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Summary: The aim of the paper is to provide seamless transition between the grid connected to an islanded mode and vice versa by switching the control of the load connected VSC (LCVSC) ensuring uninterruptable power supply. In the grid connected mode, a TOQSG (third‐order quadrature signal Generator) based method is applied for power quality improvement at the grid. During synchronization, the LCVSC adapts the phase and frequency of the grid voltage for seamless transition. The paper presents a smart microgrid, which manages the mode of operations and generation of reference signals for the smooth operation. The proposed approach is validated through test results. The grid current and CCP voltage distortion are as per the IEEE‐519 and the IEEE‐1547 standards. [ABSTRACT FROM AUTHOR]

The shift towards green aviation necessitates the adoption of all‐electric aircraft, marking an essential trend in the aviation industry. Distributed propulsion systems enhance aircraft flexibility and reliability, synchronously reducing performance demands and costs of single propulsion units compared to centralized propulsion system. The high reliability and lightweight characteristics of brushless DC motors have made them increasingly popular in distributed propulsion systems. Despite its advantage which helps to build efficient aircraft aerodynamic layout, there are more cables in the distributed propulsion system, which accounts for most of the weight of the aircraft. To address this, all‐electric aircraft utilizes high‐voltage power supply to reduce the current, so it uses SiC MOSFETs as power devices to improve its efficiency and heat dissipation. The speed controller in all‐electric aircraft determines the efficiency and reliability of the propulsion unit. However, the traditional high‐voltage speed controller poses challenges for aircraft maintenance because of high cost. Therefore, this paper emphasizes the significance of designing a low‐cost, high‐performance speed controller for all‐electric aircraft. Challenges such as circuit interference, battery voltage fluctuation, and sampling noise affecting the high‐voltage controller during operation are solved, respectively. First, to address the likelihood of mutual interference in the bridge arm, a self‐bootstrapping voltage reduction drive mode is designed to turn off SiC MOSFET and enhance circuit anti‐interference. Second, to solve the challenge of wide battery voltage fluctuation range and inconsistent controller output, an adaptive pulse width modulation (PWM) regulation mode for battery voltage is proposed, ensuring stable controller output. Finally, to counteract the impact of the comparator threshold on zero‐crossing sampling, an electrical angle delay method based on delay compensation is designed to improve the efficiency of the brushless DC motor during operation. Experimental results show that the three solutions proposed in this paper are helpful to improve the anti‐interference, stability, and efficiency of the controller in the high‐voltage distributed electric propulsion system and contribute to reduce the cost and weight in the propulsion unit. [ABSTRACT FROM AUTHOR]

The application of renewable energies such as wind and solar has become an inevitable choice for many countries in order to achieve sustainable and healthy economic development [[1]]. The paper "Steady-State Analysis and Output Voltage Minimization Based Control Strategy for Electric Springs in the Smart Grid with Multiple Renewable Energy Sources" by Y. Zou et al. presented a general steady-state analysis and minimal compensating voltage control scheme for the second generation of electric springs in the power system with substantial penetration of intermittent renewable energy sources. The paper "Parallel LSTM-Based Regional Integrated Energy System Multienergy Source-Load Information Interactive Energy Prediction" by B. Wang et al. proposed an energy prediction strategy for multienergy information interaction in regional integrated energy systems from the perspective of horizontal interaction and vertical interaction. [Extracted from the article]

Energy management for energy harvesting-based embedded systems (EHES) is an emerging field, which aims to collect renewable energy from the environment to power an embedded system. In this work, we use the systematic mapping method to study the relevant literature, with the objective of exploring and analysing the state of the art in energy management for EHES, as well as to provide assistance for subsequent literature reviews. To this end, we conducted extensive searches to find articles related to energy harvesting, embedded systems, energy consumption, and energy management. We searched for papers from January 2005 to July 2019 from three mainstream databases, ACM, IEEE Xplore, and Web of Science, and found more than 3000 papers about EHES. Finally, we selected 142 eligible papers. We have completed the system mapping research from five aspects, namely, (1) research type (validation research, evaluation research, solution proposal, philosophical paper, opinion, and experience), (2) research goals (application or theory), (3) application scenarios, (4) tools or methods, and (5) paper distribution, such as publication year and authors' nationality. The results showed that the major research type of the EHES papers is validation research, accounting for 65%, which indicated research is still in the theoretical stage and many researchers focus on how to improve the efficiency of harvesting energy, develop a reasonable energy supply plan, and adapt EHES for real-world requirements. Furthermore, this work reviews the tools used for EHES. As the future development direction, it is indispensable to provide tools to EHES for research, testing, development, and so on. The results of our analysis provide significant contributions to understanding the existing knowledge and highlighting potential future research opportunities in the EHES field. [ABSTRACT FROM AUTHOR]

To facilitate the allocation of energy and resources in the Internet of Things system, this paper presents a model for predicting user behavior in Internet of Things environments. The model is based on Bayesian learning and neural networks and is designed to provide insights into the future behavior of users, allowing for the allocation of resources in advance. In this paper, the data are preprocessed by data merging and format processing, and then the association rules are mined by association rules analysis. Finally, the data are utilized to train the behavioral prediction model of the short‐duration memory network via Bayesian optimization. The experimental results showed that the average running time of the research model was 1.682 s, the average accuracy was 96.77%, the average root‐mean‐square error was 0.382, and the average absolute error was 0.315. The designed behavior prediction model is capable of effectively predicting the user behavior of the Internet of Things, thereby enabling the reasonable allocation of energy and resources in the Internet of Things system. [ABSTRACT FROM AUTHOR]

Recently, renewable energy sources integrated with microgrid (MG) networks have provided safe, secure, and reliable power supply to both utility and industrial purposes. Power quality disturbances (PQDs) seriously affect the performance of MG networks and reduce the lifecycle of numerous sensitive devices in MG networks. Hence, this paper presents a new approach to detect and classify the PQDs using discrete wavelet transform, multiresolution analysis, and optimized‐kernel support vector machine. The obtained unique features from DWT‐MRA are fed to train the well‐known intelligent classifiers. In the optimized‐kernel SVM model, computing power is enhanced for classifying multiple PQ events based on the local density and leave‐one‐out (LOO) algorithm. To get higher separation in feature space, the kernel width of each sample is estimated based on the local density. By using the LOO method, an improved grid search strategy is implemented to get the penalty parameter to achieve satisfactory results. Moreover, a typical MG network is simulated in MATLAB software considering the validation of the proposed technique to address the power quality issues in MG networks, and the results of the proposed method are compared with other conventional ML classifiers. The simulation results confirm that the proposed method is more effective and accurate than other intelligent classifiers. [ABSTRACT FROM AUTHOR]

A healthy operation of photovoltaic installations (similar to all electrical systems) is always limited by breakdown, degradation due to aging, or imbalance caused by weather conditions. In this context, producing the maximum energy possible with an acceptable form factor is a significant challenge for autonomous systems, especially those connected to the grid. In this paper, we have two main issues to address. The first is determining the maximum power point of an unbalanced photovoltaic field (due to a defect or nonuniform weather conditions affecting the photovoltaic generators). For such a system, the particle swarm optimization (PSO) algorithm remains highly effective because it can easily handle the existence of multiple maxima simultaneously to provide the best possible solution. The second challenge is managing the imbalance between the three phases of the photovoltaic system. In this context, the results of conducted studies propose two approaches to balance and maximize the power supplied by the photovoltaic generator and converters. In addition, the presence of a battery storage system plays dual roles: firstly, compensating the power fluctuations due to nonuniform operating conditions between phases, and secondly, ensuring system power supply during periods of no sunlight exposure. The proposed approaches take into account the constraints imposed on DC voltages and currents to ensure optimal integration with the multilevel inverter stage (cascaded H‐bridge multilevel inverters). This is achieved through selective harmonic elimination control without the need for a filtering system. A comparative study between these two approaches will be conducted to assess their advantages and disadvantages. The battery‐based storage system efficiently absorbs excess energy and provides energy during deficits, thanks to a flexible control mechanism that allows easy switching between different battery groups and phases. [ABSTRACT FROM AUTHOR]

With the large-scale construction of infrastructure such as high-speed railways and the rapid development of building industrialization and prefabricated buildings, the precast concrete industry has ushered in vigorous vitality. However, there are many tricky problems such as high-energy consumption and unstable long-term performance in the existing production technology of steam-cured concrete. Based on the current research on concrete early strength technology, this paper focuses on the early strength performance and greenness evaluation of the reference cement–sulfoaluminate cement (C–SAC) system. The results confirm that when the 30% mineral admixture—60% C–10% SAC system is mixed with the composite early strength agent, the 1 day strength of mortar can reach 38.0 MPa under the condition of 45°C steam curing. The water absorption is 1.1% after 28 days. The carbon emission of the system (32.1%) is lower than that of the reference cement, and the production energy consumption and the initial resource energy are reduced by 14.4% and 8.2%, respectively. This paper provides a promising strategy for the development of low-carbon, and early strength concrete and ultimately provides technical support for the realization of high-quality and green production of precast concrete components. [ABSTRACT FROM AUTHOR]

This paper proposes a novel short-time optimization method based on a resiliency enhancement strategy for a smart distribution network during adverse weather conditions. The key idea is to integrate various electric vehicles (EVs) with different behaviour patterns and other distributed energy resources into emergency tools of distribution network operation such as topology reconfiguration and grid-supported services. In this regard, possible management programs for three types of EVs (with different levels of control potentials), energy resources, reconfiguration, and demand response programs impacts on boosting resiliency are investigated. The framework is organized on coordination and benefit sharing among distribution system operator (DSO), private sector, and EV owners to reduce possible side effects of failures that could be incepted by extreme weather conditions. A Monte Carlo-based stochastic simulation for modelling uncertainties such as EVs movement, state of change, arrival/departure time to charge stations, and weather-based failures is devised. In order to evaluate resiliency, metrics based on a multiphase method are analysed. The performance of the proposed method on extra benefit obtaining for DSO and private sector and also resiliency enhancement in disturbance progress, postdisturbance degraded, and restorative state are carried out on IEEE-33 bus test system, and obtained results are analysed in detail. [ABSTRACT FROM AUTHOR]

Transformation of the distribution network, integrated with Distributed Energy Resources (DER), into a MicroGrid (MG) is an attractive approach in improving the reliability and resilience of the network. In this paper, by introducing the important and necessary aspects of the MG formation problem, an effective methodology for transformation of a distribution network into the MG is proposed. In the presented methodology, optimal allocation of DERs within MG is obtained by optimization of power losses and voltage stabilization along feeders. Moreover, the short-circuit constraints are also taken into account simultaneously while allocating DERs. The reason for the latter is that the integration of DERs alters the amplitude and direction of the short circuit current, which can cause the overall fault current to exceed the designed fault-level capability of the circuit breakers. This issue may put CBs at risk of failure and degrading MG resilience. In this regard, resilience-oriented evaluation of the MG formation, from the perspective of considering short circuit level and coordination of OCRs, is also performed. Another advantage of short circuit studies is devising complete and economic overcurrent-based protection plan for MG in both operating modes (grid-connected and islanded). It should be pointed out that adopting an appropriate control routine is a key element in the MG formation. Hence, a control strategy, based on Energy Storage System (ESS) utilization, is suggested in this paper. This feature, which is another contribution of the paper, will guarantee seamless transition to island mode and stable operation of the MG. The proposed formulation of the MG formation is solved using a modified multi-objective PSO and the Pareto-optimal set is obtained. However, since the optimal solutions are nondominated to each other, a Fuzzy Satisfaction Method (FSM) is utilized to find the optimal solution among the Pareto-set. Utilization of the proposed methodology leads to the successful transformation of the distribution network into a MG, which not only considers the load flow constraints but also preserves the optimal coordination of the protective relays and resilience of the MG. In addition, seamless transition and stable operation of the MG in island mode is achieved. [ABSTRACT FROM AUTHOR]

The multimedia teaching information data generated in the era of big data are increasing at a huge scale, but the computing environment used before is difficult to meet users' requirements for data integration. Multimedia information has the characteristics of diversity and structure diversity, which lead to the situation that the storage and processing methods are too complicated and time-consuming to varying degrees. This brings great challenges to big data analysis. Therefore, the fusion of this part of the data information requires a large-scale data parallel framework environment to ensure that multiple data calculation instructions can be executed each time, improve the range of problem solving, and essentially improve the complexity of multimedia teaching information. The main index of large-scale data fusion is computing speed. Aiming at the performance defects of the open source computing framework Hadoop, this paper intends to establish a new computing model. This model uses distributed data sets to replace the previous data structures and solve some problems in the process of data integration, thereby significantly improving the computation efficiency and reducing resource usage. Starting from the network's perception of energy resources and considering that the randomness of energy available to nodes in the energy harvesting cognitive relay different systems will lead to different data volumes such as throughput and resource utilization, this paper takes the network's throughput as the optimization network object for resource collection and allocation research. Experiment description: compared with the previous resource management, this model can reduce the total consumption of the system; compared with the single base station mode, the proposed method can extend the network life cycle to a greater extent; meanwhile, it ensures the use of users. Based on the stability analysis of the data, an optimal solution is obtained that can maximize the stable throughput of cognitive users and effectively improve resource collection and allocation. [ABSTRACT FROM AUTHOR]

This paper is oriented to the credit investigation scenario of power grid supply chain enterprises and proposes a blockchain user credit assessment method based on improved Softmax regression in Power IoT. This method first designs a credit-rating mechanism that meets industry characteristics based on business needs. Second, it proposes a user credit evaluation model based on the blockchain architecture. Finally, the improved Softmax regression algorithm is used to train the proposed credit evaluation model, which effectively solves the credit rating. The multiclassification problem has achieved the goal of categorizing the credit rating of the enterprise. The simulation results show that the credit evaluation mechanism proposed in this paper can accurately evaluate the multisource credit data that lacks trust foundation and effectively realize the credit rating of power grid material supply chain enterprises. The credit evaluation mechanism proposed for Power IoT in this paper could have high potential for entity identity authentication and rating for securing mobile video communications. [ABSTRACT FROM AUTHOR]

This paper presents the feasibility analysis and study of integrated renewable energy (IRE) using solar photovoltaic (PV) and wind turbine (WT) system in a hypothetical study of rural health clinic in Borno State, Nigeria. Electrical power consumption and metrology data (such as solar radiation and wind speed) were used for designing and analyzing the integrated renewable energy system. The health clinic facility energy consumption is 19 kWh/day with a 3.4 kW peak demand load. The metrological data was collected from National Aeronautics and Space Administration (NASA) website and used to analyze the performance of electrical generation system using HOMER program. The simulation and optimization results show that the optimal integrated renewable energy system configuration consists of 5 kW PV array, BWC Excel‐R 7.5 kW DC wind turbine, 24 unit Surrette 6CS25P battery cycle charging, and a 19 kW AC/DC converter and that the PV power can generate electricity at 9,138 kWh/year while the wind turbine system can generate electricity at 7,490 kWh/year, giving the total electrical generation of the system as 16,628 kWh/year. This would be suitable for deployment of 100% clean energy for uninterruptable power performance in the health clinic. The economics analysis result found that the integrated renewable system has total NPC of 137,139 US Dollar. The results of this research show that, with a low energy health facility, it is possible to meet the entire annual energy demand of a health clinic solely through a stand‐alone integrated renewable PV/wind energy supply. [ABSTRACT FROM AUTHOR]

Wearable sensors offer great potential in sports, fitness, and medicine. However, their limited battery life poses a major obstacle to their widespread use. This paper explores various energy solutions to extend the battery life of wearable sensors. The first part of the paper focuses on hardware improvements for wearable sensors, such as employing low-power sensors, energy- efficient microcontrollers, and power management circuits. The second part discusses the utilization of solar, thermal, and kinetic energy harvesting techniques to power wearable sensors. These methods aim to harness energy from the environment to supplement or replace battery power. The third part examines wireless power transfer techniques like radio frequency and magnetic resonance, which enable continuous power supply to wearable sensors without the need for physical connections. Software optimization is also highlighted as a crucial aspect in reducing energy consumption. This involves developing efficient algorithms for data processing and transmission, as well as implementing sleep and wake-up modes to minimize power usage during idle periods. Furthermore, the paper emphasizes the importance of user interaction and behavior in improving energy efficiency. It suggests the development of user-friendly interfaces and providing users with feedback on their energy usage. [ABSTRACT FROM AUTHOR]

With the increasing import volume of China's oil and natural gas, the structure of China's energy supply chain (ESC) has become more and more complex. How to use the "Belt and Road" (OBOR) layout to promote China's regional energy cooperation and ensure China's energy security has become an important international issue facing China. This paper constructs an ESC network model in the context of the OBOR, and finds out the position of China in this network model. This paper also finds out the role law of each risk influencing factor, analyzes the degree of its influence, and puts forward some suggestions on the risk management of ESC according to the analysis results. This paper proposes to use decision tree algorithm to guide the development and construction of Sino-Lao economic and trade cooperation, which will help analyze and help the construction and research of Sino-Lao economic and trade cooperation and sustainable energy freight corridor. Based on the energy cooperation between China and the countries along the "Belt and Road," this paper analyzes the network structure of energy cooperation under the "Belt and Road" background based on the small-world network theory. This paper analyzes the important role of core countries in the ESC through the analysis of the basic parameters of the small-world network, the efficiency of energy cooperation, and the stability of energy cooperation. According to the value of each vertex degree obtained by the calculation formula of entropy value, the entropy value of the ESC network structure under the background of OBOR can be obtained as 2.408. It calculates that the maximum and minimum entropy of the ESC network structure at this time are 3.09 and 2.21 respectively. [ABSTRACT FROM AUTHOR]

The high consumption of energy resources but low utilization, low factor allocation, and unreasonable economic structure affect the sustainability of economic development. Based on the energy factor theory from a low-carbon perspective, this paper constructs an impact model of green total point productivity and conducts a detailed analysis of the resource mismatch in the energy industry. Under the premise of efficient resource allocation, the factor input of an enterprise is positively correlated with total point productivity. Second, the model adds environmental constraints, considers the undesired output of carbon dioxide, and considers the input of energy factors, and reduces the undesired output as much as possible while increasing the expected output. The model realizes the update estimation of each component of the stationary part of a class of nonstationary stochastic processes and can use the information at a certain time and its previous time in a cycle to forecast all subsequent times. In the simulation process, in order to make the results more objective and accurate, this paper calculates the index and its decomposition results under two conditions, and uses MaxDEA 6.0 professional software to measure the regional industry total point productivity index. The experimental results show that the effective factor of low-carbon data is 0.91, and the relative error is less than 3.11%, which meets the test threshold and effectively improves the robustness of the model. [ABSTRACT FROM AUTHOR]

In order to improve the adaptive control effect of the multiemergency power supply networking system connected to new energy (NE), this paper studies the adaptive control strategy (ACS) of the multiemergency power supply networking system connected to NE by combining intelligent algorithms and determines the appropriate installation node of the distributed power supply according to the voltage stability index. Moreover, this paper proposes a method to optimize the distribution network feeder reorganization and distributed power configuration problems based on the fireworks algorithm to reduce the network loss and improve the voltage distribution. In addition, distribution network reorganization and distributed power configuration optimization can more effectively minimize network losses and improve voltage distribution. Through the experimental research, it can be seen that the ACS of the multiemergency power supply network system connected to NE proposed in this paper has the established effect. [ABSTRACT FROM AUTHOR]

Summary: The internet of things (IoT) is a distributed heterogeneous network of lightweight nodes with very minimal power and storage. The IoT energy system for smart applications such as smart grid, smart building, and smart transportations depends on the IoT architecture, determining the high or low‐energy consumption levels. Most of the IoT objects are power‐driven by batteries with short life spans that require replacement. The replacement phase is tedious; hence this paper comprehensively discussed the IoT energy system, energy resources, and energy storage as these three elements are crucial to enable energy efficiency for the IoT applications. In comparison to the battery‐powered solutions, the scavenging of infinite quantities of energy makes the IoT systems robust. Therefore, this paper further elaborates on understanding the current situation in terms of renewable energy harvesting for these low‐energy systems. The IoT energy storage highlighted in this paper includes fuel cell, lithium battery, and supercapacitor technology. This paper also provides the findings for IoT energy system challenges and open issues in management and storage in terms of bidirectional, continuity, autonomy, fluctuation, conversion, consumption, integration, multifunction, and stability. This paper will assist researchers in more quality yet practical energy usage and savings, better IoT system architecture, and smart application sustainability. [ABSTRACT FROM AUTHOR]

Multihop smart grid is built on the basis of an integrated and high-speed communication network. Through the application of advanced sensing and measurement technology, equipment technology, control method, and advanced decision support system technology, the goal of reliable, safe, economic, efficient, environment-friendly, and safe use of the power grid is realized. In order to solve the problem of excessive demand for power supply, new energy power generation and demand response are proposed. According to the above background, the demand side economic scheduling problem is a complex optimization problem, which is difficult to be solved by ordinary algorithms. The adaptive global search algorithm based on a genetic algorithm can better solve complex optimization problems. The genetic algorithm proposed in this paper can effectively manage a large number of controllable loads in the selected area. The algorithm minimizes the cost and peak to the average ratio by changing the load. Home users can arrange their maximum load when the price is low. The peak load of residential buildings decreased from 98.5 kw/h to 90 kw/h, and the peak load decreased by about 7.53%. Through appropriate load dispatching, users minimize the daily electricity charge, which is reduced from 1352 yuan to 1245 yuan per day, and the daily electricity charge is reduced by about 7.25%. In addition, the advanced measurement, communication, and control means under the framework of the smart grid also play a key role in promoting all aspects of demand side management (DSM). [ABSTRACT FROM AUTHOR]

Summary: The intermittent, random, and uncertain characteristics of the energy harvested from solar or wind are threatening the commercial efficiency and reliability of power system. With the continuous expansion of renewable energy installation scale, it has become a new challenge to improve the level of renewable energy accommodation and reduce the rate of renewable energy abandonment. Power system production simulation provides basis for the planning and development of renewable energy to assess the capacity of renewable energy accommodation in the power grid accurately. Traditional power system production simulation ignores sequential variation characteristics of load and new energy power output, thus bringing non‐negligible errors. Moreover, most reported studies ignore the impact of power source location on the level of renewable energy accommodation. This paper is therefore prepared with the objective to establish a time‐series production simulation model of multi‐energy power system and carry out multipoint layout planning and design of multi‐energy power supply. To achieve this objective, extensive literature reviews are conducted with more attention given to recent studies on the field. On the basis of the traditional production simulation model, this paper adds the power flow constraints of the transmission network, and proposes a time‐series production simulation model. A multipoint layout planning and design method for multi‐energy power systems is proposed to give full play to the synergistic effects of natural resources, to achieve optimal resource allocation, and to provide a theoretical basis for improving the consumption of wind and renewable energy. The method is simulated with the actual meteorological and operation data of a province power grid in Northwest China. The simulation results of different scenarios are compared and analyzed, and the effectiveness of the method is verified. [ABSTRACT FROM AUTHOR]

As an important part of power infrastructure, a power monitoring system provides real-time data acquisition, state detection, and remote control of power equipment for the power grid and can deal with sudden anomalies in time. The operation and maintenance of the power monitoring system are very important to ensure the stable operation of power grid. The current mainstream remote operation and maintenance mode has internal threats such as misoperation of operation and maintenance personnel or malicious damage caused by attackers stealing operation and maintenance authority. Meanwhile, the existing operation and maintenance audit has the problems of high human resource cost and limited supervision of operation and maintenance personnel. To solve this problem, this paper proposes a collaborative filtering method for operation and maintenance behavior of power monitoring system called CFomb. Exploiting a keyword matching algorithm, CFomb determines the power resources accessed by operation and maintenance users from multiple operation instructions and extracts operation and maintenance behaviors. Referring to the collaborative filtering idea, the feature matrix decomposition scheme is introduced to train the access probability model based on the historical normal behavior of multiple operation and maintenance users, which provides a basis for real-time prediction of the access behavior probability of target operation and maintenance users. The OTSU binarization technique is used to determine the probability threshold of abnormal operation and maintenance behaviors, identify abnormal behaviors through threshold comparison, and send real-time alarms to operation and maintenance audit. The simulation experiment results show that the method in this paper can effectively identify the abnormal behavior of operation and maintenance users, reduce the overhead of manual audit, and help improve the power monitoring system's ability to respond to internal threats of operation and maintenance. [ABSTRACT FROM AUTHOR]

Human resource management mode refers to a comprehensive summary of management objectives, processes, content, methods, and other elements. The more common two modes are control mode and commitment mode. The enterprise human resource management model has many different types. The generation pair promotes the development of enterprise human resource management from the traditional model to the platform model, processing complex data with the help of data-based technical means, and realizing the integration and sharing of resource data. This paper takes an energy company as an example to carry out a detailed study. The article takes the big data as the background and the company as the research object. From the perspective of human resource management, this paper tries to find out the performance management, compensation and benefits management, and other issues of the company in human resource management under the background of the big data era and puts forward corresponding solutions for the current problems. In particular, the company gave certain opinions on how to build a human resource management system in the context of the current big data era. By conducting field research on the company and issuing questionnaires, this paper finds out the current problems of the company in human resource management and proposes corresponding solutions for these problems. [ABSTRACT FROM AUTHOR]

Under this background, this paper tries to find countermeasures and ways for carbon reduction by observing and analyzing the influencing factors of carbon emissions, designing ways to minimize carbon emissions and maximize resources and energy. In view of the above problems, the carbon emission prediction research is closely combined with the research of deep neural network, the carbon emission prediction models based on deep neural network are established, respectively, and the game theory is introduced to maximize the resource economy. Based on the analysis of the cost of energy resources, this paper puts forward a model based on game theory and makes an overall planning of the bidding online auxiliary decision-making system in combination with the actual market demand. Build a big data analysis platform based on the Internet of things, collect the data related to carbon emission for normalization, analyze the influencing factors related to carbon emission by using the principal component analysis method, select the data with higher connection value, and take the time series data as the input of the deep neural network for simulation verification. The simulation results show that the game model of carbon emission minimization and energy resource economic maximization based on deep neural network can effectively improve the economic maximization of energy resources and reduce carbon emissions. [ABSTRACT FROM AUTHOR]

The willingness to improve the security and reliability of power supply to end-users, often pushed by prescriptions of national regulatory authorities, is bringing considerable challenges for distribution system operators. Islanding a portion of the public distribution network after a fault is considered a measure to mitigate the effects of service interruptions. This procedure is usually carried out by counterfeeding the grid through a generator set (GenSet). Even if this approach is widely adopted around the world, reenergizing the grid and keeping the electric island stable is not a trivial task. In this framework, the scope of this paper is to provide a set of technical guidelines for the usage of GenSets to supply public grids in emergency conditions. The goal is to highlight the static and dynamic limits of the GenSet operations and simplify their exploitation for the grid operators. The numerical analyses, which have been carried out through the RMS simulation tool of the DigSilent PowerFactory software, also aim to evaluate the technical constraints in the case of active networks, which involve distributed generation implementing regulations according to ENTSO-E and Italian technical standards. [ABSTRACT FROM AUTHOR]

Wireless rechargeable sensor network (WRSN) uses mobile chargers (MCs) to charge sensor nodes wirelessly to solve the energy problems faced by traditional wireless sensor network. In WRSN, mobile charging schemes with multiple MCs supplementing energy are quite common. How to properly plan the MC's moving path to reduce the charge energy loss and deploy nodes to improve network coverage rate has become a huge research challenge. In this paper, a collaborative energy optimization algorithm (CEOA) is proposed for multiple chargers based on k-mean++ and node collaborative scheduling. The CEOA combines internal energy optimization and external device power supply, effectively prolongs network lifetime, and improves network coverage rate. It uses the k-mean++ to cluster nodes in the network; then, the nodes in the network are scheduled to sleep based on the confident information coverage (CIC) model. Finally, the CEOA uses a main mobile charger to carry multiple auxiliary mobile chargers to charge all the nodes in the cluster. Simulation results show that the proposed algorithm increases the network lifetime by more than 8 times and the coverage rate by about 20%. [ABSTRACT FROM AUTHOR]

In recent times, with the increase in the penetration of various renewable energy sources (RESs) into power systems, the complications related to the stability issues have increased. The main contribution of this paper is an in‐depth analysis of research in microgrid based on small‐signal, transient, and voltage stability. The small‐signal stability has been discussed based on uncertain load, limitation in power generation capacity, and nature of sluggish feedback observed in few microgrid systems. Instability caused by a transient phenomenon in microgrid has been thoroughly analyzed for losses in distributed energy resources (DERs), islanding and transition modes of operation, load shedding, and faults causing instability. The voltage fluctuations are duly considered in the perspective of load shedding, type of loads, power unbalancing, and different types of faults in microgrid systems. The existing controllers have been compared based on steady‐state error, response time, and robustness etc. The voltage, frequency, and active/reactive power control are analyzed based on centralized, decentralized, hierarchal/distributed control schemes aiming stabilization of microgrid systems. Finally, stability improvement features have been explored in detail, finding the implementation of microgrid stabilizers, energy storage devices, FACTS devices, load balancing, resource forecasting including the adaptive controller. Hence, the uniqueness of this paper can be considered as a state‐of‐the‐art review of the abovementioned stability components in the microgrid system which is not available in the published literature with a similar amount of vastness. [ABSTRACT FROM AUTHOR]

Subway traction power supply system is the energy source of subway operation. The normal operation of subway traction power supply system is directly related to the stable operation and safety of subway. If the subway traction power supply system breaks down, it not only will cause economic losses, but also may cause social disorder. Therefore, it is of great significance to study the traction power supply system of subway to ensure the safe operation of subway. The establishment of a reasonable monitoring and testing platform, the simulation analysis of the traction power supply system, and the regular health assessment can provide a strong guarantee for the safe operation of the subway. This paper aims to design a practical test platform through MATLAB modeling, so as to establish a simulation model of subway traction power supply system, and comprehensively monitor and grasp the health status of subway traction power supply system. This paper quantitatively evaluates the health status of subway traction power supply system, applies the principle of multilayer immunity, establishes the traction power supply system of analytic hierarchy process (AHP), and constructs the immune monitoring system of each layer. This method can provide the health status information of subway traction power supply system, subsystems, and components comprehensively, accurately, and intuitively and determine the subsystems and components that need to be maintained. [ABSTRACT FROM AUTHOR]

To protect energy resources and alleviate environmental pollution, many countries attach great importance to the transformation of traditional industries into clean energy industries. In this paper, fuel vehicles (FVs), hybrid vehicles (HVs), and electric vehicles (EVs) are included in the research. Then, based on the aging chain and coflow theory of SDs, we construct a dynamic matching model of the auto industry upgrade process and its energy consumption attributes. The simulation results of China's auto industry show that (1) the upgrading of the auto industry is an evolutionary process from high energy consumption and high pollution to low energy consumption and no pollution and the transition from FVs and HVs to EVs will undergo two adjustments; (2) simply reducing energy supply does not have the expected impact on vehicle size and vehicle energy consumption intensity and only by adjusting the energy supply and upgrade ratios together, energy utilization efficiency can be improved; (3) market screening time has an impact on auto industry upgrade speed by affecting vehicle market share and dwell time; (4) China's auto industry upgrade process should adhere to "problem-oriented" and strengthen consumer guidance, technology innovation, and infrastructure construction. The conclusions can provide references for industrial policy adjustment and industrial structure optimization. [ABSTRACT FROM AUTHOR]

Summary: The integration of demand side management (DSM) with smart grid (SG) can facilitate residents' transfer into smart homes and sustainable cities by reducing the carbon emission. This manuscript reviews the recent works related to the application of DSM in SG through discussing the techniques and algorithms and their associated challenges for effective implementation. This paper also critically discusses the operation mode of DSM, the profile of energy production, storage and consumption, and finally the benefit obtained by the DSM implementation. Previous literature suggested that DSM practice reduced peak‐to‐average ratio, energy cost and carbon emission by approximately 10% to 65%, 5% to 50%, and 14%, respectively. The implementation of DSM in SG deals with a number of challenges such as security and privacy, tariff regulation, energy transmission, distribution, and effective utilization of energy resources. A number of international organizations have taken various measures and solutions to guarantee the security and privacy of the DSM in SG discussed. So far, a number of algorithms have been used as optimization approach to solve the DSM optimization problems; however hybrid algorithms have showed better performance than single algorithms due to their faster convergence speed. At the end, the paper presents the research gaps and future research directions. [ABSTRACT FROM AUTHOR]

Based on the perspective of complex system management of forest biomass power generation (FBPG) supply chain, this paper explores a new way to reduce the operation cost of forest biomass raw materials supply chain and improve the operation efficiency of forest biomass supply chain in China. Firstly, the supply chain model of FBPG is summarized through the survey data, and the existing problems are found. Then, the behavior strategies among farmers, middlemen, and enterprises are analyzed, and the data that cannot be obtained in the model are estimated by using statistical methods. Finally, the complex supply chain system is simulated by computer. Through setting different forestry policies, this paper compares and analyzes the profit of the supply chain and puts forward policy suggestions to promote the development of the FBPG industry in China. In this paper, the actual situation, principles of statistics, and system simulation are effectively combined to make the behavior rules of various stakeholders fit the reality, and the research conclusions are more practical. [ABSTRACT FROM AUTHOR]

With the intensification of global environmental pollution and the overexploitation of resources and energy, low-carbon environmental economy (LCEE) has become the important means for countries in the world to achieve sustainable economic, resource, and environmental development. Therefore, the research on the coordinated development of logistics development and LCEE is very necessary in the development of our country's LCEE. Firstly, this paper discusses the development level of logistics industry in Hainan Province from five aspects: energy consumption, carbon emission, carbon productivity, economic benefit index, and carbon emission efficiency. Secondly, choose carbon emission intensity to represent the development level of low-carbon economy. Based on the efficiency coefficient method, coefficient of variation method, linear weighting method, spatial description method, and system evolution equation method, the coordination measurement method is adopted. Finally, the coordinated development of logistics development and low-carbon environmental economy in Hainan Province is analyzed. On this basis, this paper puts forward the related promotion strategies for the coordinated development of logistics development and logistics industry in Hainan. The research results show that the system coupling degree in Hainan Province is about 0.9, indicating that the logistics industry and the low-carbon economic subsystem have a relatively high degree of coupling development. Compared with other provinces, Hainan's level of coupled and coordinated development is at the upstream level. [ABSTRACT FROM AUTHOR]

The Overhead Contact System (OCS) is critical infrastructure for train power supply in rail transit systems. OCS state monitoring and fault detection are indispensable to guarantee the safety of railway operations. The existing human-based OCS state monitoring and fault diagnosing method has some inherent drawbacks, such as poor real-time capability, low detecting precision, and waste of human resources. Edge Intelligence (EI) can perform complex computing tasks offloaded from trains within a little delay, and it is believed to help empower the OCS. In this paper, we propose an EI-based OCS state monitoring and fault detecting system. The latest Computer Vision (CV) model YOLOv5s is used to detect the OCS faults using the collected images. Edge Computing (EC) is used to perform the CV model inference. The EC system receives the OCS images taken by the train cameras and calculates the real-time fault detection results. The consistency and scalability of running jobs on edge devices are also addressed in our approach. Extensive experimental results demonstrate that the proposed EI-based system can detect OCS faults in real-time. The adopted YOLOv5s achieves a high fault detection rate, outperforming other models. [ABSTRACT FROM AUTHOR]

Nowadays, power electronic technology is widely affecting people's daily work and life. However, there are still many problems in the current power supply research. When the fault information of power transformer is not complete or there is some ambiguity or even the information is lost, it will largely lead to the conclusion and correct conclusion of fault diagnosis. In this case, the fuzzy theory is applied to the fault diagnosis of shunt capacitor, and the fuzzy fault diagnosis system of shunt capacitor is studied. At the same time, a map-based fault diagnosis system is proposed. In this paper, the cloud computing technology is introduced into the deep learning and compared with SVM and DBN algorithm. The research results of this paper show that the accuracy of fuzzy diagnosis results is 94%, 84%, 90%, 80%, 83%, and 70%, respectively, which shows that the model diagnosis reliability is relatively high. Among the three algorithms, MR-DBN overall detection rate is higher and the time-consuming is lower than the other two methods. The diagnostic accuracy and misjudgment rate of DBN are as follows: 96.33% and 3.90%. The diagnosis accuracy and misjudgment rate of SVM are as follows: 96.40% and 3.83%. The diagnostic accuracy and misjudgment rate of MR-DBN are, respectively, 99.52% and 0.57%. Compared with the other two methods, MR-DBN has the highest diagnostic accuracy and the lowest error rate, which to a large extent indicates that MR-DBN algorithm has higher diagnostic accuracy and has greater advantages and reliability in power supply diagnosis and identification. It not only improves the accuracy of power capacitor fault diagnosis and identification but also provides a new method for the application of power capacitor fault research and development. [ABSTRACT FROM AUTHOR]

Summary: In the modern world, only conventional energy resources cannot fulfil the growing energy demand. Electricity is a fundamental building block of a technological revolution. Today, most of the electricity demand is met by the burning of fossil fuels but at the cost of adverse environmental impact. In order to bridge the gap between electricity demand and supply, nonconventional and eco‐friendly means of energy generation are considered. Renewable energy systems (RESs) offer an adequate solution to mitigate the challenges originated due to greenhouse gasses (GHG). However, they have an unpredictable power generation with specific site requirements. Grid integration of RESs may lead to new challenges related to power quality, reliability, power system stability, harmonics, subsynchronous oscillations (SSOs), power quality, and reactive power compensation. The integration with energy storage systems (ESSs) can reduce these complexities that arise due to the intermittent nature of RESs. In this paper, a comprehensive review of renewable energy sources has been presented. Application of ESSs in RESs and their development phase has been discussed. Role of ESSs in increasing lifetime, efficiency, and energy density of power system having RESs has been reviewed. Moreover, different techniques to solve the critical issues like low efficiency, harmonics, and inertia reduction in photovoltaic (PV) systems have been presented. Unlike most of the available review papers, this article also investigates the impact of FACTS technology in RESs‐based power system using multitype flexible AC transmission system (FACTS) controllers. Three simulation models have been developed in MATLAB/Simulink. The results show that FACTS devices help to maintain the stability of RESs integrated power system. This review paper is believed to be of potential benefit for researchers from both the industry and academia to develop better understanding of challenges and solution techniques for REs‐based power systems and future research dimensions in this area. [ABSTRACT FROM AUTHOR]

With the gradual acceleration of modernization, metro projects have been opened for the development and construction of urban areas. How to ensure the smooth operation of subway project in public service has become the focus of urban public safety service research. In view of this content, this paper studies the subway field of urban rail transit operation and improves the public safety of urban rail transit operation. Firstly, the factors affecting the track traction power supply are analyzed, the anti-interference technology of ring array electromagnetic sensor is added, and the research scheme is formulated according to the track potential research standard to ensure the operation of the power supply system. Then, the electromagnetic sensor traction algorithm is used to detect the subway track power supply signal, and the working characteristics and advantages of single electromagnetic induction and double electromagnetic induction are analyzed. The experimental results show that compared with the traditional electromagnetic sensor power supply signal detection algorithm, the content of this paper can judge whether the power supply is normal according to electromagnetic induction, detect whether the subway traffic deviates from the track traction coordinates, and understand the size of the deviation from the track. It improves the efficiency of traction power supply detection, stabilizes the running track of subway traffic, and is conducive to the protection and development of urban public safety. [ABSTRACT FROM AUTHOR]

In this paper, a multiobjective framework for simultaneous reconfiguration and allocation of photovoltaic (PV) energy resources in radial distribution networks is performed for minimizing the power losses, lowering network loading factor, and reducing cost of energy resources as well as increasing the voltage stability index. A novel algorithm called the improved clouded leopard algorithm is used to find the set of optimum decision factors in the combined execution of restructuring and also PV resource distribution. The clouded leopard optimization (CLO) algorithm, which is widely used, takes its cues from the sleeping and foraging habits of the animal, and the improved CLO (ICLO) is formed using adaptive inertia weight to overcome premature convergence. In five cases of base distribution network and different contribution of reconfiguration and PV allocation, single- and multiobjective approach has been applied on 33- and 69-bus distribution networks. According to the findings, the best case includes simultaneous reconfiguration and PV allocation in the radial networks based on the multiobjective approach unlike the single-objective method which obtained the highest network performance with the best compromise between different goals. Also, in the best lowest losses, the decrease in the network loading and cost of energy resources and the better voltage profile and stability are obtained satisfying the constraints. The losses, network loading, voltage profile, and voltage stability are improved by 60.40%, 37.89%, 6.17%, and 27.5% for 33-bus network and also enhanced by 71.77%, 41.23%, 4.76%, and 20.48% for 69-bus network, respectively. Also, the results showed that network reconfiguration only has the weakest performance among other cases based on reconfiguration or photovoltaic sources. Moreover, the superior capability of the ICLO in addressing the aim of the study is proved in comparison with the traditional CLO and well-known particle swarm optimization (PSO) and manta ray foraging optimization (MRFO) to obtain the better objective value and statistical criteria to solve the best case. [ABSTRACT FROM AUTHOR]

The use of decentralized renewable energy systems will continue to play a significant role in electricity generation especially in developing countries where grid expansion to most remote areas is uneconomical. The income levels of these off-grid communities are often low, such that there is a need for the delivery of cost-effective energy solutions through optimum control and sizing of energy system components. This paper aims at minimizing the net present cost (NPC) and the levelised cost of energy (LCOE). The study presents a hybrid power system involving a hydroelectric, solar photovoltaic (PV), and battery system for a rural community in Cameroon. The optimization of the system was done using HOMER Pro and validated using a meta-heuristic algorithm known as genetic algorithm (GA). The GA approach was programmed using the MATLAB software. After the HOMER simulation, the optimal power capacity of 3 kW solar PV, 334.89 Ah battery, and 32.2 kW microhydropower was used to meet the load. The village load profile had a daily energy usage of 431.32 kWh/day and a peak power demand of 38.49 kW. The optimized results showed an NPC and LCOE of $90,469.16 and 0.0453 $/kWh, respectively. The system configuration was tested against an increase in hydropower capacity, and it was observed that increasing the hydropower capacity has the ability to significantly reduce the LCOE as well as the battery and solar PV size. A comparative analysis of the two approaches showed that the optimization using GA was more cost-effective than HOMER Pro with the least LCOE of 0.0344 $/kWh and NPC of $86,990.94 as well as a loss of power supply probability (LPSP) of 0.99%. In addition, the GA method gave more hydropower generation than HOMER Pro. This supports the fact that stochastic methods are more realistic and economically viable. They also accurately predict system operation than deterministic methods. [ABSTRACT FROM AUTHOR]

Under the "carbon peak and carbon neutrality" goal, the construction of an efficient, low-carbon, and economical energy supply system is of great significance for advancing a dual carbon strategy. In allusion to the integrated energy systems (IES) with hydrogen energy coupling, a hydrogen energy coupling IES low-carbon optimization operation strategy that took account of green certificate and ladder-type carbon joint trading and dual-incentive demand response was proposed in this paper. First, a hydrogen energy multiuse system composed of an electrolyzer, a hydrogen fuel cell, a methane reactor, and hydrogen energy storage was constructed to make full use of the low-carbon cleaning characteristics of hydrogen energy. Besides, a combined model of hydrogen mixed with natural gas was established to improve the utilization efficiency of hydrogen energy. Second, a dual-incentive demand response model including price incentives and subsidy incentives was constructed to fully use the ability to adjust demand-side resources. Next, in view of the carbon emission reduction mechanism of the green certificate, a green certificate and ladder-type carbon joint trading mechanism was constructed. In addition, a green certificate trading mechanism and a reward and punishment tiered carbon trading mechanism had been introduced separately in the IES optimization operation model to reduce carbon emissions of the system. The calculation simulation sets up different scenarios for comparative analysis. As shown by the results, the proposed model could effectively improve renewable energy consumption capacity and energy utilization efficiency. The effectiveness of hydrogen energy utilization, demand respond, and green certification carbon trading mechanism in improving system economy and low-carbon efficiency is verified. [ABSTRACT FROM AUTHOR]

Due to the rapid increase in the use of electric vehicles and instability in energy supply, the application of vehicle-to-grid (V2G) technology has gained attention in the freight transportation sector. V2G has the potential to increase the efficiency of power grid and make additional profits by utilizing surplus power from electric vehicle batteries. This paper proposes an optimization model for electric trucks (ETs) to provide operational decision-making support for the freight transportation sector. The objective of the model is to minimize the total net cost, which includes charging cost, discharging reward, and time penalties, while considering changes in ET charging cost and the system marginal price. Furthermore, we conduct sensitivity analysis in the vehicle routing problem with soft time windows using ETs in the V2G system. [ABSTRACT FROM AUTHOR]

Summary: The continuous increase of the distributed energy resources (DERs) penetration levels leads to voltage stability problems in the distribution system. One of the approaches for the mentioned emerging challenge is the proper placement of automatic voltage regulators (AVRs). This paper investigates the optimal placement and sizing of AVRs in a distribution network by presenting a new modification of the teaching‐learning‐based optimization (TLBO) algorithm. The objective functions consist of minimizing the distribution system voltage deviation, energy generation cost, and electrical losses. The modification improves the convergence velocity and accuracy of the TLBO algorithm using the combination of mutation technique and quasi‐opposition‐based‐learning concept. This paper compares the performance of the proposed algorithm with other famous evolutionary algorithms. The test distribution system contains installed DERs that work more efficiently after the placement of AVRs based on the mentioned objective functions by the proposed optimization algorithm. The simulation results display the best optimization algorithms for AVRs placement with a significant level of less than 0.10 (ie, probability‐value). The proposed multiobjective optimization algorithm's considerable merit is the accuracy and convergence velocity in solving this specific optimization problem. [ABSTRACT FROM AUTHOR]

Summary: Arguably, the most important challenge nowadays in the energy industry is the utilization of intermittent renewable energy sources. On the one hand, renewable sources are the future in terms of sustainability. While on the other hand, several questions remain unanswered in connection with their day‐to‐day operation and system integration. Creating microgrids with local control of the distributed energy resources seems to offer solutions but there is a lack of practical experience. Especially in Europe, where a microgrid with islanding capability is connected to a widespread, synchronously operating grid, it is a complicated task, owing to the control methods. In this paper, the technical possibilities are presented, which are necessary to allow island mode operation of a microgrid. The case study discusses a "living lab" in which several energy generation technologies have been deployed thus it is a good representation of future renewable‐based microgrids. To support the island operation, numerical calculations and simulations are used to determine power and energy needs of necessary flexibility measures. Basis of the calculations is the year‐long data measured on‐site. Sizing and operation modes for energy storage and demand‐side resources and an architectural scheme are presented. Net present value for all technological options are compared to aid the selection of the best option. A sensitivity analysis is also performed to assess how potential change in various parameters of the economical calculation affect the results. It is shown that provision of uninterruptible power supply and thus island operation of the site can be provided within reasonable techno‐economical limits. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a new robust distributed approach for the energy management of rural multi‐microgrids communities in Chile. These rural communities are often geographically isolated and have a poor connection to the main electricity grid. Operating them as individual microgrids allows the community to connect to the grid or behave as a stand‐alone system (islanded). Connecting and coordinating the operation of the microgrids can improve the system's robustness to energy and demand uncertainty, as well as reducing the dependency on the main grid. However, this coordination can be challenging because the communication between the different microgrids can sometimes deteriorate, moreover, both load and local generation profiles are challenging to predict. For these reasons, in this paper, a new robust distributed hierarchical Energy Management System (EMS) for coordination of multiple microgrids (multi‐microgrids) is introduced, which uses robust distributed Model Predictive Control (MPC). Distributed optimisation permits improving reliability in the presence of poor or lack of communications between microgrids. To consider the uncertainty associated with renewable energy generation and load consumption, fuzzy prediction interval models are included in the MPC design. The proposed method is compared with robust centralised MPC and deterministic distributed MPC to assess its performance. Simulation results are presented using data representative of a rural community in coastal Chile applied to the robust MPC design. The outcomes of this work demonstrate that the proposed robust distributed EMS can improve the power supply, apply peak shaving, and can successfully operate when there is a loss of communication between microgrids. The use of decreasing fuzzy intervals allows an accurate prediction of both load and renewable energy generation, making the system robust to uncertainty in the available resources. Moreover, results show that in absence of communication, the power supply obtained under different scenarios with the distributed EMS is better than the equivalent centralised EMS. [ABSTRACT FROM AUTHOR]

With the integration of distributed renewable energy to the distribution network and the development of multiterminal flexible DC transmission technology, multiterminal flexible DC distribution network has broad application prospects. At the same time, with the rapid development of new energy vehicles, the echelon utilization of power battery has become a research hotspot. By analyzing the characteristics of flexible DC distribution network and echelon utilization battery, the structure and control strategy of modular multilevel converter (MMC), DC solid-state transformer, photovoltaic power generation, wind power generation, and echelon utilization battery energy storage system are established, respectively, in this paper. To achieve a DC network connection of various types of power supply and load, this paper proposes a starting method of multiterminal flexible DC distribution network and a cooperative control strategy of the wind-solar-storage system. A six-terminal ring-shape DC distribution network model is built in real-time digital simulation (RTDS) platform. The simulation results show that the modeling methods and control strategies of each component in the real-time simulation model meet the operation requirements of the multiterminal flexible DC distribution network, which provides a reference for the construction and research of the flexible DC distribution network. [ABSTRACT FROM AUTHOR]

Solar water heaters (SWHs) are one of the most effective plans for general and easy use of solar energy to supply hot water in domestic and industrial sectors. This paper gives the first-ever attempts to assess the optimal localization of SWHs across 22 major cities in Zambia, as well as determine the possibility of hot water generation and model the greenhouse gas (GHG) emission saving. The climate data used is extracted by using the MeteoSyn software which is modeled in TSOL™. Results show the high potential of GHG emission reduction due to nonconsumption of fossil fuels owing to the deployment of SWHs, and three cities Kabwe, Chipata, and Mbala had the highest GHG mitigation by 1552.97 kg/y, 1394.8 kg/y, and 1321.39 kg/y, respectively. On average, SWHs provide 62.47% of space heating and 96.05% of the sanitary hot water requirement of consumers. The findings have shown the potential for the deployment of SWHs in Zambia. The techno-enviro study in this paper can be used by the policymakers of Zambia and countries with similar climates. [ABSTRACT FROM AUTHOR]

There is higher harmonics and electromagnetic interference caused by high-power-density switching power supply during high-frequency and normal operations which affects power quality of switching power supply and also impacts the environment. To solve this problem, this paper proposes a dual digital signal processor (DSP) controller suitable for unified power quality regulators and designs high-power-density switching power supply control system for promoting green environment. The overall structure of the system is divided into two parts, the power main circuit and the control circuit. In the main power circuit, the electromagnetic interference (EMI) filter is used to filter the input AC voltage and convert it into a stable DC voltage. A high-frequency inverter is used to invert the DC voltage into a high-frequency AC voltage to achieve harmonic control and reactive power compensation. In the control circuit, dual DSP controller is used to improve real-time detection, control of the output voltage, and current and driver temperature of the high-power-density switching power supply, while high-precision analysis and processing system collect different information and then achieve the power quality control of the switching power supply based on system software to keep this environment green. The experimental results suggest that the harmonic content of the experimental objects controlled by the system is reduced by 16.7% compared with the existing power quality control and the absolute error of the system is less than 0.02 V which improves the de-noising performance. The power generation systems require that the consumption of natural resources and renewable energy solutions can assist in saving the natural resources. [ABSTRACT FROM AUTHOR]

Large-scale and widely dispersed distributed energy resource (DER) can be gathered by a virtual power plant (VPP) in a given area, and its parameters can be combined into a single external operation profile. Each distributed energy source in the VPP has a complete backup of the critical information for the entire network because it is a node of blockchain. The distribution network can be accessed by DER freely and adaptable under the scientific management of the VPP, and it can offer the system high-reliability, high-quality, and high-security power services. An energy blockchain network model based on particle swarm optimization (PSO) to optimise the neural network is proposed in this paper as a solution to the issues with the current VPP models. This will enable distributed dispatching of the VPP and reasonable load distribution among units. According to the simulation results, this algorithm's error is minimal and its accuracy can reach 94.98 percent. This model can more accurately capture demand-side real-time information, which benefits VPP's stable scheduling with a welcoming environment and transparent information. It also enhances the system's data security and storage security. This system can successfully address the issues of subject-to-subject mistrust and high information interaction costs in the VPP. [ABSTRACT FROM AUTHOR]

In order to stabilize the output fluctuation of wind power generation, this paper applies control strategy to control the action of the battery energy storage system according to the stability target, and quickly makes up for the system demand. In this paper, the optimal scheduling of new energy power system with energy storage is studied. This model predictive control is introduced into a certain system and optimized by model predictive control strategy so that the combination can match the predetermined expectations. Then, the model predictive control strategy is used to improve the schedulability of the wind farm. The objective function is to minimize the amount of action, and the composite output can track the scheduling instructions. Considering the cost, the way of storage and operation cost is optimized, and the penalty cost of wind abandonment is also reduced. Through rolling optimization and feedback correction, the model predictive control is optimized and solved, and finally, the action instructions of the storage system are obtained so that the storage output can effectively calm the wind. Power makes the combination schedulable. The optimized wind power/energy storage output is equivalent to the benign and dispatchable power supply of the power grid. [ABSTRACT FROM AUTHOR]

The Sino–Russian Arctic energy cooperation is a successful example based on the comprehensive strategic partnership between the two countries. In order to analyze the impact of Sino–Russian oil and gas resources cooperation in the Arctic on China's energy security, this paper selects 11 influencing factors such as energy self-sufficiency rate and uses the energy security index method to evaluate the three dimensions of energy supply, demand, and environmental security. The assessment results show that China's energy security is mainly affected by the over concentration of energy import sources. At the same time, energy demand and environmental security will also have an important impact on China's energy security. However, relative to energy demand, environmental security factors such as the proportion of clean energy consumption and channel safety factor have a greater impact on China's energy security. After China and Russia strengthen cooperation in oil and gas resources in the Arctic, China's energy security index is expected to increase from 0.4419 in 2020 to 0.5412 in 2025. Therefore, China can use technology, funds, scientific research, and other support to carry out all-round cooperation with Russia in the Arctic waterway, oil and gas exploration and development, and Arctic scientific research. [ABSTRACT FROM AUTHOR]

With the development of the intelligent and interactive power system, the elements of distribution network planning continue to increase. The distribution network connects the transmission system and individuals, directly affects the individual's power consumption experience, and is a key link in the power system. A reasonable planning scheme can not only improve the power supply capacity and reliability of the distribution network but also fully apply the data of each system in the distribution network to realize the optimal planning of the medium-voltage distribution network driven by data. Firstly, this paper constructs the CIM model and the distribution network topology model and establishes the wiring pattern recognition feature library. The network reconstruction and planning method research was carried out for the target line, and a typical operation scenario of the distribution network was generated. At the same time, based on the time period network loss index, the distribution network reconfiguration optimization model and distribution network expansion planning model are established, and the solution method of the distribution network reconstruction and expansion planning model is expounded. A reconstruction optimization scheme with the best overall network loss performance is in the network operation scenario. The experimental results of the final example show that based on the proposed time period network loss index, the overall operation loss of the distribution network in a period can be calculated more accurately, and the optimized planning scheme is more suitable for the load power consumption characteristics of the region, and the method has certain feasibility. [ABSTRACT FROM AUTHOR]

We live in a world that is completely dependent on energy; thus, humankind can no longer live without power. With electricity being the main form of energy today, this has increased the complexity of our life today. In Uganda, electricity generation is mainly through hydropower, which puts the country in the bottleneck of overdependence on one source of energy. There are many energy systems out there that the country can use to diversify its electricity generation. Therefore, the need to understand the level of development and utilization of various energy systems has been the underlying question for this present study. A comprehensive literature survey was conducted using electronic databases, including ScienceDirect, Wiley, Sage, Scopus, Taylor & Francis, and Google Scholar. The publications in the form of reports, conference papers, working papers, discussion papers, journal articles, book sections, and textbooks were considered in this study. In total, 11 energy systems, including human and animal energy, solid biomass (firewood), hydropower, wind, geothermal, solar, nuclear, peat, coal, petroleum, and nonsolid biomass (methanol, hydrogen, ethanol, biodiesel, and biogas), are described. The current and future development and utilization of these energy systems have been described. The challenges for developing and utilizing these systems were elaborated on, and the solutions for their challenges were presented. Hydropower from the Nile River, being the main river for large hydropower plant construction, is the dominant energy system in Uganda. Nuclear energy will be the salvation for the country's electric energy supply in the near future. Therefore, Uganda needs to bet big on nuclear energy. [ABSTRACT FROM AUTHOR]