System Load Research Articles

Task-Importance-Oriented Task Selection and Allocation Scheme for Mobile Crowdsensing

In Mobile Crowdsensing (MCS), sensing tasks have different impacts and contributions to the whole system or specific targets, so the importance of the tasks is different. Since resources for performing tasks are usually limited, prioritizing the allocation of resources to more important tasks can ensure that key data or information can be collected promptly and accurately, thus improving overall efficiency and performance. Therefore, it is very important to consider the importance of tasks in the task selection and allocation of MCS. In this paper, a task queue is established, the importance of tasks, the ability of participants to perform tasks, and the stability of the task queue are considered, and a novel task selection and allocation scheme (TSAS) in the MCS system is designed. This scheme introduces the Lyapunov optimization method, which can be used to dynamically keep the task queue stable, balance the execution ability of participants and the system load, and perform more important tasks in different system states, even when the participants are limited. In addition, the Double Deep Q-Network (DDQN) method is introduced to improve on the traditional solution of the Lyapunov optimization problem, so this scheme has a certain predictive ability and foresight on the impact of future system states. This paper also proposes action-masking and iterative training methods for the MCS system, which can accelerate the training process of the neural network in the DDQN and improve the training effect. Experiments show that the TSAS based on the Lyapunov optimization method and DDQN performs better than other algorithms, considering the long-term stability of the queue, the number and importance of tasks to be executed, and the congestion degree of tasks.

Load prediction and energy efficiency improvement of steelmaking waste heat centralized heating systems under mild climate in China

HVAC energy consumption accounts for more than 50 % of the total building energy consumption globally and using industrial waste heat has huge potential for energy savings in central heating systems. Under the background of the use of renewable energy and the gradual rise of central heating in mild climate areas in China, it is worth paying attention to the problem of excessive energy consumption caused by the inaccurate load prediction of central heating systems when the ambient temperature changes. L City, Guizhou Province, is the first city with central heating in southern China. The temperature variation during winter in L City can manifest the climatic characteristics of a mild region. Therefore, based on the operational data of the steel waste heat central heating system in L City, Guizhou Province, China, during the heating season of 2022–2023, this study discusses the load prediction and energy-saving optimization strategy of the central heating system in a mild climate. SketchUp was used to conduct 3D modeling of the actual buildings in the heating area and TRNSYS was imported to simulate the system load. The difference between the heating load forecast and the actual operating data was analyzed in detail by using the calculation results. Based on the simulation results, the waste heat utilization strategy was adjusted and the influence of different heating schemes on the pump energy consumption under different load demands was discussed. The results reveal that based on the temperature change rule, the accurate load prediction can lead to a 28.64 % saving of the system energy. Concerning system energy consumption, by adjusting the water flow and choosing the large and small pump system, the energy saving amounts to 39.48 % compared with the traditional single-pump system. Meanwhile, in the transition season when the heating demand is less than 20 MW, the adjusted heating scheme can achieve a 38.5 % saving based on the constant flow scheme and a 5.4 % saving based on the constant temperature difference scheme. This article provides a scientific energy-saving solution for heating in mild climates using industrial waste heat, not only reducing energy consumption but also providing an important reference for the energy-saving renovation of heating systems in similar areas.

Microbial challenges and solutions in root canal therapy

Root canal therapy (RCT) faces significant challenges due to microbial invasion and biofilm formation within the root canal system. The intricate anatomy of root canals, including isthmuses, lateral canals, and apical deltas, provides a conducive environment for pathogenic microorganisms, complicating effective disinfection. Key pathogens such as Enterococcus faecalis and Candida albicans are highly resilient, capable of forming biofilms that protect them from antimicrobial agents and host defences. Current antimicrobial strategies in endodontics combine mechanical instrumentation with chemical disinfection. Mechanical debridement, though essential, often falls short of thoroughly cleaning the complex root canal anatomy. Sodium hypochlorite (NaOCl), the gold standard irrigant, exhibits broad-spectrum antimicrobial properties but struggles to penetrate biofilms and dentinal tubules fully. Adjunctive solutions like chlorhexidine (CHX) and ethylenediaminetetraacetic acid (EDTA) enhance the antimicrobial efficacy of NaOCl by providing prolonged action and removing the smear layer, respectively. Intracanal medicaments, such as calcium hydroxide (Ca(OH)2), are utilized to maintain antimicrobial activity between treatment sessions. Despite its high pH and effectiveness against a wide range of bacteria, Ca(OH)2 shows limited efficacy against biofilm-forming bacteria like Enterococcus faecalis. Innovations in irrigation techniques, such as passive ultrasonic irrigation (PUI) and laser-activated irrigation (LAI), have improved the delivery and activation of irrigants, enhancing their ability to disrupt biofilms and penetrate the root canal system. Nanoparticle-based irrigants and medicaments represent a promising advancement in endodontic disinfection. Their small size and large surface area allow for better penetration and interaction with bacterial cells, significantly reducing bacterial load in the root canal system. Combining traditional and advanced antimicrobial strategies is essential for overcoming the limitations of current methods and improving the success rates of endodontic therapy. Understanding the mechanisms of microbial invasion and biofilm formation, along with integrating emerging technologies, can lead to more effective disinfection protocols. This approach aims to enhance treatment outcomes and ultimately ensure the longevity of endodontically treated teeth.

The power control and efficiency optimization strategy of dynamic wireless charging system for multiple electric vehicles

PurposeThis study aims to solve the problem that both the speed and the required driving power of electric vehicles (EVs) will change during the dynamic wireless charging (DWC) process, making it difficult to charge EVs with a constant power considering the overall efficiency of DWC system, the numbers of EVs and the power supply capacity. Therefore, this paper proposes the power control and efficiency optimization strategies for multiple EVs.Design/methodology/approachThe wireless power charging system for multiple loads with a structure of double-sided LCC compensation topology is established. The expressions of optimal transmission efficiency and optimal equivalent impedance are derived. Taking the Tesla Model 3 as an example, a method to determine the number of EVs allowed by one transmitter coil and the overall charging power is proposed considering EV speed, power supply capacity, safe braking distance and overall efficiency. Then, the power control strategy, which can adapt to the changes of EV speed and the efficiency optimization strategy under different numbers of EVs are proposed.FindingsIn this paper, a method to determine the numbers of EVs allowed by one transmitter coil and the overall charging power is proposed considering EVs speed, power supply capacity, safe braking distance and overall efficiency. The accuracy of the charging power is good enough and the overall efficiency reaches a maximum of 91.79% when the load resistance changes from 5Ω to 20Ω.Originality/valueIn this paper, the power control and efficiency optimization strategy of DWC system for multiple EVs are proposed. Specifically, a method of designing the number of EVs and charging power allowed by one transmitter coil considering the factors of EV speed, power supply capacity, safe braking distance and overall efficiency is designed. The overall efficiency of the experiment reaches a maximum of 91.79% after adopting the optimization strategy.

Dynamic load shedding and system restoration using wide area distribution management system

AbstractThe demand for electric power has consistently been on the rise, owing to urbanisation and technological improvements. On the generation side, renewable sources have been favoured over their polluting, exhaustible, non‐renewable counterparts. These changes in the power system have necessitated a system for maintaining the supply‐demand balance, to maintain system stability. A complex power system is also more prone to blackouts and grid failure. Islanding helps in provision of supply to consumers in a microgrid, reducing the possibility of a blackout. Depending on the power demand and generation, loads need to be shed or restored to mitigate power imbalances. A wide area distribution management system (WADMS) is proposed to dynamically shed and restore loads in the islanded mode, with the aid of micro phasor measurement units (µPMUs). A priority and consumer‐based load shedding and restoration (PCLS) algorithm is proposed in the WADMS that preferentially sheds or restores loads based on their assigned load priority indices and number of consumers. The algorithm has been tested on a modified IEEE 13 bus system, incorporated with a solar photovoltaic (PV) system, diesel generators (DGs) and an energy storage system (ESS) in MATLAB Simulink.

Integration of Distributed Generations and electric vehicles in the distribution system

Power quality challenges are a major problem these days because of the significant increase in load demand and the existence of various reasons for disruptions in the distribution system. A voltage sag or a decrease in the magnitude of the line voltage can be brought on by rapid fluctuations in load or distribution system issues. Enhancing the grid current profile, reducing grid voltage sag, and improving other performances can all be achieved through the combination of electric vehicles with distribution generation systems. In this paper, the integration of Distributed Generation with Electric Vehicles in the distribution system has been done to enhance system performances such as true power loss index, imaginary power loss index, voltage deviation index, short circuit current index, cost function, and environmental impact reduction index for 16 bus distribution system in constant impedance, constant current, and constant power load models, or ZIP load models through proper location, sizing, coordinated controlling, and types of Distributed Generation and Electric Vehicle pairs for both charging and discharging modes by adopting Hybrid Genetic Algorithm - Monte Carlo Simulation optimization methodologies. This study's primary goal is to determine the optimal distribution generation and electric vehicle pairing to improve the aforementioned parameters, which will assist researchers in their future planning. Researchers, industry professionals, scientists, and individuals working with Distributed Generation and Electric vehicles in the smart grid will find this statement to be of great use.

Integrating interlayer ventilation into phase change walls: Comprehensive performance and optimization in summer

Integrating phase change materials (PCM) into building walls can improve the thermal performance of walls. However, phase change walls (PCW) in hot climates have poor thermal performance during nighttime due to the exothermic process of PCM. In this work, we proposed a novel PCW integrating with interlayer ventilation (IVPCW), which is innovative for all-day availability. Its comprehensive performance analyses and optimization were conducted. The results indicate that utilizing interlayer ventilation improved the comprehensive performance of the PCW. Moreover, the optimal phase change temperature range is [28 °C, 34 °C], the optimal phase change layer thickness is 30 mm, the optimal air delivery method is method 2, and the optimal air layer thickness is 50 mm. The average daily cooling load of IVPCW with the optimal parameter was 126.08 kJ/m2 during the cooling-season week, which was 43.7 % lower than the pre-optimization. Long-term performance analyses conclude that IVPCW can effectively reduce the cooling load, electrical cost, and CO2 emissions of air conditioning systems, which were 75.0 % lower compared with the ordinary wall (OW) and 59.1 % lower compared with PCW. In summary, IVPCW has a superior energy-saving and carbon-reducing performance to OW and PCW. This study established a theoretical foundation for the engineering implementation of IVPCW.

Implementation of a Small-Sized Mobile Robot with Road Detection, Sign Recognition, and Obstacle Avoidance

In this study, under the limited volume of 18 cm × 18 cm × 21 cm, a small-sized mobile robot is designed and implemented. It consists of a CPU, a GPU, a 2D LiDAR (Light Detection And Ranging), and two fisheye cameras to let the robot have good computing processing and graphics processing capabilities. In addition, three functions of road detection, sign recognition, and obstacle avoidance are implemented on this small-sized robot. For road detection, we divide the captured image into four areas and use Intel NUC to perform road detection calculations. The proposed method can significantly reduce the system load and also has a high processing speed of 25 frames per second (fps). For sign recognition, we use the YOLOv4-tiny model and a data augmentation strategy to significantly improve the computing performance of this model. From the experimental results, it can be seen that the mean Average Precision (mAP) of the used model has increased by 52.14%. For obstacle avoidance, a 2D LiDAR-based method with a distance-based filtering mechanism is proposed. The distance-based filtering mechanism is proposed to filter important data points and assign appropriate weights, which can effectively reduce the computational complexity and improve the robot’s response speed to avoid obstacles. Some results and actual experiments illustrate that the proposed methods for these three functions can be effectively completed in the implemented small-sized robot.

Effect of Bedding Angle on Energy and Failure Characteristics of Soft–Hard Interbedded Rock-like Specimen under Uniaxial Compression

To investigate how bedding planes affect the energy evolution and failure characteristics of transversely isotropic rock, uniaxial compression tests were conducted on soft–hard interbedded rock-like specimens with varying bedding angles (α) using the RMT-150B rock mechanics loading system. The test results indicate that throughout the loading process, the energy evolution shows obvious stage characteristics, and the change of α mainly affects the accelerating energy dissipation stage and the full energy release stage. With the increase of α, the ability of rock to resist deformation under the action of energy shows the characteristics of “strong–weak–strong”. The energy dissipation process is accelerated by medium angle bedding planes (α = 45°~60°). The precursor points of the ratios of dissipation energy to total energy (RDT) and elastic energy to dissipation energy (RED) can be used to effectively predict early failure. With the gradual increase of α, the difficulty of crack development is gradually reduced. The changes of energy storage limitation and release rate of releasable elastic energy are the immanent cause of different macroscopic failure modes of specimens with varying α.

Optimal regulation strategy of energy storage combined with new energy stations participating in power market

Abstract Energy storage systems can efficiently address the challenges of inadequate power grid regulation capabilities and the escalating complexity of maintaining frequency stability due to a significant integration of renewable energy sources. Under the background of power marketization reform, it is highly important to establish a perfect power market mechanism and incorporate energy storage into it to stimulate energy storage advancements. Consequently, this paper proposes an optimization model for energy storage in conjunction with new energy stations participating in the power market, following the introduction of new energy storage technologies. It effectively verifies the optimization energy storage charge and discharge duration with examples. It achieves the realignment of output curves for coordinating the operation of new energy stations and energy storage facilities, enabling peak shaving and load shifting to minimize the peak-to-valley differential in the system load. It makes the overall output more ‘stable’ and improves the efficiency of the comprehensive operation of wind, solar, and energy storage. In the future, the engagement of energy storage will be essential in the market, and it is crucial to propose practical suggestions to enhance its participation.

Disaggregation Estimation Method for Load and PV Output in the Distribution System using State Estimation and Independent Component Analysis

Disaggregation Estimation Method for Load and PV Output in the Distribution System using State Estimation and Independent Component Analysis

Virtual power plant optimization with demand response and multi-complementary energy planning

Abstract To effectively address the increasing complexity of system operation optimization and resource allocation caused by the continuous enrichment of supply-side resources and the flexible variability of demand-side loads in the energy system, a robust optimization model for virtual power plants considering demand response and multi-energy complementarity is proposed. Handling renewable energy and load uncertainty in virtual power plants by building a two-stage robust optimisation model. The introduction of robust coefficients allows for flexible adjustment of the conservatism of the optimized scheduling. Results indicate that considering demand response and multi-energy complementarity can effectively enhance the system’s robustness and flexibility.

X-Stor: A Cloud-Native NoSQL Database Service with Multi-Model Support

In recent years at Tencent, we have observed that the use of multiple NoSQL databases for storing business data with diverse models has led to increased programming and deployment costs, as well as inefficient maintenance and underutilized resources. In this paper, we report X-Stor, a cloud-native NoSQL database system that supports multiple data models by extending different storage engines and efficiently managing them through a unified control plane. This design significantly reduces expenses and enables rapid expansion of new models, while seamlessly supporting their complete functionality through storage engine extensions. By consolidating multi-tenant services and data models on the same physical machines, X-Stor significantly enhances the utilization of cluster resources. Additionally, X-Stor introduces a standardized metric called Request Unit (RU) to measure tenant resource consumption for consumption-based pricing purposes. Leveraging this metric, we design RU-based resource management strategies and achieve efficient multi-tenant resource isolation and system load balancing. Currently, X-Stor manages a storage capacity of over 12PB for online operational data, including more than 100,000 tables with multiple data models. It handles 700 billion requests per day with a peak of 30 million requests per second. We evaluate the performance of X-Stor on popular benchmarks and production workloads. The results show that X-Stor performs well under diverse data models.

Globally optimal sequencing of optimal reactive dispatch control adjustments to minimize operational losses in transmission systems by graph shortest path, parallel computing, and dynamic programming

Minimizing operational losses in transmission systems through the Optimal Reactive Dispatch (ORD), a non-convex mixed-integer nonlinear programming problem, is crucial for operational cost reduction, resource optimization, and greenhouse gas emission mitigation. Besides all intricacies associated with solving ORDs, transmission system operators encounter the challenge of determining sequences in which ORD control adjustments must be implemented before significant changes occur in generators scheduled power output and system loading. Sequencing ORD control adjustments, in spite of not being novel, remains modestly scrutinized in the literature. This paper introduces a two-phase framework that tackles the globally optimal sequencing of n ORD control adjustments over n! potential paths by solving the ORD to minimize operational losses in transmission systems in the first phase, and optimally sequencing ORD control adjustments employing fast power flow calculations, graph shortest path, parallel computing, and dynamic programming in the second phase. We discuss the framework’s second phase asymptotic time complexity, which is exponential over factorial for brute-force approaches, and its capability to guarantee globally optimal paths toward minimal operational losses determined in the framework’s first phase. ORD control adjustments for transmission systems with up to 27 controllable variables are benchmarked against two mixed-integer nonlinear programming solvers: BARON, a global non-convex solver, and Knitro, a local solver (assuming convexity around local optima). Globally optimal sequences of ORD control adjustments over n! potential paths (more than 1028 for sequencing 27 control adjustments) and average algorithm runtimes validate the straightforward application and, more importantly, effectiveness of such a comprehensive framework.

Optimising design and operation of sewage source heat pump: techno-economic and environmental assessment

ABSTRACT Heat pump can be used to recover abundant thermal energy contained in the discharge of municipal wastewater treatment plants. While there are some design standards for common heat pump systems, the design of a sewage source heat pump (SSHP) system is still often based on a fixed heat load and neglects the interdependencies between the equipment sizing and operating parameters. To address the issue that previous design methods have not balanced investment and operational costs well from a global optimisation perspective, this work formulates the simultaneous optimisation of SSHP design and operation as a non-linear programming problem. The proposed model features the consideration of multiple working conditions caused by the impact of ambient temperature variation on the heat load of the SSHP system. The feasibility and potential benefits of the optimised SSHP system are also evaluated by incorporating techno-economic performances and environmental impact analyses into the mathematical framework. A case study is carried out to demonstrate the effectiveness of the proposed methodology. The results show that the total annual cost of the optimally designed and operated SSHP in Harbin could be 9% lower than in Beijing and 39% lower than in Shanghai, suggesting that constructing and running the SSHP system in severe cold regions with great heating demands might be more economical than in less cold regions. The CO2, SO2, and NOx emissions of the SSHP could be approximately 50% less than that of coal-fired boiler heating, and 80% less than that of direct electric heating with coal-fired electricity.

Shear Behavior of Reactive Powder Concrete Ferrocement Beams with Light Weight Core Material

In this paper, the shear behavior of ferro-cement hollow beams is investigated experimentally and analytically. Ten reinforced concrete beams with cross-sectional dimensions of 100 × 200 × 1300 mm and a clear span of 1000 mm were cast and tested until failure under a two-point loading system. Ferrocement beams in this research contained either an autoclaved aerated lightweight brick core (AAC) or an extruded foam core (EFC) and were reinforced with either expanded metal mesh (EMM) or welded wire mesh (WWM). The structural behavior of the studied beams, including first crack, deflection, ultimate load, crack pattern, failure mode, and ductility index, was investigated. The experimental data were used to validate finite element models created with the ABAQUS finite element program. It can be concluded that the optimum performance of ferrocement beams can be achieved using beams with a second layer of expanded steel mesh as additional reinforcement, which led to an increase in the ultimate load and maximum deflection by 12.9% and 22.8%, respectively. Furthermore, the Numerical results agreed with the experimental results, where the ratio between the NLFE ultimate loads and the experimental ultimate loads varies between 1.02 and 1.07, with an average ratio of 1.04.

Enhancing power quality in grid-connected hybrid renewable energy systems using UPQC and optimized O-FOPID

Hybrid Renewable Energy Systems (HRES) have recently been proposed as a way to improve dependability and reduce losses in grid-connected load systems. This research study suggests a novel hybrid optimization technique that regulates UPQC in order to address the Power Quality (PQ) problems in the HRES system. The load system serves as the primary link between the battery energy storage systems (BESS), wind turbine (WT), and solar photovoltaic (PV) components of the HRES system. The major objective of the study is to reduce PQ issues and make up for the load requirement inside the HRES system. The addition of an Optimized Fractional Order Proportional Integral Derivative (O-FOPID) controller improves the efficiency of the UPQC. The Crow-Tunicate Swarm Optimization Algorithm (CT-SOA), an enhanced variant of the traditional Tunicate Swarm Optimization (TSA) and Crow Search Optimization (CSO), is used to optimize the control parameters of the FOPID controller. Utilizing the MATLAB/Simulink platform, the proposed method is put into practice, and the system’s performance is assessed for sag, swell, and Total Harmonic Distortion (THD). The THD values for the PI, FOPID, and CSA techniques, respectively, are 5.9038%, 4.9592%, and 3.7027%, under the sag condition. This validates the superiority of the proposed approach over existing approaches.

Power system loading margin enhancement based on a multi-objective TCSC placement to mitigate the risk of blackouts

In the evolving power systems, loading margin (LM) enhancement has a significant effect on the mitigation of the risk of cascading line outages and thereby the occurrence of large blackouts. On the other hand, flexible alternating current transmission system (FACTS) devices, especially thyristor-controlled series compensated (TCSC) devices, are more effective than other mitigation strategies such as constructing new lines because of environmental and economic reasons. In this paper, a multi-objective framework is presented to maximize the LM with a minimum installation cost of TCSCs. The optimization problem is solved by using the ɛ-constraint method. The benefit of each Pareto solution is calculated by the probabilistic risk assessment, and the most preferred one is selected by a benefit/cost analysis. The proposed method is developed based on a modified alternating current (AC) version of the ORNL–Pserc–Alaska (OPA) model inspired by the self-organized criticality (SOC) theory in complex power grids. Using the benefit/cost criterion, it is revealed that by the proposed approach, a larger value for the most preferred Pareto solution is obtained in comparison with the defined transmission expansion planning (TEP) scenarios. It confirms the suitability of the proposed method to suppress the cascading outages instead of expensive and time-consuming policies such as building new lines.

Coordinated Control of Constant Output Voltage and Maximum Efficiency in Wireless Power Transfer Systems

This article presents a coordinated control method used for wireless power transfer (WPT) systems. This method can improve WPT system transmission efficiency while maintaining the constant output voltage. First, the topology of the DC–DC converter is selected and the equivalent circuit model of the WPT system is established. Then, the WPT system characteristics are discussed and the mutual inductance estimation process is presented. Furthermore, the coordinated control method is proposed, where the constant voltage output is achieved by connecting the Buck–Boost converter after the diode rectifier. Meanwhile, the optimal phase shift angle is calculated and sent to the controller to achieve maximum transmission efficiency tracking control, according to the measured load voltage and current. Finally, simulations and experiments are adopted to verify the proposed coordinated control method. The experimental results indicate that the average system transmission efficiency is increased by 1.80% and the efficiency fluctuation is decreased by 2.67% when the system load resistance varies, while the average system transmission efficiency is increased by 1.80%, and the efficiency fluctuation is decreased by 3.14% when the mutual inductance changes. This means the proposed coordinated control method is effective under the conditions of the WPT load and mutual inductance variations.

Reservation of the heat supply system with biofuel heat energy sources

The implementation of the reservation of the heat supply system to a populated area was analyzed. It is noted that increasing the reliability of heat supply to consumers is a fundamental task, especially given the current military situation in Ukraine. The solution to this difficulty is considered when reserving heat supply by constructing a backup source of thermal energy using alternative types of fuel. A combined thermal scheme for connecting a hot water heat generator using biofuel to the existing city heat supply system is proposed to increase its reliability and survivability, which ensures the efficiency and reliability of operation of the heat supply system during the heating and summer periods. It is shown the possibility of operating a solid fuel heat generator with its power significantly less than the total heat load of the city's heating supply in several operating modes: during the heating period - for heating the return coolant at the inlet to the existing gas boiler house; in the summer period - for the needs of hot water supply in full; in the event of an emergency shutdown of a gas boiler house in winter - to maintain the viability of the heat supply system. The results of a calculation analysis of the operation of a 5,0 MW biofuel heat generator under different operating modes are presented. Heating the return coolant by 4...5 °C in winter, in addition to saving natural gas, prevents low-temperature corrosion of existing gas boilers. In the event of an emergency lack of natural gas supply, the combustion of wood chips ensures the production of thermal energy at the level of thermal losses of the heating network while maintaining the coolant temperature at least 3 °C at the calculated external air temperature. Thanks to the accumulation of thermal energy at night in the summer period in the volume of heating network pipes as a buffer tank, compensation for peak hot water consumption is provided with the available power of the solid fuel boiler house equal to the average load of the hot water supply system. When implementing a project to back up the heat supply system in Lutsk using a combined thermal scheme for connecting a solid fuel boiler house, an increase in the reliability and viability of the system and natural gas savings of 40,5% during a year of operation were achieved