System Load Research Articles

Many-objective optimization based mutual feed scheduling for energy system of integrated energy station

The battery storage system of the integrated energy station contains electric vehicles as the distributed energy storage node of the regional power system can help the regional power system to achieve peak shaving and valley filling. This paper proposes a many-objective optimization based mutual feed model of the energy system of the integrated energy station. The model includes multiple modules of electric vehicle (EV), wind turbine (WT), photovoltaic (PV) and battery energy storage system (BESS). BESS was modeled according to the two behaviors of EVs charging and swapping. The model ensures the role of peak shaving and valley filling for regional power, reduces the loss of battery life in the battery storage system of the integrated energy station, and reduces the operating cost of the battery storage system. In the optimized results, the regional power system load peak-to-valley ratio by 28.72 %, and the daily battery charge/discharge life loss is minimized to 0.00036 %. In addition, for known many-objective optimization problems, this paper also proposes a new meta-heuristic many-objective optimization algorithm the non-dominated sorting hunter prey optimization (NSHPO for short) and compared with other four many-objective optimization algorithms by three indexes (Distribution range, Hypervolume index, and Running time). The simulation results show that compared with other algorithms, the HV metrics of NSHPO are lower by about 1.56 %-7.26 %, Spread metrics are better by about 0.39 %-2.73 %, and the proposed NSHPO algorithm is better in terms of convergence and distributivity.

Co-optimization of power and spinning reserve in multi-area electrical networks with wind farms

Integrating wind farms into electrical networks reconfigures energy matrices and poses new challenges to system operators. For instance, wind farm owners must now guarantee spinning reserves stipulated in grid codes. This prompts an urgent quest for optimizing power and reserve allocation in liberalized markets using advanced models. In this sense, this paper introduces an optimal power flow (OPF) model to co-optimize power and spinning reserve in multi-area electrical networks with wind farms. The granularity and layout of the wind plant are considered for a correct calculation of the spinning reserve in each variable speed wind turbine. Unlike classical OPF studies, where generator participation is free and mandatory in system reserves, this new approach considers both power and reserve as variable factors in the optimization process, which depend on reserve costs, system load, and wind speed. The approach is evaluated through comparative analyzes on two power networks: a small 12-bus, two-area network with two wind farms and a larger 100-bus, four-area network with four wind farms. Multi-period 12-hour look-ahead analyzes are conducted with variable conditions of demand and wind speed. The studies demonstrate the advantages of the new model compared to the classical OPF, showcasing its ability to optimize power dispatch with wind farms actively contributing to the spinning reserve requirements of the power network.

Breaking bad news in oncology practice: experience and challenges of oncology health professionals in Ethiopia – an exploratory qualitative study

ObjectiveTo explore the experience and challenges health professionals face during breaking bad news (BBN) to patients with cancer in the oncology centre of Black Lion Specialized Hospital (BLSH), Addis Ababa,...

Time-frequency analysis of plate-shell coupled structures under moving stochastic load

Random vibration is of critical importance in understanding the dynamic behavior of engineering structures, especially the nonstationary responses for their coupled enclosers consisting of plates and shells subjected to moving stochastic loads. In this study, the pseudo excitation method (PEM) is integrated into the method of reverberation ray matrix (MRRM) to establish a full-domain stochastic dynamic system. With the help of rearrangement matrices and coupling spring connection technologies, the two plate-shell models of sequential and reinforced coupling are considered by employing the simple first-order shear deformation shell theory (S-FSDST) and Hamilton's principle. Then, the continuous movement of the excitation between each substructure is achieved through load coordinate normalization in nonhomogeneous equations, which are separated from the generalized solutions in the frequency domain. Therefore, it can be easily converted into a global pseudo-excitation and obtain the time-history responses relying on the inverse Laplace transform. After matching the present results with the finite element software and Monte Carlo simulation (MCS), the accuracy and efficiency of the proposed RRM-PEM are well-confirmed. Finally, by performing a series of numerical cases of power spectral density (PSD) and time-varying root mean square (RMS) under different loading velocities, frequency bands and system modal dampings, the time-frequency rules for different substructures of closed plate-shell coupled system under moving random loads are revealed for the first time.

Meta‐heuristic optimization techniques for scheduling in multiple‐input multiple‐output multiple‐access channel system

SummaryIn a highly dense multiple‐input multiple‐output (MIMO) communication system, proper resource allocation is exceedingly challenging. The simultaneous selection of antennas at the base‐station (BS) and scheduling of users in an uplink multiple access channel (MAC) system add up to the signal processing load in the MIMO wireless system. The method chosen for combining the user scheduling and antenna selection must have a very low computational complexity. However, the exhaustive search algorithm (ESA), which achieves the best sum‐rate capacity in a MIMO‐MAC system, has a very high level of complexity. In this paper, we use a metaheuristic‐based optimization algorithms to optimize the sum‐rate capacity in a MAC system with significantly less computational complexity. First, we use the beluga whale optimization (BWO) metaheuristic method to solve the optimization problem. The BWO is a swarm‐based algorithm that draws inspiration from beluga whale behavior. An improved BWO (iBWO) is also presented and is utilized for the same optimization challenge. The proposed iBWO scheme improves the search capability of the algorithm and thereby ensures better exploration and exploitation capabilities of the beluga whales. We compare the performance of these algorithms with a standard differential evolution (DE) algorithm. The simulation results demonstrate that the proposed iBWO is a very competitive meta‐heuristic algorithm for solving optimization problems. The proposed iBWO algorithm achieves near optimal throughput in a MIMO‐MAC system. Also, the computational complexity related to the suggested optimization technique in a MIMO‐MAC system is found to be very less compared with the complexity related to ESA.

Air-conditioning load characteristics and grey box predicting model in subway stations

With the continuous expansion of metro, energy conservation of air-conditioning system in subway stations draws the attention of the whole world. Most of the researches are focused on improving the energy efficiency of the system, while the load characteristics of subway stations are paid little attention. The inaccurate load prediction in the designing of air-conditioning system results in redundancy and low operating efficiency. Therefore, accurately predicting the air-conditioning load is crucial for energy saving in subway stations. In this paper, an innovative three-stage subway air-conditioning load test method was proposed and implemented in nine subway stations in Qingdao. Based on the test data, the special air-conditioning load characteristics of subway stations as underground buildings were analyzed. The results showed that electrical equipment load accounted for the largest part, reaching 53.7 %, while only 42 % of the equipment power was converted into air-conditioning load. The positive role of the envelope should not be ignored, especially in the case of entrance and exit walls, which bore almost all of the fresh air load. Based on the three-stage test, a grey box model was established and the error was validated to be within 6 %. With this model, the load of air-conditioning system can be calculated by inputting passenger flow, power of electrical equipment and public area. This paper provides a reference for the design and operation of air-conditioning system in subway stations.

Design and development of grid independent integrated energy system for electric vehicle charging stations at different locations in Malaysia

Electric vehicles (EVs) have revolutionized the transportation sector as an alternative to conventional fossil fuel-based transportation. It becomes more effective when the charging energy comes from green, renewable, cost-effective, eco-friendly resources. The present work discusses modelling a hybrid renewable energy system for EV charging stations in Malaysia. This work presents techno-economic investigation for different hybrid energy system arrangements of solar photovoltaic (PV), wind turbine (WT), natural gas generator (GS) and battery energy storage (BES) for EV charging station. The EV load demand is calculated based on the transportation fleet of an institution considered for five different climatic locations in Malaysia using HOMER-Pro software. Considering the technical sizing of the hybrid system, the minimization of the energy system and net present cost are discussed for each locations. The optimization study of hybrid energy systems is further examined for social benefits and environmental analysis by including the human development and employment creation index. The comparative analysis of hybrid energy storage systems is performed separately. This study uses sensitivity analysis to elaborate on the cost-effectiveness and technical feasibility of the hybrid system, considering the cost of system components, macroeconomics parameters, and load variation. The finalized results indicate that the solar PV-BES-GS12 hybrid energy system is the most suitable combination for the integrated grid-independent EV charging station operation at each locations.

Analysis of partial load loss of the PCS and internal storage battery loss in residential PV power generation and storage battery systems

AbstractThis study aims to quantify the amount of loss due to partial load of power conditioning system (PCS) and internal loss of storage battery in residential photovoltaic (PV) power generation and storage battery system by using home energy management system (HEMS) data. After identifying the configuration of the PV power generation and storage battery system, HEMS data on PV power generation, household demand, storage battery charging, storage battery discharging, purchased power and sold power were extracted and analyzed according to the research purpose. As a result, the loss due to partial load of the PCS and the average internal loss of the storage battery were quantified. In addition, it was confirmed that a more realistic system simulation could be performed by considering the impact of the partial load of the PCS and the average internal loss of the storage battery.

Linear and nonlinear axial behaviour of internal replacement pipe systems for pipeline rehabilitation

Internal replacement pipe (IRP) is an innovative trenchless technology for the rehabilitation of legacy steel and cast-iron pipelines. This advanced IRP system must be properly designed so that it can safely and effectively restore the service of damaged pipelines. This paper studies the axial behaviour of IRP systems for repairing pipelines with circumferential host-pipe discontinuities under seasonal and extreme levels of temperature change. Analytical solutions along with a total of 270 linear and nonlinear finite element (FE) simulations, validated against experimental results and available closed-form solutions, were used for a parametric study on the effects of geometrical and material properties on axial stresses and deformations of IRP systems subjected to temperature change. Effects of the internal pressure, material and geometric nonlinearities, and different modes of IRP-host pipe unbonding were also investigated. An analytical model was developed for the prediction of temperature change induced loading and response of the IRP system. Using the results obtained from a comprehensive FE-based parametric study, three modification factors were extracted for the application to the developed analytical model in order to accurately predict the maximum axial stress of IRP and the opening of a circumferential host-pipe discontinuity subjected to various levels of temperature change.

An Adaptive Virtual Inertial Control Strategy for DC Distribution Networks

The DC distribution network is a low-inertia system, which is very sensitive to load disturbance, system failure, and other factors. By adding virtual capacitance to the converter connected to the power supply, the virtual inertia of the DC power grid can be improved. Firstly, this paper proposes a strategy for adjusting virtual capacitance based on the voltage change rate to achieve adaptive control of virtual inertia, which enables the converter to quickly absorb or release energy during power fluctuations. Secondly, the adaptivity of the strategy is improved and the main control parameters in the proposed control method are qualitatively analyzed. Finally, the four-terminal photovoltaic storage DC distribution network system is constructed. Through Simulink, the adaptive virtual inertia control is incorporated on the battery side to simulate and validate the effectiveness of the strategy and the rationality of parameter analysis. The results show that this method can provide flexible and adjustable inertia support for DC grids and improve the voltage stability of DC grids.

Analysis and calculation of the dynamic voltage reserve of the converter when working under load in systems of subject regulation by electric drives of direct current

Analysis and calculation of the dynamic voltage reserve of the converter when working under load in systems of subject regulation by electric drives of direct current

Optimum Plant Density Improved Cotton (Gossypium hirsutum L.) Root Production Capacity and Photosynthesis for High Cotton Yield under Plastic Film Mulching

Optimum Plant Density Improved Cotton (Gossypium hirsutum L.) Root Production Capacity and Photosynthesis for High Cotton Yield under Plastic Film Mulching

Design and Validation of a Stratified Shear Model Box for Seismic Response of a Sand-Blowing Reclamation Site

The global increase in building collapses and damage on soft-soil sites due to distant significant earthquakes poses similar challenges for sand-blowing reclamation (SBR) sites on soft-soil layers. This study was initiated to capture the vibration characteristics of the SBR sites and to provide fresh insights into their seismic responses. Initially, considering the heterogeneity and layered structure of soil at SBR sites, we developed a novel stratified shearing model box. This model box enables the simulation of the complex characteristics of soil layers at SBR sites under laboratory conditions, representing a significant innovation in this field. Subsequently, an innovative jack loading system was developed to apply active vertical pressure on the soil layer model, accelerating soil consolidation. Furthermore, a new data collection and analysis system was devised to monitor and record acceleration, pore water pressure, and displacement in real time during the experiments. To verify the model box’s accuracy and innovation, and to examine the seismic response of SBR sites under varying consolidation pressures, four vibration tests were conducted across different pressure gradients to analyze the model’s predominant period evolution due to consolidation pressures. The experimental results demonstrate that the model box accurately simulates the propagation of one-dimensional shear waves in soil layers under various consolidation pressures, with notable repeatability and reliability. Our experiments demonstrated that increasing consolidation pressure results in higher shear wave speeds in both sand and soft-soil layers, and shifts the site’s predominant period towards shorter durations. Concurrently, we established the relationship between the site’s predominant period and the input waves. This study opens new paths for further research into the dynamic response properties of SBR sites under diverse conditions through shaking-table tests.

Seismic Performance Evaluation of Traditional Chinese Mortise-Tenon Frame Infilled with Friction Dissipative Window Frame

ABSTRACT This paper presents a new form of window frame that employs a friction dissipative connection. The design is informed by a structural analysis of the sill window in traditional wooden structures and inspired by friction dissipative mechanisms. By considering the frictional wear of the friction block, a more accurate formula for calculating the frictional torque is derived and its accuracy is verified. Additionally, a finite element model of the sill window-wooden frame system is established and verified using experimental data. Then a further finite element model of the sill window-wooden frame system with the friction dissipative window frame is established and analyzed, and the effectiveness of the reinforcement scheme of the friction dissipative connection is verified. The analysis shows that compared to the sill window-wooden frame system, the load-carrying capacity and energy dissipation of the sill window-wooden frame reinforced with the friction dissipative window frame are significantly improved. When the bolt pre-tightening force is 100 kN, the peak load and stiffness of the sill window-wooden frame system reinforced with the friction energy dissipation window frame are increased by 2.7 and 2.57 times, respectively, compared to the sill window-wooden frame, which can effectively prevent the seismic damage of ancient wooden structures.

Reengineering of an Artificial Protein Cage for Efficient Packaging of Active Enzymes.

Protein cages that readily encapsulate active enzymes of interest present useful nanotools for delivery and catalysis, wherein those with programmable disassembly characteristics serve as particularly attractive platforms. Here, a general guest packaging system based on an artificial protein cage, TRAP-cage, the disassembly of which can be induced by the addition of reducing agents, is established. In this system, TRAP-cage with SpyCatcher moieties in the lumen is prepared using genetic modification of the protein building block and assembled into a cage structure with either monovalent gold ions or molecular crosslinkers. The resulting protein cage can efficiently capture guest proteins equipped with a SpyTag by simply mixing them in an aqueous solution. This post-assembly loading system, which circumvents the exposure of guests to thiol-reactive crosslinkers, enables the packaging of enzymes possessing a catalytic cysteine or a metal cofactor while retaining their catalytic activity.

An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems

Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications.

Developing preliminary cost estimates for foundation systems of high-rise buildings

This study provided a unique Artificial intelligence (AI) model for the cost estimation of the foundation system for high-rise buildings. First, parametric data research was conducted, including 180 examples of foundation systems for high-rise building models related to criteria such as average column spacing, floor height, slap load, overall weight, base shear, and over. In addition, moment, gravity load, seismic load, design load, various columns (mid-edge-corner), soil profile, bearing capacity, GWT, excavation depth, and foundation systems are all factors to consider (shallow and deep footing). The research encompassed column spacing (5.0, 7.0, and 9.0 m) and the number of stories from 15 to 35, with an average cost decrease of 15%. The AI model utilizes extensive machine learning algorithms to analyze structural and geotechnical parameters. It provides a predictive tool that improves decision-making in the preliminary design. The methodology includes collecting data, training models utilizing innovative neural network techniques and validating them against current cost estimation models. The findings indicate that the AI model significantly improves the accuracy of cost estimations, with a precision rate of 95%, compared to traditional methods. Moreover, the model presents a potential cost savings of up to 15%. Construction designers and decision-makers may utilize the study’s findings to increase the accuracy of foundation system selection, lower costs and construction length, and improve overall project performance. Future work may involve applying this concept to infrastructure and substructure projects and collecting more data for benchmarking and empirical study.

Integrated drug delivery system loading ROS-generating antitumor drugs for signal-amplified photoacoustic imaging and multimodal tumor therapy

The usefulness of antitumor drugs that produce reactive oxygen species (ROS) in accurate tumor diagnosis and treatment has been underestimated. Here, β-Lapachone (Lapa, antitumor drugs), 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS, precursor photoacoustic agents) and 2, 2′-azobis[2-(2-imidazolin-2-yl) propane] dihydrochloride (AIPH, free radical initiators) were successfully loaded into zeolitic imidazolate framework (ZIF) in situ by a one-pot method and named Lapa/ABTS/AIPH@ZIF-67 nanoparticles (LBIZ NPs). After entering the tumor cell, the ZIF structure of LBIZ NPs is disrupted in the acidic tumor microenvironment, which results in the release of Co2+ and drugs. In one hand, Lapa and Co2+ together increased hydroxyl radicals (•OH) levels to achieve Enhanced-Chemodynamic therapy (ECDT). As the amount of •OH went up, the amount of ABTS•+, the oxidation product of ABTS, would also increase, thus increasing the sensitivity and contrast of photoacoustic imaging. In the other hand, the photothermal effect produced by ABTS•+ induces AIPH to crack and release alkyl radicals (•R) to achieve thermodynamic therapy. The LBIZ NPs achieved more effective tumor treatments by fully utilizing light and heat energy. Hence, this integrated drug delivery system is able to achieve signal-amplified photoacoustic imaging and Enhanced-Chemodynamic/Photothermal/Thermodynamic trimodal combination therapy. This work provides a new insight into the application of ROS-generating antitumor drugs to precise imaging of tumors.

Aviation Operators’ Total Loads Analysis by Multi-Criteria Decision-Making

The aviation operator's role and duties undergo a dramatic change from direct engagement to information management and surveillance in today's advanced aviation environment. In this modern and highly automated working environment, operators must handle various information, data, and transmission during operations, particularly while making decisions in specific situations. The total load system of operators (information, communication, mental, task, and workload) has been fundamentally reshaped in response to these dynamic changes. The highly automated system is linked to unbalanced operator loads, unanticipated losses in situation awareness, inappropriate decision-making quality, and higher stress levels. This research employs the Analytical Hierarchy Process (AHP) as a foundational framework, integrating the Fuzzy Analytical Hierarchy Process (FAHP) on the AHP model to address uncertainties and applying the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) for ranking the aviation operators' total loads. online Survey distributed among 63 experts across four aviation operator groups: less experienced pilots, expert pilots, less experienced Air Traffic Controllers (ATCOs), and experienced ATCOs. serves to evaluate the pivotal characteristics of the operators' total loads model. The results highlighted the communication load's effect on the aviation operators specifically the level of the language as well the workload specifically both practicing referring to the experience and skills.

Synergistic Integration of EVs and Renewable DGs in Distribution Micro-Grids

This paper proposes a multi-objective optimization framework for safe, reliable, and economic integration of electric vehicles (EVs) and renewable distributed generators (DGs) in distribution micro-grids. EV and DG coordination optimization with the use of vehicle-to-grid (V2G) technology along with system reconfiguration optimization is developed to provide collective revenues and address integrational complications that may occur by additional system loading due to EV charging and EV-DG energy exchanges. A Genetic Algorithm (GA) optimizes the EV charging/discharging in synergies with renewable DGs to maximize benefits that can be captured by their collaborative participation in electricity market and through renewable energy arbitrage. The developed EV charging/discharging optimization is implemented in a real 134-bus distribution network and is evaluated for its potential operational implications, namely, increased system losses. A system reconfiguration is then proposed to reduce the system losses by optimizing the flow of power through switching on/off the connections within the micro-grid and/or with other distribution systems. Simulation results demonstrate the efficiency of the proposed method in not only providing collective revenues, but also in enhancing the system operation by reducing the losses of the distribution grid. The collective benefits proposed by the developed optimization and validated by the simulation results facilitate transitioning to clean and eco-friendly sources of energy for generation and transportation, which in turn leads to more sustainable development of societies and communities.