharing among the battery, supercapacitor, and PV sources, optimizing the utilization of available renewable energy and ensuring stringent voltage regulation of the DC bus. Notably, the ML-based control ensures precise torque and speed regulation, resulting in significantly reduced torque ripple and transient response times. In practical terms, the system maintains the DC bus voltage within a mere 2.7% deviation from the nominal value under various operating conditions, a substantial improvement over existing systems. Furthermore, the supercapacitor excels at managing rapid variations in load power, while the battery adjusts smoothly to meet the demands. Simulation results confirm the system's robust performance. The HESS effectively maintains voltage stability, even under the most challenging conditions. Additionally, its torque response is exceptionally robust, with negligible steady-state torque ripple and fast transient response times. The system also handles speed reversal commands efficiently, a vital feature for real-world applications. By showcasing these capabilities, the paper lays the groundwork for a more sustainable and efficient future for LEVs, suggesting pathways for scalable and advanced electric mobility solutions. [ABSTRACT FROM AUTHOR]

In 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. [ABSTRACT FROM AUTHOR]

In this study, the thermal runaway (TR) suppression test platform of battery modules was built based on the real power battery pack to reduce the thermal runaway propagation (TRP) of electric vehicle power battery packs. The inhibition effect of different fire extinguishing devices on TRP of ternary lithium-ion battery modules was experimentally investigated. Big scale tests yielded combustion behaviors, TRP intervals, peak temperatures, cooling efficacy of extinguishants, heat transfer, and fire traces of the battery modules. Notably, perfluorohexanone application prolonged the TRP by 427 s relative to the blank control experiment, reducing the cell casing temperature to 267 ℃, although it did not fully arrest TRP between cells. However, water mist application effectively halted inter-cell TRP, ensuring post-spray temperatures at thermocouple sites remained below 100 ℃ and reduced heat transfer best. These findings deliver crucial experimental insights that bolster the development of fire extinguishing strategies for the safety management of electric vehicle power battery assemblies. [ABSTRACT FROM AUTHOR]

Biomedical implantable devices like deep brain stimulators, implantable cardioverter-defibrillators and cardiac pacemakers are essential for treating the human heart and brain-related diseases. In the past few decades, a considerable amount of research has focused on improving bio-implant technologies. Conventional bio implant devices consist of an external generator like a battery to power the system, which requires replacement after a particular time. Therefore, in recent years, self-powered implants with various energy harvesting techniques have been proposed to avoid frequent surgery for battery replacement and to miniaturise the implant systems. However, the research communities have yet to explore all the limitations and possibilities of improvement on such energy-scavenging technologies, especially when the application is in vivo. Several aspects of recent developments in energy harvesting technologies feasible for biomedical implantable devices are reported systematically. A detailed review of piezoelectric energy harvester mechanism and miniaturisation, electric output and power management and biocompatibility of an energy harvester for implantable medical devices in vitro and in vivo environments. Furthermore, the piezoelectric energy harvester's durability, packaging material, connection and evaluation criteria are discussed. [ABSTRACT FROM AUTHOR]

This manuscript presents an optimal control system for energy management of the hybrid energy storage system (HESS) as battery and super capacitor (SC) on electric vehicles (EVs). The proposed system is parallel execution of levy flight with Tunicate swarm optimization. The proposed method is improved by levy flight distribution; hence, it is named Improved Tunicate swarm optimization (ITSA). Here, the HESS method calculates the super capacitor reference voltage in terms of load dynamics and improves the power. At first, compute the super capacitor reference voltage assuming real-time load dynamics. Furthermore, diminish the range of battery power magnitude and power loss concurrently. In the proposed system, the Improved Tunicate swarm optimization is merged for generating the probable HESS control signal data set. Furthermore, the proposed method gains the super capacitor voltage, amount of battery current, variation of battery current. Through the proposed method, the hybrid energy storage system parameters are enhanced, and the proposed system gives a reliable solution. The proposed method is performed on the MATLAB/Simulink work platform. The quality of a hybrid energy storage system is compared with that of other methods. [ABSTRACT FROM AUTHOR]

This paper studies the precise clock management technology for the power consumption information collection system which demands precise requirements of the electric power meter clock. The causes of the clock deviation of the collection system and the problems caused by the clock deviation are analysed first; Then the key technologies for achieving accurate time synchronization are explained; Next, the master station and terminal time synchronization algorithm is designed. Only two NLP synchronizations are required to obtain a high accuracy time deviation, and the accuracy of the algorithm is demonstrated through a test of an example. Finally, the electric power meter time adjustment scheme using a combination of broadcast time adjustment and ESAM point-to-point time adjustment is designed to achieve high accuracy and low error, which has high reliability and practicality. [ABSTRACT FROM AUTHOR]

This research work is designed for the management of the electric power of an autonomous hybrid system which generally integrates several subsystems, whose main source of production is solar energy (photovoltaic panels) coupled with a hydrogen fuel cell using a storage device (lithium battery). This energy coupling behavior is used in a wide range of operating conditions ensuring the originality of the exploitation of the energy produced to supply electricity to remote regions and isolated urban regions of southern Algeria, which will be modeled by a recent graphic formalism methodology macroscopic energy representation and controlled by a simple method the maximum control structure that takes into account all the inputs and outputs of the system. This hybrid system is controlled by an energy management strategy by acting on a common continuous bus with variable residential load via a DC/DC converter, allowing control of the amount of energy between the different energy resources to minimize the use of the fuel cell from which to minimize hydrogen consumption. Another is used to maintain the voltage of this bus at its reference via the battery by regulating the bidirectional DC/DC converter. • The HES is intended to supply electricity to remote urban areas of southern Algeria. • This energy coupling behavior is used in a wide range of operating conditions. • The HES modeled by the recent graphic formalism MER and controlled by the MSC. • MER uses MCS, designed to control inputs/outputs influencing the behavior of HES. • The effectiveness of this device in minimizing the consumption of hydrogen. [ABSTRACT FROM AUTHOR]

Accuracy of battery charge status (SOC) estimation plays a significant role in the management of electric vehicle power batteries. However, recently, abrupt changes from SOC data often occurs in the actual operation of electric vehicles and some errors appear in the establishment of battery models and noise models, which give rise to the poorly adaptive and robust performance of traditional algorithms in the process of SOC estimation. The fusion algorithm proposed in this paper can effectively improve the accuracy of models and SOC estimation of lithium-ion batteries. Based on the second-order R-C network model, this method optimizes the accuracy of parameter identification by adopting the adaptive recursive weighted least square algorithm (ARWLS). In addition, the adaptive fading extended Kalman filter algorithm (AFEKF) is applied to estimate the SOC of lithium-ion batteries. Additionally, via introducing a fading factor, the optimal Kalman gain is updated in real-time, which can reduce the impact of data mutation on battery modeling. Compared with the offline AEKF algorithm and the EKF algorithm, the adaptive recursive weighted least square-adaptive fading extended Kalman filter (ARWLS-AFEKF) fusion algorithm had higher accuracy and adaptability, which can be adapted to the variable noise environment. [ABSTRACT FROM AUTHOR]

This study aims to develop statistical and machine learning methodologies for forecasting yearly electricity consumption in Saudi Arabia. The novelty of this study include (i) determining significant features that have a considerable influence on electricity consumption, (ii) utilizing a Bayesian optimization algorithm (BOA) to enhance the model's hyperparameters, (iii) hybridizing the BOA with the machine learning algorithms, viz., support vector regression (SVR) and nonlinear autoregressive networks with exogenous inputs (NARX), for modeling individually the long-term electricity consumption, (iv) comparing their performances with the widely used classical time-series algorithm autoregressive integrated moving average with exogenous inputs (ARIMAX) with regard to the accuracy, computational efficiency, and generalizability, and (v) forecasting future yearly electricity consumption and validation. The population, gross domestic product (GDP), imports, and refined oil products were observed to be significant with the total yearly electricity consumption in Saudi Arabia. The coefficient of determination R 2 values for all the developed models are >0.98, indicating an excellent fit of the models with historical data. However, among all three proposed models, the BOA–NARX has the best performance, improving the forecasting accuracy (root mean square error (RMSE)) by 71% and 80% compared to the ARIMAX and BOA–SVR models, respectively. The overall results of this study confirm the higher accuracy and reliability of the proposed methods in total electricity consumption forecasting that can be used by power system operators to more accurately forecast electricity consumption to ensure the sustainability of electric energy. This study can also provide significant guidance and helpful insights for researchers to enhance their understanding of crucial research, emerging trends, and new developments in future energy studies. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have shown promise in providing ancillary services, e.g., frequency regulation. This is mainly due to their capacities and fast response. On the contrary, the rapid integration of EVs in the grid poses challenges, such as frequency and voltage stability. In order to mitigate the above-mentioned issues, several dispatching strategies have been introduced in the recent literature to optimize the charging/discharging rates of EVs. In this paper, a comparative study of power management strategies for secondary frequency regulation (SFR) employing a fleet of EVs is presented. A hierarchical control scheme is employed to compare two cases, namely control at the charging station (CS) level and novel control at the EVs level. Under both cases, a multi-objective optimization approach is utilized to define the optimal charging and discharging rates of EVs using a pattern search algorithm. Furthermore, the performance of the two models is experimented under contingency cases, a notable contribution of this study. Finally, simulations are carried out using OPAL-RT real time simulator to validate the performance of the two models based on real-time traces obtained from Pennsylvania, New Jersey, and Maryland (PJM) interconnection and California independent system operator (CAISO). To further validate the proposed model, a comparison with a mixed-integer linear programming (MILP) based model is presented. [ABSTRACT FROM AUTHOR]

Tower state recognition is of great importance in smart electric power management. Most previous efforts were devoted on the guyed towers and tower structures or materials to monitor the structural stability of towers through sensors fixed on them. Actually, identifying their states as a whole is also needed for remote management. This study addresses the task of tower state recognition in high-resolution remote-sensing images under the framework of deep learning. To achieve this goal, a new remote-sensing image dataset is constructed. Technically then, an ensemble learning strategy is developed to classify the states of the transmission towers. Specifically, MobileNetV3 is taken as the weak classifier as it is simple yet parameter-light in the family of convolutional networks. Along the line of ensemble learning with the principal of Adaboost in machine learning, an Adaboost-style network paradigm, named as Adaboost-MobileNetV3 in this paper, is finally proposed to recognize the states of towers. To evaluate its effectiveness, a series of deep convolutional networks are compared with the proposed network. The extensive comparative experiments indicate that the proposed Adaboost-MobileNetV3 network is suitable for practical tower state recognition, which achieves about 90% accuracy with at least about 4% higher than these compared models. [ABSTRACT FROM AUTHOR]

The current paper is the result of research program dedicated to designing new tools and instruments in designing and optimizing electric power networks management, and training for creating interaction between energy utilities providers and user behavior. We implemented a SCADA system for energy management and control, then we integrated a micro grid system with user interaction specific tools. On the current implementation we measured the training skills delivery results according with Scottish Credit and Qualifications Framework for creating the frame of acceptance and interaction for an efficient energy management. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Energy conservation is a key challenge for a sustainable lifestyle. However, it is often ignored because of a lack of knowledge and inability to predict the lifetime of electric or electronic equipment. Energy conservation is the most costeffective strategy for coping with energy shortage. This study proposes the implementation of e-Life devices to solve this problem and promote public awareness. Electricity-intensive products have their own power specifications and lifespans; over time, the amount of power they use is greater than expected because of the effects of aging. The e-Life algorithm used in an Internet of Things (IoT)-enabled electronic device is integrated into any electrical load to monitor power usage and understand the product replacement time. It is proposed to be aware of the excessive power consumption of all-electric or electronic equipment. [ABSTRACT FROM AUTHOR]

Copyright of China Forestry Economy is the property of China Forestry Economy Magazine Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It has been a challenge for electric power management to automatically extract power lines from LiDAR point clouds. However, environmental and technical issues have made management more challenging in complicated areas where power lines are in close proximity to buildings and/or trees. In this study, the structure and types of the data captured by a LiDAR sensor in regions containing line corridors were analysed. The crucial stage is appropriately identifying from the data the essential parts of a power line corridor route. The point cloud dataset used in the study belongs to the Borssele region in Zeeland, the Netherlands. By manually labelling the dataset, three classes were identified: wire, pylon, and others. For the classification of point clouds, the Random Forest method was utilised. To assess the obstacles posed by the class wire, 5 m, 10 m, and 15 m 3D buffer zones are created. The visual presentation of obstacles within the buffer zone is achieved by assigning them a separate class code and indicating that they are inside and partially within. Based on the results, the correctness values of the classes of wire and others are considered to be satisfactory. However, the class pylon contains points with incorrect labels after the classification. As a result, the accuracy of the pylon class is much lower than the accuracy of the other two classes. [ABSTRACT FROM AUTHOR]

Multistep power consumption forecasting is smart grid electricity management's most decisive problem. Moreover, it is vital to develop operational strategies for electricity management systems in smart cities for commercial and residential users. However, an efficient electricity load forecasting model is required for accurate electric power management in an intelligent grid, leading to customer financial benefits. In this article, we develop an innovative framework for short-term electricity load forecasting, which includes two significant phases: data cleaning and a Residual Convolutional Neural Network (R-CNN) with multilayered Long Short-Term Memory (ML-LSTM) architecture. Data preprocessing strategies are applied in the first phase over raw data. A deep R-CNN architecture is developed in the second phase to extract essential features from the refined electricity consumption data. The output of R-CNN layers is fed into the ML-LSTM network to learn the sequence information, and finally, fully connected layers are used for the forecasting. The proposed model is evaluated over residential IHEPC and commercial PJM datasets and extensively decreases the error rates compared to baseline models. [ABSTRACT FROM AUTHOR]

In recent years, the marketing data sources of enterprises are not sufficient, and the quality of data cannot be guaranteed. In addition, although the data has the characteristics of "large amount," there is a lot of data garbage in it, and the amount of valuable data is not large. Taking electric power enterprises as an example, electric power marketing is the key of power supply enterprises, and its development determines the survival and development of enterprises to some extent, which is of great significance to power supply enterprises. Big data (BD) not only brings many positive effects to the marketing of electric power enterprises but also brings some negative effects to electric power enterprises. Based on the background of BD, this paper studies the integration and innovation path of marketing data management in electric power enterprises. The research shows that the accuracy rate of this method is the highest, followed by machine learning, and finally AI. With the increase of the number of samples, the accuracy of this method becomes higher and higher. When the number of samples reaches 13, the accuracy of this method can reach 85%, that of machine learning is 56%, and that of AI is 43%. It can be seen that this method is more suitable for data management integration of electric power marketing. According to the characteristics of the era of BD, electric power enterprises should innovate marketing data management system, construct efficient and perfect data processing flow, fully tap the potential value of data, and make contributions to the development of electric power enterprises. [ABSTRACT FROM AUTHOR]

One of the priority objectives of microgrids is to achieve energy self-sufficiency, generally resorting to distributed generation sources and backup systems; however, they are usually connected to conventional electrical networks that ensure supply to the loads. Addressing this problem, this work presents a proposal (managing elements) to minimize the dependence of power from the external electrical system in the months of greatest demand and thus guarantee the supply of the other months. The proposed methodology compares two statistical techniques: central composition design with 20 simulated experimental replicas and regression with 28. In both cases, the monthly average purchased power is analyzed as a primary response and its standard deviation as a secondary. The study variables are seven photovoltaic arrays and the feed characteristics of the turbine-generator storage of the microgrid of the Center for Development of Renewable Energies (CEDER), belonging to the Center for Energy Research, Environmental and Technological (CIEMAT). The results, with high predictive quality supported by indexes of approach to the real values of solar radiation and the operation of the turbine-generator binomial, provide regions where CEDER has the possibility of increasing the capacities of solar systems and/or modifying the geometry of the mini-hydraulics supply according to your specific conditions. [ABSTRACT FROM AUTHOR]

The selection, sizing, and management of energy storage system for an electric vehicle (EV) are critical while considering later's large-scale deployment. The complementary nature of battery and ultracapacitor (UC) makes the combination a suitable hybrid energy storage system (HESS) for EV since the peak to average power demand ratio is very high especially for an urban drive commonly characterized by frequent acceleration and deceleration. A suitably sized UC combined with the battery pack provides necessary high power density as well as high energy density. The HESS makes the EV more effective with improved dynamic response, higher kilometers per charge, and longer battery life. This paper presents a fuzzy logic control based power management strategy to employ UC to relieve the battery of peak discharge currents as well as rapid charging currents. The power conditioning unit limits the battery current as well as its rate of change by maintaining the UC state of charge in the desired limits. The paper presents some of the key simulation and experimental results for a prototype EV system tested for various modes of operations. [ABSTRACT FROM AUTHOR]

Due to the hierarchical management structure of electric power enterprises, data flow often needs to go through multiple layers of nodes for review, which makes data synchronization slow and distortion high and data reliability difficult to improve. The existing data asset management mode of electric power enterprises faces security problems such as node trustworthiness and data tampering in the process of identity mutual trust, transmission and storage, data collection and data usage. In view of this, this paper proposes a blockchain-based digital asset management framework using the decentralized, untrustworthy, and hard-to-tamper features of blockchain. In the proposed framework, we optimized security authentication mechanism, data recording and consensus process, and data storage and transmission strategy involved in blockchain data sharing applications. The framework can effectively ensure the security, integrity, operability and consistency of digital assets in the management process under the blockchain environment. [ABSTRACT FROM AUTHOR]

A power management framework for hybrid electric vehicles (HEVs) is proposed based on deep reinforcement learning (DRL) with a Long Short-Term Memory (LSTM) network to minimize the fuel consumption through determining the power distribution between the two propulsion sources, the internal combustion engine (ICE) and the electric motor (EM). DRL is effective for handling the high-dimensional state and action spaces in the HEV power management problem, and the LSTM structure leverages temporal dependencies of input information, providing internal state predictions automatically without introducing extra state variables. This technique is entirely online, meaning that the framework is constructed in real time during the training phase, independently of a prior knowledge of driving cycles. The learned information stored in the LSTM network is utilized efficiently, and the computational speed is enhanced by making multiple predictions simultaneously in each step. Simulation over various driving cycles demonstrates the efficacy of the proposed framework in fuel economy improvement. [ABSTRACT FROM AUTHOR]

Risk management of electric power transmission lines requires knowledge from different areas such as the environment, land, investors, regulations, and engineering. Despite the widespread availability of databases for most of those areas, integrating them into a single database or model is a challenging problem. Instead, in this paper, we propose a novel method to calculate risk probabilities on the implementation of transmission lines based on unstructured text from a single source. It uses the Brazilian National Electric Energy Agency's (ANEEL) weekly reports, which contain decisions about the electrical grid comprising most of the aforementioned areas. Since the data are unstructured (text), we employed NLP techniques such as stemming and tokenization to identify keywords related to common causes of risks provided by an expert group on energy transmission. Then, we used models to estimate the probability of each risk. This method differs from previous works, which were based on structured data (numerical or categorical) from single or multiple sources. Our results show that we were able to extract relevant keywords from the ANEEL reports that enabled our proposed method to estimate the probability of 97 risks out of 233 listed by an expert. [ABSTRACT FROM AUTHOR]

This paper proposes an enhanced three‐layer predictive hierarchical power management framework for secure and economic operation of islanded microgrids. The tertiary control, guaranteeing the microgrid economic operation, is built upon the semi‐definite programming‐based AC optimal power flow model, which periodically sends power references to secondary control. To mitigate uncertainties arising from renewable generations and loads, a centralized linear model predictive control (MPC) controller is proposed and implemented for secondary control. The MPC controller can effectively regulate the microgrid system frequency by closely tracking reference signals from the tertiary controller with low computational complexity. Droop‐based primary controllers are implemented to coordinate with the secondary MPC controller to balance the system in real time. Both synchronous generators (SGs) and solar photovoltaics (PVs) are simulated in the microgrid power management framework. A unified linear input‐state estimator (ULISE) is proposed for SG state variable estimation and control anomaly detection due to compromised cyber‐physical system components, etc. Simulation results demonstrated that SG states can be accurately estimated, while inconsistency in control signals can be effectively detected for an enhanced MPC. Furthermore, comparing with conventional proportional‐integral (PI) control, the proposed hierarchical power management scheme exhibits superior frequency regulation capability whilst maintaining lower system operating costs. [ABSTRACT FROM AUTHOR]

The electricity demand has grown continuously in recent years, raising the necessity to expand generation sources, distribution networks, and equipment efficiency. In addition, it is necessary to attend sustainable development in a conciliated manner. Applications involving the intelligent management of the distributed networks have increased to achieve a balance between growth and sustainability. In this context, this article presents the development of an Intelligent Electric Power Management System (IEPMS) for the economic maximisation of a photovoltaic system applied to a prosumer residential unit without storage in Brazil. Using historical meteorological data and a heuristic to simulate energy use habits, the IEPMS forecasts both generation and demand in 24 hours. From the projections, an optimisation problem was built and solved using the genetic algorithm technique to find the most economical moments for driving loads. This model aims to reach the lowest daily cost of electricity, considering the return (sale) of unused energy to the power distribution company. The validation of the IEPMS considered four usage patterns, integrating 26 scenarios, those composed by the (i) flexibility; (ii) type of tariff; and (iii) hit rates provided by the climate forecasting method proposed for the system. As a result, the IEPMS savings considering the white tariff were 34.72% for one year, assuming full-time external work usage. Additionally, it was possible to identify in all scenarios that the proposed method’s performance was not less than 97%, measured through the relative error among distinct hit rates of the evaluated climatic forecast.

Electric vehicles and hybrid electric vehicles are revolutionizing the modern transport system and transition to electric vehicle comes with its challenges. The power management of the electric vehicles needs to be an efficient one in order to bring electric vehicles to the mainstream and project them as a viable alternative to the conventional fuel based vehicles. This work presents the power management strategy for plug-in hybrid electric vehicles and electric vehicles using intelligent techniques. The technique uses the fuzzy logic controller for the energy optimization. The fuzzy logic controller calibrates the battery charging process in conformation with the real time power demand, input power of the vehicle speed, real time battery state of charge and motor power. The fuzzy inference system with appropriate set of rules dictates the value of fuzzified linguistic variable. This work is realized using MATLAB-SIMULINK environment and simulations depict the behavior of the battery state of charge and the output power response of the hybrid electric vehicle. [ABSTRACT FROM AUTHOR]

This study describes the effectiveness of applying Fuzzy controller into the renewable energy storage system for better efficiency. Different small size power generators need to be initially started up in order to fulfil the demand of power at the rush hours. V2G (Vehicle to grid) technology is an interface of bi-directional electrical grid which allows electric vehicles to take energy from the mesh. The calculation for the power requirement of the battery is initiated for identifying the charging load. When it comes to power management for the electric vehicles (EVs), information technology plays a vital role for the V2G framework. The delivery of power request to the aggregate of EVs through the aggregator is done with the help of sending a signal from the RTO/ISO. The load is generated with the help of huge as well as continuously running units of power generation and gradable EVs. In V2G optimization, lowering the overall operational cost may be achieved by using wind power as well as using the grid. [ABSTRACT FROM AUTHOR]

Electricity is one of the critical role players to build an economy. Electricity consumption and generation can affect the overall policy of the country. Such importance opens an area for intelligent systems that can provide future insights. Intelligent management for electric power consumption requires future electricity power consumption prediction with less error. These predictions provide insights for making decisions to smooth line the policy and grow the country's economy. Future prediction can be categorized into three categories, namely (1) Long-Term, (2) Short-Term, and (3) Mid-Term predictions. For our study, we consider the Mid-Term electricity consumption prediction. Dataset provided by Korea Electric power supply to get insights for a metropolitan city like Seoul. Dataset is in time-series, so statistical and machine learning models can be used. This study provides experimental results from the proposed ARIMA and CNN-Bi-LSTM. Hyperparameters are tuned for ARIMA and neural network models to increase the models' accuracy, which looks promising as RMSE for training is 0.14 and 0.20 RMSE for testing. [ABSTRACT FROM AUTHOR]

Single input multiple output (SIMO) buck converter is a good solution to enhance the area efficiency of the power management unit. Because the SIMO type buck converter uses only one inductor to buffer the transferred energy for the multiple output ports, it usually suffers from severe transient cross regulation in a continuous conduction mode. Most of the well‐known approaches to suppress the transient cross regulation are either to create the additional free‐wheeling loop or to refine the controller loop. The control logic is usually quite specific which may not apply to the other controller types controller. A phase sequence interchange (PSI) scheme is proposed to mitigate the transient cross regulation without modifying the original control design. Compared to the other techniques, the proposed method can be easily applied, but not limited to the compensator‐based control or the non‐compensator‐based control in the buck converter. This study starts with the illustration of the reason why the PSI can mitigate the transient cross regulation. Detail designs are then presented to implement the proposed scheme on both the compensator type and the non‐compensator type controls. Experimental results show that the suppression of the cross regulation is comparable to the other state of the arts. [ABSTRACT FROM AUTHOR]

Knowledge of drivers' mobility patterns is useful for enabling context-aware intelligent vehicle functionality, such as route suggestions, cabin preconditioning, and power management for electric vehicles. Such patterns are often described in terms of the Points of Interest (PoIs) visited by an individual. However, existing PoI extraction methods are general purpose and typically rely on detecting periods of low mobility, meaning that when they are applied to vehicle data, they often extract a large number of false PoIs (for example, incorrectly extracting PoIs due to stopping in traffic), reducing their usefulness. To reduce the number of false PoIs that are extracted, we propose using features derived from vehicle signals, such as the selected gear and status of doors, to classify candidate PoIs and filter out those that are irrelevant. In this paper, we (i) present Activity-based Vehicle PoI Extraction (AVPE), a wrapper method around existing PoI extraction methods, that utilizes a postclustering classification stage to filter out false PoIs, (ii) evaluate the benefits of AVPE compared to three state-of-the-art general purpose PoI extraction algorithms, and (iii) demonstrate the effectiveness of AVPE when applied to real-world driving data. [ABSTRACT FROM AUTHOR]

Intelligent analysis is used to process the structure of a wireless sensor network (WSN) and produce some information that can be used to improve the performance of the WSN's management applications. This paper introduces a new approach for wireless sensor networks which is based on neural networks. The presented work is an intelligent method based on the existing concept of multi-agent systems for WSN management. The proposed work shows a performance improvement. Wireless sensor networks need to be managed in different ways, e.g. power consumption of each sensor, efficient data routing without redundancy, control of data reading and sending intervals, etc. The random distribution of wireless sensors, the numerous variables that affect the operation of the WSN, and the uncertainty of various algorithms (for example, self-localization of sensors) make the WSN fuzzy. Given this fuzzy nature and numerous details, a neural network is an ideal tool to use to cover these details, which are so difficult to detect and explicitly, model. This paper presents a neural network-based approach, which results in more efficient routing path discovery and sensor power management. [ABSTRACT FROM AUTHOR]

The objective of this work is to develop a realistic control technique for stably regulating the power output of a photovoltaic array. The associated process is referred to as the regulated power point tracking (RPPT). Ideally, the converter duty ratio is expected to be a continuous variable so that the photovoltaic power output can be freely adjusted. However, such a continuous‐mode RPPT is possible only with the help of an analogue pulse‐width‐modulation (PWM) control. In contrast, the modern digital PWM control causes a duty ratio quantisation. As a result, the photovoltaic power output can, practically, be adjusted only at some discrete power levels. Therefore, existing RPPT control techniques, which optimistically assume a continuous power production range, may lead to persistent low‐frequency power oscillations because of the digital PWM. To overcome the particular problem, a quantum‐mode RPPT control technique is proposed in this paper. The quantum‐mode RPPT basically limits its search only to the practically attainable discrete power levels by suitably modifying the given power reference command. Thus, the power tracking can always reach a converged state. For the faster RPPT, the perturbation step size is dynamically adapted. The proposed RPPT control technique is verified via both simulations and hardware experiments. [ABSTRACT FROM AUTHOR]

The progress of the social economy and the rapid development of the power field have created more favorable conditions for the construction of my country's power grid. In this network age, how to further realize the connection between the power system and the Internet of Things is the key content of many scholars' research. In the Internet of Things environment, there have been many excellent results in the collection, storage, and management of electric power big data, but the problem of information security has not been completely solved. Based on big data analysis and Internet of Things technology, this paper studies the architecture design of power information security terminals. In view of the diverse types of power grid mobile information and the large amount of data, this paper designs a power transportation mobile information security management system structure, which improves the effective management of power data by the system through big data, smart sensors, and wireless communication technology. According to the experiment, the power information security terminal constructed in this paper can effectively reduce communication resources and save communication costs in the process of aggregating multidimensional data. In the user satisfaction survey, residents' satisfaction with the convenience and safety of the intelligent power system is also as high as 9.312 and 9.233. On the whole, the application of big data and Internet of Things technology to the construction of power information security terminals can indeed improve the service efficiency of power companies under the premise of ensuring safety and allow users to have a better experience. [ABSTRACT FROM AUTHOR]

Summary: This article presents a power management system (PMS) designed for smart homes aiming to deal with the new challenges imposed by the proliferation of plug‐in electric vehicles (EVs) and their coexistence with other residential electrical appliances. The PMS is based on a hybrid wireless network architecture composed by a local hub/gateway and several Bluetooth Low Energy (BLE) and Wi‐Fi sensor/actuator devices. These wireless devices are used to transfer information inside the smart home using the Message Queuing Telemetry Transport (MQTT) protocol. Based on the proposed solution, the current consumption of the EV battery charger and other residential electrical appliances are dynamically monitored and controlled by using a configurable algorithm, ensuring that the total current consumption does not cause the tripping of the home circuit breaker. An Android client application allows the user to monitor and configure the system operation in real‐time, a developed Wi‐Fi smart plug permits to measure the RMS values of current of the connected electrical appliance and change its state of operation remotely, and an EV battery charger may be controlled in terms of operating power according to set‐points received from the Android client application. Experimental tests are used to evaluate the quality of service provided by the developed smart home platform in terms of communication delay and reliability. An experimental validation for different conditions of operation of the proposed smart home PMS concerning the power operation of the EV battery charger with the proposed control algorithm is also presented. [ABSTRACT FROM AUTHOR]

Powerline inspection is an important task for electric power management. Corridor mapping, i.e., the task of surveying the surroundings of the line and detecting potentially hazardous vegetation and objects, is performed by aerial light detection and ranging (LiDAR) survey. To this purpose, the main tasks are automatic extraction of the wires and measurement of the distance of objects close to the line. In this article, we present a new fully automated solution, which does not use time-consuming line fitting method, but is based on simple geometrical assumptions and relies on the fact that wire points are isolated, sparse and widely separated from all other points in the data set. In particular, we detect and classify pylons by local-maxima strategy. Then, a new reference system, having its origin on the first pylon and $y$ -axis toward the second one, is defined. In this new reference system, transverse sections of the raw point cloud are extracted; by iterating such procedure for all detected pylons, we are able to detect the wire bundle. Obstacles are then automatically detected according to corridor mapping requirements. The algorithm is tested on two relevant data sets. [ABSTRACT FROM AUTHOR]

The increased usage of electric and electronic devices on board the aircraft requested by the "More Electric Aircraft" (MEA) requires the adoption of multi-objective control algorithms, with controllers (supervisors) able to autonomously switch among different objectives. However, the operating point changes during operations, and "small signal" analysis becomes inconclusive to assess overall stability. Thus, the use of standard Proportional–Integral (PI) or Proportional–Integral–Derivative (PID) controllers may become an issue. In this paper, by using nonlinear mathematical tools, the theoretic stability of the controlled electrical devices is considered. Two different control tasks are considered, namely battery charging and generator current limiting. Then an automaton supervisor switches between the two control objectives. Each objective is achieved by using a sliding mode control with adaptive sliding manifold. The adaptation strategy increases robustness, and is crucial for MEA applications, since the controlled system parameters are highly uncertain, due to their harsh environment. Rigorous stability proofs are given, along with detailed simulations in different scenarios. Also hardware implementation is proposed and a comparison with existing strategies (i.e., the Proportional–Integral) is presented, showing the effectiveness of the proposed approach. • Management of electric power in the framework of the More Electric Aircraft is proposed. • A novel control technique with adaptive sliding surface has been designed. • Rigorous proofs of stability for the open-loop and the closed-loop system are provided. • Both simulations and experiments show the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

This paper assesses the contribution of a controllable load (a reverse osmosis [RO] seawater desalination plant), together with an energy storage system in Porto Santo's small islanded electric power system. The controllable load and storage system are used to (i) smooth the net‐demand fluctuations and adapt it to the availability of renewable energy sources (RES), thus avoiding possible curtailments and contributing to a higher dissemination of RES, (ii) minimize the overall operational cost associated with the production of electricity and potable‐water, and (iii) reduce the environmental pollutants associated with the electric power systems on the island. The nonlinear nature of the problem makes it difficult to quickly obtain a robust solution through conventional mathematical tools. Therefore, an evolutionary algorithm is developed to find feasible solutions for dispatching the resources for a one‐week simulation period. The proposed algorithm determined the power output of the conventional thermal power plant, the RO desalination plant operating periods, and the storage charging and discharging periods and powers. In the proposed scenario, through a seven‐day simulation, 50% of the demand is supplied by renewable sources. The numerical results illustrate a reduction in the average total electricity‐peak demand of the island. The obtained diagrams are compared with the data gathered on Porto Santo's energy system. They display that the proposed solution is economically beneficial for the management of the electric power grid of the island of Porto Santo, while reducing the global warming potential (GWP) of the electric power system. Furthermore, it reveals that in a scenario with 50% penetration of renewable sources, through the proposed solution, a more efficient and predictable operation of the conventional electricity generators and the RO desalination plants can be achieved. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This dissertation proposes power management of optimal control scheme for hybrid energy storage system (HESS) like super capacitor and (SCAP) battery in electric vehicles. The proposed technique is a parallel performance of both the random decision forest (RDF) and krill herd optimization (KHO), and thus, it is called as KHO–RDF method. The main objective is to minimize the difference between the actual and reference power in the battery and SCAP. Here, the HESS framework comprises of two sections: (1) figuring the SCAP reference voltage dependent on load dynamics. (2) Maximizing the power flow through HESS. The reference voltage of SCAP by evaluating real-time load dynamics is computed at first, i.e., the vehicle dynamic, motor characteristics, regenerative braking systems and driving conditions. Furthermore, at the same time the magnitude variety of battery power was minimized and the power loss will occur. The input parameters of SCAP are load current, battery current and state of charge. In proposed technique, possible control signals dataset of HESS is fused to produce KHO. By utilizing the practiced dataset of KHO, the RDF is trained and predicts the optimal parameters of HESS. Moreover, the proposed technique advances the SCAP voltage, battery current magnitude, battery current varieties and battery power. With the proposed approach, the parameter of HESS is optimized and it provides certain solutions. The proposed technique is executed in Matrix Laboratory (MATLAB)/Simulink working platform. By using the comparison analysis with the existing procedures, the performance of the HESS is surveyed. [ABSTRACT FROM AUTHOR]

Featured Application: This article presents a photovoltaic (PV)-powered charging station for electric vehicles with power management that integrates vehicle-to-grid (V2G). Based on a DC microgrid integrating PV sources, the charging station is controlled by a searching peak and valley algorithm. Following the description and the validation simulation, a PV-powered charging station for electric vehicles represents a high potential for application because it can be easily implemented as a project pilot in urban areas. The increase in the number of electric vehicles (EVs) has led to an increase in power demand from the public grid; hence, a photovoltaic based charging station for an electric vehicle (EV) can participate to solve some peak power problems. On the other hand, vehicle-to-grid technology is designed and applied to provide ancillary services to the grid during the peak periods, considering the duality of EV battery "load-source". In this paper, a dynamic searching peak and valley algorithm, based on energy management, is proposed for an EV charging station to mitigate the impact on the public grid, while reducing the energy cost of the public grid. The proposed searching peak and valley algorithm can determine the optimal charging/discharging start time of EV in consideration of the initial state of charge, charging modes, arrival time, departure time, and the peak periods. Simulation results demonstrate the proposed searching peak and valley algorithm's effectiveness, which can guarantee the balance of the public grid, whilst meanwhile satisfying the charging demand of EV users, and most importantly, reduce the public grid energy cost. [ABSTRACT FROM AUTHOR]

The power generation from a renewable energy system has a fluctuating nature due to the intermittent sources. Mismatch between power generation and load demand causes a deviation from the desired frequency in power supply. A frequency control scheme for an isolated PV-wind-battery hybrid energy system is proposed by using fuzzy logic control for automatically select the frequency deviation suppressing element without communications among the different operational controllers. That said, the scheme proposed in this paper requires fewer measuring instruments. The proposed control scheme requires two parameters – system frequency and battery state of charge measurement in order to execute the following tasks. The first task includes – selection of proper frequency balancing element (i.e. battery or a dump load or a primary variable load) that depends on the frequency deviation and battery state of charge. The second task is to address the control operation of the selected element from battery, dump load and primary load. Here, we have demonstrated that by the control scheme, fluctuation of system frequency can be suppressed within an allowable limit owing to effective power management of the hybrid system. The scheme presented here simplifies the integration of renewable energy systems to an existing hybrid power system. [ABSTRACT FROM AUTHOR]

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