System Load Research Articles

Behind-the-Meter Load and PV Disaggregation via Deep Spatiotemporal Graph Generative Sparse Coding With Capsule Network.

Nowadays, rooftop photovoltaic (PV) panels are getting enormous attention as clean and sustainable sources of energy due to the increasing energy demand, depreciating physical assets, and global environmental challenges. In residential areas, the large-scale integration of these generation resources influences the customer load profile and introduces uncertainty to the distribution system's net load. Since such resources are typically located behind the meter (BtM), an accurate estimation of BtM load and PV power will be crucial for distribution network operation. This article proposes the spatiotemporal graph sparse coding (SC) capsule network that incorporates SC into deep generative graph modeling and capsule networks for accurate BtM load and PV generation estimation. A set of neighboring residential units are modeled as a dynamic graph in which the edges represent the correlation among their net demands. A generative encoder-decoder model, i.e., spectral graph convolution (SGC) attention peephole long short-term memory (PLSTM), is devised to extract the highly nonlinear spatiotemporal patterns from the formed dynamic graph. Later, to enrich the latent space sparsity, a dictionary is learned in the hidden layer of the proposed encoder-decoder, and the corresponding sparse codes are procured. Such sparse representation is used by a capsule network to estimate the BtM PV generation and the load of the entire residential units. Experimental results on two real-world energy disaggregation (ED) datasets, Pecan Street and Ausgrid, demonstrate more than 9.8% and 6.3% root mean square error (RMSE) improvements in BtM PV and load estimation over the state-of-the-art, respectively.

Real Time Prediction and Modelling of Power Grid System Stability and Load Balancing

Abstract The extensive geographical distribution of the power system determines its highly complex structure. The system includes key components such as generators, substations, transmission and distribution grids, and users. This system has characteristics such as nonlinearity, time-varying, and parameter uncertainty. Power system stability is inherently linked to its regular operational performance. This article uses Simulink models to simulate the working state of generators and circuit breakers during faults. Under the conditions of a synchronous generator with a rated power of 350MW, a voltage of 10.5KV, a three-phase parallel RLC load rated voltage of 115KV, and a power consumption of 300MW, when a short occurs on line, the speed and relative angle of the generator tend to a fixed value, and the system remains stable. When a fault occurs in the circuit within 0.1 seconds, the circuit breaker will cut off the faulty line within 0.6 seconds. Avoiding the occurrence of oscillations and unstable states in typical dynamic systems.

Reliability-constrained configuration optimization for integrated power and natural gas energy systems: A stochastic approach

With the escalating dependence on electricity and natural gas infrastructure, ensuring both reliability and economic efficiency becomes paramount. It necessitates reliability centric measures to mitigate disruptions that could cascade between these interconnected systems. To address this challenges, this paper introduces a reliability-constrained two-stage stochastic model to optimize power-to-gas (P2 G) and gas-to-power (G2P) unit placement and sizing, aiming to enhance the reliability of both systems under stochastic scenarios. The proposed model, employing Sequential Monte Carlo (SMC) within its optimization framework, seeks to minimize investment, operation, and reliability costs. By addressing temporal uncertainties in component outages for both systems and considering uncertainties in power and gas system loads with a high temporal resolution and annual load growth, the model provides a comprehensive reliability perspective. Furthermore, sensitivity analysis is conducted to explore the impact of varying Values of Lost Load (VOLL) on the planning results. Numerical evaluation, using two integrated energy systems including IEEE 14-bus-10-gas node, and large-scale energy systems including IEEE 118-bus-85-gas node integrated power-gas system (IPGS), demonstrates a significant 12.53 % improvement in overall system reliability. Furthermore, a 2.81 % reduction in operation costs and a substantial 26.3 % reduction in reliability costs, validating the effectiveness of the proposed model.

A Biomechanical Analysis of Oblique Metacarpal Metadiaphyseal Fracture Fixation in a Cadaver Model

Although metacarpal fractures are typically managed nonoperatively, when surgical management is indicated, metacarpal fractures are commonly treated with crossed Kirschner wires (K-wires), which may limit early range of motion. Intramedullary implants are increasing in use with the potential advantage of early range of motion; however, stability in oblique metacarpal neck fractures remains a theoretical concern. The purpose of this study was to determine the biomechanical stability of noncompressive intramedullary fixation for oblique metacarpal neck fractures compared with crossed K-wire fixation. The index, long, and small metacarpals were harvested from three matched pairs of fresh-frozen cadavers. Oblique fractures at the metadiaphyseal region were created in each metacarpal. Each metacarpal was randomized to noncompressive, threaded intramedullary nail fixation or fixation with two crossed K-wires. Specimens were mounted in a Materials Testing System load frame and axially loaded until failure. Load to failure (LTF), stiffness, and load to 2 mm displacement were calculated from load-displacement curves. Differences in peak LTF, stiffness, and load to 2 mm displacement between noncompressive intramedullary fixation and crossed K-wire fixation were evaluated. The noncompressive intramedullary fixation cohort had a significantly higher LTF (1,190.9 ± 534.7 N vs 297.0 ± 156.0 N) and stiffness (551.3 ± 164.6 N/mm vs 283.0 ± 194.5 N/mm) when compared with the crossed K-wire fixation cohort. Load at 2 mm displacement was greater in the noncompressive intramedullary fixation cohort compared with crossed K-wire fixation (820.5 ± 203.9 vs 514.1 ± 259.6). For oblique metadiaphyseal metacarpal fractures, noncompressive intramedullary fixation provides a biomechanically superior construct under axial loading in terms of LTF, stiffness, and load to 2 mm of displacement compared with crossed K-wire fixation. Noncompressive intramedullary nails may be an alternative to K-wire fixation for the treatment of oblique metadiaphyseal metacarpal fractures.

Loss characteristics and optimization method of a compressed air energy storage radial turbine with nozzle governing

During the operation of the compressed air energy storage (CAES) system, a discrepancy exists between the air storage pressure and the turbine inlet pressure. At the same time, to ensure that the turbine operates efficiently when the system load varies, the turbine needs to be regulated reasonably. In this paper, the influence of three nozzle governing (NG) parameters on turbine performance during the NG process is investigated: the inlet pressure of the NG region, the area of the NG region, and the base pressure. The flow characteristics and loss mechanisms under partial admission conditions and NG conditions are also analyzed. Furthermore, the NG process is optimized by using a transition section. The results show that the loss under the NG condition with low inlet pressure at the NG region is similar to the partial admission loss, both of which are mainly affected by the area of the NG region. In both working conditions, vortex structures appear at the stator outlet of the NG region. At the same time, the mixing of different airflows and the flow separation phenomenon within the rotor leads to a significant increase in entropy production rate. The transition section can attenuate the leakage and reduce the entropy production rate in the radial gap between the stator and rotor, increasing turbine efficiency by up to 1.1 % at maximum when using the transition section.

Ground test modeling of cylindrical aero-engine casing under high-temperature and high-pressure aerodynamic load

The aero-engine casing is subjected to high-temperature and high-pressure aerodynamic loads during operation. It is essential to perform the ground strength tests for the casing. To optimize the ground strength test and to improve the accuracy of temperature and pressure loading during the test, a mathematical model of high-temperature and high-pressure aerodynamic loading system for simulating the ground strength test of typical aero-engine casing is established to study the actual temperature and pressure loads on casing during ground test. The temperature and pressure results calculated by the mathematical model under typical loads matched well with the experimental measurements. The verification results show that under the test conditions, the thermal inertia of the heater, the radiation heating of the gas in the test chamber by the heater, and the heat transfer of gas mixing in the pressure process can be ignored. The sensitivity analysis of the parameters affecting the accuracy of pressure and temperature loading is carried out. We show that the pressure can be controlled quantitatively by adjusting the inlet and exhaust pipe parameters, and accurate temperature control can be achieved by designing reasonable heater power and selecting appropriate heating control parameters.

Experimental and numerical investigation on rock fracturing and fragmentation under coupled static pressure and blasting

The blasting technique is widely adopted to break rock in civil and mining engineering, and its operation is generally subjected to static pressure due to tectonic and gravitational stresses. In the present study, the rock fracturing and fragmentation under coupled static pressure (uniaxial pressure and confining pressure) and blasting are experimentally and numerically investigated. First, 11 blast tests with 100-mm cubic red sandstone samples are carried out based on a biaxial loading system. Then, the blast-produced rock fragmentation in blast testing is numerically modeled using finite element method with LS-DYNA, and the explosion pressure attenuation and fracture evolution in rock samples are numerically reproduced. The simulated rock fragmentation data are obtained by image processing in ImageJ, and the fragment size distribution of red sandstone under combined static stress and blasting is characterized using Weibull distribution. Accordingly, the effects of uniaxial pressure and confining pressure on blast-created rock fragmentation are quantitatively compared and analyzed, and the corresponding mechanisms are discussed and revealed. The current findings indicate that the static pressure plays a role in increasing blast-induced compressive stress and reducing tensile stress in both radial and hoop directions and thus results in a decrease in the length and number of fractures propagating vertically to the direction of applied pressure, further leading to the creation of coarser fragmentation. Blasting under confining pressure produces larger fragments than that under uniaxial pressure. The average fragment size increases quickly at first and then increases slowly with the increase of static stress, which can be well characterized using a logarithm equation. Based on current findings, increasing the free surface is crucial in practical blasting to improve rock fragmentation performance under blasting.

Parameter adaptive stochastic model predictive control for wind–solar–hydrogen coupled power system

With the increasing global energy scarcity and environmental concerns, the wind–solar–hydrogen (WSH) coupled system has garnered widespread attention as an efficient and eco-friendly renewable energy solution. Addressing the impact of uncertainty on the source and load of the coupled system concerning its power balance, a parameter adaptive stochastic model predictive control (PAS-MPC) power regulation strategy based on the scenario analysis method is proposed herein. In order to characterize the probabilistic information about the uncertainty on both sides of the system, scenario generation and reduction techniques are used to obtain classical scenarios of wind and solar outputs as well as electrical loads as inputs to PAS-MPC. With the aim of optimizing the computational time constant of SMPC, the parameter adaptive method is proposed to change the prediction time domain and control time domain of the system. Simulation results demonstrate the effectiveness of PAS-MPC in addressing uncertainties on the source and load sides of the WSH coupled system. Optimization results reveal that PAS-MPC considerably improves the system power balance compared to SMPC. Compared to the conventional model predictive control (MPC), PAS-MPC effectively mitigates high-power state of the controllable equipment of the system, thereby enhancing its lifecycle.

OPTIMIZING PROCESSOR WORKLOADS AND SYSTEM EFFICIENCY THROUGH GAME-THEORETIC MODELS IN DISTRIBUTED SYSTEMS

The primary goal of this research is to examine how different strategic behaviors adopted by processors affect the workload management and overall efficiency of the system. Specifically, the study focuses on the attainment of a pure strategy Nash Equilibrium and explores its implications on system performance. In this context, Nash Equilibrium is considered as a state where no player has anything to gain by changing only their own strategy unilaterally, suggesting a stable, yet not necessarily optimal, configuration under strategic interactions. The paper rigorously develops a formal mathematical model and employs extensive simulations to validate the theoretical findings, thus ensuring the reliability of the proposed model. Additionally, adaptive algorithms for dynamic task allocation are proposed, aimed at enhancing system flexibility and efficiency in real-time processing environments. Key results from this study highlight that while Nash Equilibrium fosters stability within the system, the adoption of optimal cooperative strategies significantly improves operational efficiency and minimizes transaction costs. These findings are illustrated through detailed 3D plots and tabulated results, which provide a detailed examination of how strategic decisions influence system performance under varying conditions, such as fluctuating system loads and migration costs. The analysis also examines the balance between individual processor job satisfaction and overall system performance, highlighting the effect of rigid task reallocation frameworks. Through this study, the paper not only improves our understanding of strategic interactions within computational systems but also provides key ideas that could guide the development of more efficient computational frameworks for various applications.

Application of artificial neural network for peak load forecasting in 150 kV Semarang power system

Accurate load forecasting is essential for reliable and efficient operation of power systems. Traditional forecasting methods often struggle with capturing complex nonlinear patterns in load data. Artificial neural networks (ANNs) have emerged as a promising alternative due to their ability to learn complex relationships from historical data (Syed et al. in IEEEA 9:54992–55008, 2021. https://doi.org/10.1109/ACCESS.2021.3071654). This study investigates the potential of ANNs for short-term peak load forecasting in a 150 kV power system in Semarang, Indonesia. The study examines the impact of different input variables, including historical peak load, minimum load, population, and energy production, on forecasting accuracy. Several ANN architectures are trained and evaluated using mean absolute percentage error (MAPE) and mean squared error (MSE) metrics as reported by Demuth and De Jesús (neural network design). The results indicate that ANNs can achieve high accuracy in predicting peak load, with MAPE values below 10%. The study also demonstrates the importance of carefully selecting input variables and training parameters for optimal model performance. The findings highlight the potential of ANNs for improving load forecasting accuracy in power systems, contributing to enhanced grid reliability and operational efficiency. The findings of this study contribute to a deeper understanding of the application of ANNs in power system load forecasting. They demonstrate the potential of ANNs to achieve high accuracy and provide valuable insights into the factors influencing model performance. The findings are relevant for power system operators, researchers, and policymakers working to improve grid reliability and efficiency as reported by Prabha Kundur and Malik (Power System Stability and Control, McGraw-Hill Education, New York, 2022. https://www.accessengineeringlibrary.com/content/book/9781260473544).

Synergetic UPQC Application for Power Quality Enhancement in Microgrid Distribution System: SCSO Approach

Nowadays, Hybrid renewable energy systems (HRES) are increasingly being integrated into grid-connected load systems to lower losses and boost dependability. When the HRES system is connected to the grid system to satisfy needed load demand, power quality issues arise because of imbalanced load circumstances, non-linear load, and critical load. So this manuscript proposed a synergetic Unified Power Quality Conditioner (UPQC) application for power quality enhancement in microgrid distribution systems. The Sand Cat Swarm Optimization (SCSO) technique is based on the proposed sand cat swarm optimization method. The proposed strategy's primary goal is to regulate power flow, minimize harmonic distortion in both voltage and current waveforms, and maximize UPQC. By then, the proposed method's performance has been implemented into practice on the MATLAB platform and contrasted with several currently used techniques. The proposed technique displays the low Total Harmonic Distortion (THD) and operation time in all existing approaches like Moth Flame Optimization (MFO), Genetic Algorithm (GA), Salp Swarm Algorithm (SSA), Ant Lion Optimizer (ALO), Improved Bat Search Algorithm (IBSA), Lion Algorithm (LA), Artificial Bee Colony (ABC), Atom Search Optimization (ASO) and Crow Search Algorithm (CSA). The proposed method attains THD as before UPQC is 31%, after UPQC is 3%, load current is 1.42%, and load voltage is 2%. It also achieves a low operation time of 506ms and a low settling time of 0.15s compared to the existing methods. The significant improvement in power quality metrics demonstrates the effectiveness of the proposed SCSO technique.

Weakening characteristics of pile group foundations in clayey soil under vertical cyclic loading: Experimental and numerical investigation

Long-term cyclic loading can substantially reduce the bearing capacity of pile foundations, potentially compromising the structural integrity of offshore foundations. A small-scale model test system for vertical cyclic loading of pile foundations is independently designed. Model experiments and numerical simulations were conducted to investigate the weakening characteristics of four foundation types (i.e., slab foundation, single pile with cap, 2 × 2 pile group foundation, and 3 × 3 pile group foundation) in clayey soil under cyclic loading. The results of both methods demonstrate that 3 × 3 pile group foundations exhibit the greatest resistance to deformation, followed by 2 × 2 pile groups, single piles with a cap, and slab foundations under cyclic loading. Furthermore, a 3D numerical model was developed to simulate the real-world behavior of a pile group foundation for an offshore wind turbine. Results reveal that although the ultimate compressive bearing capacity decreases with higher cyclic loading levels, typical cyclic loads (0.1–0.2) encountered in practice do not greatly affect the long-term bearing capacity of the foundations. The proposed experimental and numerical simulation methods offer valuable insights into the weakening characteristics of pile group foundations under cyclic loading.

Endogenous ethanol- and thermo-tolerant strain from Malaysia pineapple peel: Screening and molecular characterisation

The current study aims to identify an endogenous acetic acid bacterial strain isolated from a Malaysian pineapple capable of tolerating high ethanol and temperatures. A total of 23 strains of presumed acetic acid bacteria were isolated from the pineapple peel waste. Subsequently, three isolates with the highest acid yield were subjected to 16S rDNA sequencing; Acetobacter tropicalis (A. tropicalis), Staphylococcus haemolyticus, and Staphylococcus ureilyticus. Tolerance evaluations against pH, temperature, ethanol, sugar, and acetic acid demonstrated that all three strains tolerated significant temperatures, ethanol, and sugar levels. Based on the results, A. tropicalis possesses the potential to reduce cooling system load during acetic acid manufacture in hot climate regions with a notable acetic acid yield.

Vulnerability-based seismic resilience and post-earthquake recovery assessment for substation systems

The function of substation systems is significantly influenced by earthquakes. To assess the seismic resistance of substation systems and improve post-earthquake recovery efficiency, this study constructs a multi-module framework for seismic vulnerability assessment of substations. Based on the equipment functional status analysis module, a functional network model was established considering the structural function and electrical load of substation systems. The system functional state matrix was obtained through Monte Carlo simulation, and the seismic vulnerability was assessed from the aspects of electrical transmission reliability and the importance of power users. Subsequently, a substation functional index was established based on seismic vulnerability parameters. Post-earthquake functional recovery strategy analysis frameworks were constructed to quantitatively evaluate the recovery process. A stepped functional time-varying function was proposed through iterative analysis, thereby converting the probability parameters of functional status into seismic resilience deterministic indices. Through seismic resilience analysis of a typical 220 kV step-down substation, seismic vulnerability curves for both electrical transmission and power users were obtained. Furthermore, the seismic sensitive intervals of the substation and key equipment for post-earthquake recovery were clarified. Notably, the seismic resilience level and optimal recovery strategy of the typical 220 kV step-down substation were determined.

Integrated Energy System Load Forecasting with Spatially Transferable Loads

Integrated Energy System Load Forecasting with Spatially Transferable Loads

MANAGEMENT OF SCALABILITY IN CLOUD-BASED APPLICATIONS

The following is an abstract of the article. The article presents an analysis of the challenges associated with monitoring and managing the scalability of a cloud application. To this end, a module for monitoring and managing the scalability of a cloud application has been developed as part of this study. The development process included the introduction of automatic scaling, and monitoring using Prometheus and Grafana, which allows for a high level of availability and resource efficiency. The study comprised a series of phases, including requirements analysis, system design, development, testing, and evaluation. Consequently, the system's performance, stability, and capacity to scale in response to fluctuating workloads were enhanced. The module exhibits a high degree of adaptability to changes in system requirements and load, which is a crucial attribute for the dynamic development of business applications. This solution assists in optimizing the allocation of resources and reducing infrastructure costs. The project has been found to fully meet the set goals and objectives, as well as the requirements for effective resource management of the Amazon Web Services cloud platform using Terraform, Prometheus, and Grafana. The practical value of the developed module is evidenced by a significant improvement in resource efficiency, service stability and cost optimisation. The module design has been subjected to rigorous testing and has been successfully implemented in a test environment, thereby demonstrating the sustainability and efficiency of the developed solution. The experience gained in the implementation and operation of this solution may prove useful for further expansion and optimization of cloud solutions in other projects and companies specializing in the provision of cloud solutions. The findings of this study were validated in a test environment at an IT company with a specialization in cloud technologies. The objective was to ascertain the functionality and efficiency of the developed module in a real-world context of cloud infrastructure operation. The testing process entailed the configuration of the module on pre-existing cloud infrastructure systems, its integration with Prometheus and Grafana for monitoring purposes, and the execution of a series of stress tests designed to assess the module's scalability. As a result of this testing, a number of critical points were identified that required further optimization. The results of the study and the issues identified during the project testing have enabled the identification of several areas for further improvement and development of the system. First and foremost, the optimization of automatic scaling algorithms represents a crucial avenue for improvement. The development of these algorithms should be oriented towards utilizing historical monitoring data to anticipate potential shifts in system load. Another pivotal area for enhancement is the precision of monitoring systems. The integration of supplementary tools and the expansion of existing monitoring systems' functionality will facilitate the acquisition of more comprehensive insights into the system's condition. This, in turn, will facilitate the expedient identification and eradication of potential issues.

Динамическая модель харвестера, оснащенного канатной анкерной системой, при проведении лесозаготовительных работ на крутом склоне

The efficiency of using logging machines for work in mountainous or hilly areas characterized by the presence of steep slopes depends on many factors, among which the quantitative parameters of the dynamic state of both the harvester itself and the anchor system play a priority role to ensure the safe operation of these technological machines. This article considers a complex spatial dynamic model that combines into a common interconnected system all the main components of the simulated phenomenon, including directly a harvester with a hydraulic manipulator mounted on it, a movable assortment in space, an anchor system consist-ing of an anchor rope and a winch, an anchor tree for attaching an anchor rope and the soil of the support surface. The dynamic mathematical model of such a six-component system includes 35 generalized coordinates that determine the linear and angular displacements of the mass centers of the listed system components. A system of 35 differential equations constructed using the Lagrange’s equation was solved by the 4-th-order Runge-Kutta method. For this purpose, a computer program has been developed, the description of which is contained in the article. In relation to the basic version of the simulated system with quantitative initial characteristics focused on the characteristics of a three-axle wheeled harvester with a 6K6 wheel formula, calculations of oscillatory processes for dynamic parameters determining the loading of the anchor system and the safety of harvester operation in the process of assortment manipulation were carried out.

Flexible operation of nuclear hybrid energy systems for load following and water desalination

Nuclear hybrid energy systems (NHES) have the potential to provide dependable and emission-free electricity to the grid while also increasing the flexibility and reliability of the electrical grid. Molten salt reactor (MSR) technology can provide consistent, carbon-free electricity while also increasing efficiency, security, and sustainability and reducing nuclear waste. This study investigates the integration of Molten Salt Reactors (MSR) and conventional Pressurized Water Reactors (PWR) with desalination technologies: Direct Contact Membrane Distillation (DCMD), Multi-Stage Flash Distillation (MSFD), and Reverse Osmosis (RO). Dynamic first-principles models were developed and tested using real grid data from the New York Independent System Operator. The results demonstrate that nuclear power is capable of flexibly responding to changing grid demand while simultaneously producing clean water, particularly during periods of low electricity demand. The MSR-RO system was found to be the most efficient in electricity generation and water production, and all hybrid systems reduced CO2 emissions by 356,000 to 682,000 tons annually. Economic analysis reveals that nuclear desalination technologies are cost-competitive with conventional systems, especially when paired with RO. These findings confirm the technical feasibility and environmental benefits of nuclear hybrid systems for sustainable electricity and water production.

Hydrogel-based formulations for urothelial cancer therapy.

Drug infusion therapy after surgery for urothelial carcinoma is an effective measure to reduce cancer recurrence rate. Hydrogels are drug carriers with good biocompatibility and high drug loading capacity, which can optimize the pharmacokinetics of drugs in the urinary system to improve the therapeutic effect. Compared with the traditional free drug in situ perfusion, the hydrogel drug loading system can still maintain effective drug concentration in the face of continuous urinary flushing due to its good mucosal adhesion effect. The significantly prolonged drug retention time can not only improve the therapeutic effect of drugs, but also reduce the discomfort and risk of urinary tract infections caused by frequent drug infusion, and improve patient compliance. In addition, the combination of hydrogel with nanoparticles and magnetic materials can also improve the mucosal permeability and targeting effect of the hydrogel drug loading system, so as to overcome the mucus layer of urinary epithelium and the physiological barrier of tumor and minimize the impact on normal tissue and cell functions. At present, the research of hydrogels for urothelial cancer treatment involves chemotherapy, immunotherapy, gene therapy, inhibition of metabolism and multi strategy synergistic therapy. This review summarizes the research progress of hydrogels for the treatment of urothelial carcinoma, hoping to provide a reference for the future research of safe, reliable, effective, and advanced hydrogels with little side effects.

Engineering Microgrids Amid the Evolving Electrical Distribution System

Non-wires alternatives and microgrid technologies are maturing and present great opportunities for electric utilities to increase the benefits they offer to their customers. They have the potential to decrease the cost of resolving traditional electrical system loading issues, contribute to carbon emissions reductions, and improve the electrical distribution system’s resilience to extreme weather events. The authors of this manuscript present a review of the research on microgrids and their practical applications. This is leveraged with the past work of the authors of this manuscript and other authors to develop specific objectives for microgrids, practical criteria for engineers to consider when deploying microgrids, stochastic methods to optimize microgrid designs, and black start requirements. This guidance is then used for the design of actual networked microgrids being deployed with adaptive boundaries.