Power Consumption Management Research Articles

Development of mathematical models of power consumption at coal plants

The object of research is coal enterprises where powerful electrical equipment is used (underground installations up to 3000 kW, tunneling complexes of 500–1500 kW, technological complexes of 6–10/0.4 kV, etc.). The deficit of generated capacities, caused by growing energy consumption, can be reduced by regulating power consumption modes. The relevance of this issue is determined by the need to conduct a research of the electricity consumption system, to determine the qualitative and quantitative characteristics of electricity consumption using mathematical models. The lack of mathematical models makes it difficult to analyze energy intensity and consumption modes of each technological operation in the overall balance of electricity consumption of coal enterprises. The article considers the structure and classification of the main technological groups of energy consumers, the development of mathematical models for each type of load modes, as well as a generalized model of electricity consumption of coal enterprises, with the use of mathematical apparatus of probability theory and mathematical statistics. As a result of the work, mathematical models of the electricity consumption process for the main technological groups and for models of daily electricity consumption of coal enterprises as a whole were developed, and it was also established that technological objects of electricity consumption are divided into three different values, in terms of power consumption modes: constant, uniform and pulse. For each class of consumers their statistical characteristics were obtained. The work results can be applied for managing power consumption modes of coal enterprises

Dynamic Clock Switching Scheme for Enhanced Power Efficiency in System-on-Chip Architectures

High power consumption and thermal management are critical challenges in traditional System- on-Chip (SoC) architectures due to multiple active clock sources feeding individual IP blocks, even during idle states. This paper introduces a dynamic clock switching power-saving scheme that consolidates clock sources by dynamically switching to a single lower-frequency clock during low-power modes. A Clock Control Agent (CCA) monitors real-time operational status, power consumption, and performance needs, leveraging AI for predictive adjustments. Experimental results demonstrate a significant reduction in idle power consumption and improved thermal management, without compromising performance or data integrity. This scheme addresses inefficiencies in existing methods such as Dynamic Voltage and Frequency Scaling (DVFS) and clock gating, offering a robust and efficient solution for next-generation SoC designs.

A MATLAB APP FOR BALANCING A CAPACITOR BANK IN THE DOUBLE STAR CONNECTION - NEUTRAL, NOT CONNECTED

This study presents a methodology for maintaining and rebalancing Double Star configuration capacitor banks, which are critical for managing power consumption peaks. As electricity demand is projected to rise by 21% by 2040, efficient maintenance methods are essential to ensure energy security. The paper introduces a novel approach to rebalance capacitor banks using a Matlab application, which simulates the unbalance current that may occur due to capacitive unit failures. Key aspects of capacitor banks, such as construction, connections, and protection methods, were reviewed to inform the methodology. Capacitor banks are used to control voltage, correct power factor, and reduce losses, but they can experience imbalances due to natural variances in capacitance or failures. The study developed a mathematical model to calculate the unbalance current and validated this model through simulations in LTspice software. The resulting rebalancing flowchart provides a step-by-step guide for technicians to follow during maintenance. The project achieved its goals, demonstrating through 24 simulations that the developed methodology and Matlab application could successfully predict unbalance currents with a negligible margin of error. The paper concludes that the methodology is ready for implementation, and each capacitor bank will require a tailored rebalancing application based on the presented calculations.

Energy-Efficient Handover Algorithm for Sustainable Mobile Networks: Balancing Connectivity and Power Consumption

In the era of pervasive mobile and heterogeneous networks, maintaining seamless connectivity during handover events while minimizing energy consumption is paramount. Traditional handover mechanisms prioritize metrics such as signal strength, user mobility, and network load, often neglecting the critical aspect of energy consumption. This study presents a novel approach to handover decision-making in mobile networks by incorporating energy-related metrics, such as battery level, energy consumption rate, and environmental context, to make informed handover decisions that balance connectivity quality and energy efficiency. Unlike traditional methods that primarily focus on signal strength and network load, our approach addresses the critical need for energy efficiency, particularly in high-mobility scenarios. This innovative framework not only enhances connectivity but also significantly improves power consumption management, offering a more sustainable solution for modern mobile networks. Through extensive simulations, we demonstrate the effectiveness of our proposed solution in reducing energy usage without compromising network performance. The results reveal significant improvements in energy savings for mobile devices, especially under high-mobility scenarios and varying network conditions. By prioritizing energy-efficient handovers, our approach not only extends the battery life of mobile devices but also contributes to the overall sustainability of mobile networks. This paper underscores the importance of incorporating energy metrics into handover decisions and sets the stage for future research in energy-aware network management.

Optimizing High-Speed Serial Links for Multicore Processors and Network Interfaces

In modern computing environments, high-speed serial links have become a critical component for ensuring efficient data transfer between multicore processors and network interfaces. These links, characterized by their ability to transmit data at very high rates over single or multiple lanes, are essential for meeting the increasing bandwidth demands of contemporary applications. The optimization of these serial links is crucial for maintaining performance, reliability, and energy efficiency in systems that leverage multicore processors and advanced network interfaces. This paper explores the key strategies for optimizing high-speed serial links in the context of multicore processors and network interfaces. We begin by examining the architectural considerations and design principles that influence the performance of serial links, including signal integrity, power consumption, and thermal management. We also discuss the impact of link speed and data encoding techniques on overall system efficiency. One of the central challenges in optimizing high-speed serial links is managing signal integrity across different operating conditions. Techniques such as equalization and adaptive filtering are essential for mitigating the effects of signal degradation due to channel impairments and crosstalk. The paper provides a detailed analysis of these techniques, including their implementation in both hardware and software.

Optimizing High-Speed Serial Links for Multicore Processors and Network Interfaces

In modern computing environments, high-speed serial links have become a critical component for ensuring efficient data transfer between multicore processors and network interfaces. These links, characterized by their ability to transmit data at very high rates over single or multiple lanes, are essential for meeting the increasing bandwidth demands of contemporary applications. The optimization of these serial links is crucial for maintaining performance, reliability, and energy efficiency in systems that leverage multicore processors and advanced network interfaces. This paper explores the key strategies for optimizing high-speed serial links in the context of multicore processors and network interfaces. We begin by examining the architectural considerations and design principles that influence the performance of serial links, including signal integrity, power consumption, and thermal management. We also discuss the impact of link speed and data encoding techniques on overall system efficiency.

An IoT Based Smart Watt Metering for Energy Management System

This paper presents the analysis of IoT based Smart Watt Metering for Energy Management System. The proposed system, "An IoT Based Smart Watt Metering for Energy Management System," introduces a cost-effective and energy-efficient solution for monitoring and managing power consumption of electrical appliances. Utilizing non-intrusive current and voltage sensors, the system measures key electrical parameters such as current, voltage, and power factor in real-time. The ESP32 microcontroller, known for its low power consumption, processes and transmits this data via Wi-Fi to a central server or cloud platform, enabling remote access through a user-friendly web or mobile application. The interface allows users to visualize power usage trends, set consumption thresholds, and receive alerts for abnormal usage. Additionally, the system incorporates data logging capabilities, storing historical consumption data for long-term analysis and energy-saving decision-making. This innovative approach provides a detailed and accessible means for users to optimize energy usage and reduce costs, contributing to more efficient energy management amidst the global energy crisis

Optimizing High-Speed Serial Links for Multicore Processors and Network Interfaces

In modern computing environments, high-speed serial links have become a critical component for ensuring efficient data transfer between multicore processors and network interfaces. These links, characterized by their ability to transmit data at very high rates over single or multiple lanes, are essential for meeting the increasing bandwidth demands of contemporary applications. The optimization of these serial links is crucial for maintaining performance, reliability, and energy efficiency in systems that leverage multicore processors and advanced network interfaces.This paper explores the key strategies for optimizing high-speed serial links in the context of multicore processors and network interfaces. We begin by examining the architectural considerations and design principles that influence the performance of serial links, including signal integrity, power consumption, and thermal management. We also discuss the impact of link speed and data encoding techniques on overall system efficiency.

Towards Energy-Aware Federated Learning via Collaborative Computing Approach

This research delves into the consequences of the high complexity of on-device operations executed during the federated learning process. We investigate how the varying computational capabilities and battery levels among mobile devices can introduce performance disparities and influence training quality. Hence, in order to deal with these challenges, we propose EAFL+, a novel energy optimization technique, that focuses on managing power consumption in devices with limited battery capacity. EAFL+ is a cloud–edge–terminal collaborative approach that provides a new architectural design for achieving power-aware FL training by leveraging resource diversity and computation offloading. The innovative scheme enables the efficient selection of an approximately-optimal offloading target, from a set of Cloud-tier, Edge-tier, and Terminal-tier resources and achieves the best cost-quality tradeoff for the devices taking part in the FL system. Our evaluation shows EAFL+ can help conserve the devices’ energy participating in training, which improves the participation rates and increases the clients’ contributions, hence achieving higher accuracy and faster convergence. Through experiments on real datasets and traces in an emulated FL environment, EAFL+ reduces the drop-out of clients to zero and enhances accuracy by up to 24% and 9% compared to EAFL and Oort, respectively.

Design and Implementation of an SGX Based Electricity Information Collection and Management System

With the rapid growth of the number and scale of smart grid users, traditional data encryption transmission methods can no longer meet the performance requirements of data aggregation. In response, a power consumption information collection and management system based on SGX software protection extension is proposed. The system mainly consists of three parts: user electricity data acquisition terminal, SGX data security processing and distributed storage module on the chain, and data monitoring management display platform. The user electricity data collection terminal collects electricity data from various buildings, residences, rooms, and other smart meters, analyzes and uploads it. After calling the trusted function of SGX technology, it enters the security zone provided by SGX for data processing. Finally, the data security processing results and data are uploaded to the blockchain for storage. In order to visually display user electricity usage data, an intelligent monitoring platform for user electricity collection and management has been established. This system can reduce the workload of user electricity data collection, ensure the accuracy of data collection, and provide an efficient and highly reliable system platform for user electricity data management.

Novel AMI in Zigbee Satellite Network Based on Heterogeneous Wireless Sensor Network for Global Machine-to-Machine Connectivity

This study endeavored to enhance the efficiency and utility of microcomputer meters. In the past, their role was predominantly confined to remote meter reading, entailing high construction and communication transmission costs, coupled with subsequent maintenance and operational expenditures. These factors collectively impacted the enthusiasm of various stakeholders to invest in this realm. Hence, in alignment with the smart city development initiative, the natural gas industry has pioneered the establishment of an advanced metering infrastructure with heterogeneous wireless sensor networks (HWSNs) at its core. This visionary leap incorporates global machine-to-machine connectivity (G-M2MC) technology, interconnecting all facets of its operations, thereby positioning itself as a trailblazer within the industry. While advancing this endeavor, the project’s scheduling aligns with the enterprise’s sustainability goals in the early stages of digital transformation. This strategic allocation of resources is responsive to government policies and aspires to cultivate a digitally connected smart green energy hub, thereby expediting the transformation of the living environment. The objective is to provide a stable, secure, cost-effective, and reliable system that can be shared among peers. Furthermore, this study delved into the analysis of congestion avoidance in intelligent Zigbee satellite transport networks based on the HWSNs-GM2MC of non-synchronous satellite orbit system (NGSO) pivotal technologies, utilizing them to integrate the smart LNGas management system (SGMS). Concurrently, it developed application services through the smart meter application interface (SMAPI), distinct from conventional microcomputer meters. However, it is imperative to acknowledge that cloud computing, while processing sensitive data, grapples with issues of latency, privacy, efficiency, power consumption, and zero-trust security risk information management and ethical authority management capabilities in the defense of disaster relief responses.

Research on the design and balancing strategy with multi-section lithium battery protection IC

This study uses 0.5um BCD technology to design a multi section lithium battery protection chip suitable for small mobile electronic devices such as Bluetooth earphones and smart bracelets. By providing a complete system structure diagram, the principles and design methods of several key circuits in the chip were introduced. This chip can effectively monitor the voltage of lithium batteries, take protective measures against abnormal situations such as overcharging, discharging, and overcurrent, and has a balancing module with dynamic power consumption management function. The balancing module adopts a passive balancing architecture, and the balancing algorithm used is an average based voltage detection algorithm. Using this algorithm can make the balancing system structure simple and reduce system power consumption, reduce the differences between batteries within the group, and thus improve the uniformity of the battery pack. The overcharging detection accuracy of the designed chip is ±25mV, the overcharging detection accuracy is 50mV, and the static working current of the chip is 7uA.

Optimizing Energy Consumption in Buildings: Intelligent Power Management Through Machine Learning

In the realm of energy conservation, managing power consumption within buildings emerges as a pivotal challenge. This study introduces sophisticated models that optimize energy usage by intelligently managing power distribution in various zones of a building. To achieve this, four machine learning classifiers, Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) algorithm, and Naive Bayes (NB), were employed. These classifiers were integrated with feature reduction techniques, namely Boruta and Principal Component Analysis (PCA), to diminish model complexity. The study delineates three distinct power management strategies: Full, Selected, and Shutdown. The effectiveness of these models was evaluated using a dataset obtained from a building's energy consumption measurements. A comparative analysis revealed that the integration of the RF classifier with the Boruta feature reduction method significantly excelled, achieving a classification accuracy of 98%. Additionally, this combination demonstrated an execution time of merely 0.4549 seconds. The findings of this research not only underscore the efficacy of combining specific machine learning classifiers with feature reduction techniques but also highlight the potential of such integrations in optimizing energy consumption in building environments. This approach paves the way for more energy-efficient and sustainable building management practices.

Ключевые направления и мировые практики повышения эффективности и надежности водоснабжения

The article reviews modern research and practical developments in the field of increasing the efficiency and reliability of water supply systems. The analysis covers both the micro level, which includes optimizing the opera-tion of individual components of the system, and the macro level, related to the management and optimization of the water supply system as a whole. Particular attention is paid to improving the efficiency of pumping units, leaks and pressure losses management, as well as water treatment processes optimization. At the macro level, the article presents a review of strategies for the optimal distribution of water between water intakes, a study of the zones of water intakes influence on the hydraulic network, dynamic pressure planning and power consump-tion management of pumping stations. The importance of monitoring and diagnostics systems for increasing the reliability of water supply systems is emphasized. Key areas of research and current problems are identified, and promising approaches to improving the management of urban water supply systems are proposed

Artificial neural network-based home energy management system for smart homes

<p>Energy efficiency is widely recognized as one of the most significant and economical ways to lower greenhouse gas (GHG) emissions. The aims and goals are that smart meters can evaluate and communicate in-depth real-time electricity usage, enable remote real-time monitoring and management of power consumption, and provide consumers with real-time pricing and analyzed usage information. The house energy management controller decides which loads will be powered based on the real home energy demands and the predefined load priorities. Artificial intelligence (AI) is being used increasingly in control applications due to its great effectiveness and efficiency. As a result, in this work, the author designed, simulated, and optimized an artificial neural network-based model simulation framework that simulates a home with a variety of home appliances and optimizes the total energy consumption of the home realistically through intelligent control of home appliances. The MATLAB application was used to model and examine the performance of four common household appliances: the water heater (WH), washing machine (WM), air conditioner (AC), and refrigerator (RG). The result shows a considerable reduction and savings in energy consumption without a decrease in consumer comfort.<strong></strong></p>

A refined decision-making method for orderly power consumption in virtual power plants considering load characteristics

Abstract In recent years, electricity consumption has been increasing, while the supply of electricity is relatively lagging, and power shortages often occur. Therefore, the management of organized power consumption is an urgent task for the current power industry. Based on the characteristics of power load, this paper constructs a decision-making management framework for orderly power consumption by considering four aspects: generation, grid side, demand side, and social benefit. Through the various types of flexible load resources aggregated within the virtual power plant, it participates in regulating its power, effectively reduces the peak-valley difference, and realizes refined, orderly power consumption management. Finally, based on the multi-objective optimization model, refined decision-making for orderly power consumption with the participation of virtual power plants is constructed and compared to traditional decision-making for orderly power consumption. The results show that compared with conventional decision-making, the peak-to-valley difference is reduced by 8.7789 WM, the economic benefit of the power enterprise is improved by 228,300 yuan, and customer satisfaction is enhanced by 0.1613, which verifies the effectiveness of the decision-making method of the sequential power consumption in this paper.

Energy­efficient routing protocol in wireless sensor networks

Recently, self-organizing networks have gained popularity, which are distinguished by the possibility of connecting terminals anywhere and at any time without the need for a centralized network infrastructure. Mobile self-organizing networks are a promising direction for the development of information and communication systems. However, one should take into account the problems arising from the very nature of such networks: mobile nodes lead to an increase in the dynamism of the network topology due to the possibility of disconnection and the probability of movement; the limited supply of power sources of nodes requires careful management of power consumption in the design of hardware and protocols. Energy-saving design in self-organizing networks is more complex and important than in other wireless networks. The lack of infrastructure requires mobile nodes to act as routers, which increases traffic and therefore power consumption. Also, the energy-saving mode must take into account different network performance criteria, which can be caused by different protocols and power limitations. Thanks to the latest advances in technology and the rapid development of the production of small and affordable sensors, it has become possible to use them technically and economically effectively. These electronic sensors, which have high sensitivity, measure environmental parameters and convert the received data into an electronic signal that is transmitted over a wireless channel. After processing this signal, you can get information about the properties of objects or events that occur near the sensor. The application of a large number of such sensors, which can monitor a large area and exchange data wirelessly, forms a single wireless sensor network. Route allocation is a key challenge for resource-constrained networks. With the growth of computing power in wireless sensor technology, these sensor nodes are required to be equipped with more and more capabilities to perform more complex tasks. Each sensor is limited by its energy level, processing power, and ability to perceive information. Thus, the network of these sensors forms a more reliable and accurate structure. The article discusses the issue of using the HABRP protocol for its application in wireless sensor networks. The use of the HABRP protocol allows for improvement in the efficiency of wireless sensor networks, namely their energy efficiency. Therefore, the energy efficiency and simplicity of the HABRP protocol make it an excellent candidate for use in wireless sensor networks.

Study on graphs of electrical load and analysis of power consumption modes

The issues of planning and managing power consumption modes are considered, using electrical load schedules, comparisons of the declared maximum load with the actual one are presented, the main directions for increasing the efficiency of rational use of electricity and regulating electrical load schedules are given.

Study on the Change of Power Consumption Structure in Xumai Township, Nimu County, Lhasa, Xizang

The research on the evolution of rural power consumption structure in Xizang reflects the information on rural power development in Xizang, and the information on rural power development shows the people's living standards and demand trends. In order to understand the depth of changes in the power consumption structure of farmers and herdsmen households, the project team conducted a field survey of the changes in power supply sources, power consumption structure, power consumption equipment, and operation and management status of Sumai Township, a typical village in Nimu County, Lhasa City, Xizang over the years, and explored the history and trend of power development in remote villages in Xizang. Field research has found that before 2014, the township mainly relied on small-scale independent solar photovoltaic power supply, which was almost used for lighting; Since the implementation of the State Grid in 2014, electricity consumption has shifted from low voltage to wide voltage. In the past three years, the annual electricity consumption growth rate in Xumai Township has exceeded 6%, and the power supply stability has reached 90%. At the same time, the township utilizes solar photovoltaic street lights. The electrical appliances used have developed from a single lamp to power appliances such as household appliances, agricultural electric vehicles, tsamba machines and oil presses. The distribution line of the township is gradually being replaced with medium and low voltage, and is equipped with a 35kV Xumai substation. The 10kV medium and low voltage distribution network project of the rural power grid is gradually being put into operation and put into practice. However, research has found that villagers hope to use more clean energy such as solar energy for power supply and heating, in order to reduce the cost of electricity and gas bills.

CONTROL OF VOLTAGE MODES AS A MEANS OF LOAD REGULATION

The article substantiates the regulation of power consumption modes of production facilities due to the management of voltage modes in the power supply system. It is shown that the regulation of electricity consumption with the help of consumers - regulators (CR) is quite often associated with losses that occur when the CR is turned off. Studies have shown that the management of voltage modes in the power supply system, which affects its level, can play a positive role in increasing the efficiency of the optimization of power consumption modes. It is shown that a comprehensive solution to the problem of power consumption management is expedient both at the expense of CR control and the influence on the voltage regimes of the power supply system. It is shown that the regulating effect of the load is characterized by static characteristics. Solving the problem of forming static characteristics is possible in two ways: through active experiments and based on the integration of static characteristics of individual consumers. Experimental studies were conducted to identify the static characteristics of the load. Experiments were carried out on transformers of the main step-down substation of the chemical industry enterprise, equipped with on-load tap-changers. Experimental static characteristics with linear changes are constructed. The parameters of the model are determined by the method of least squares. Constructed experimental and analytical static characteristics. The voltage mode control procedure for load regulation is implemented in the power consumption control system.