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The application of ductile rock bolts has been a crucial method for solving the problems of large deformations, energy absorption and stability control issues in deep rock masses. To study the anchoring mechanism of the key expansive structure, this paper proposes a novel type of bolt — the Ductile-Expansion bolt, and conducts research on anchoring mechanics, energy absorption characteristics, and failure modes of the bolt. In addition, this paper defines the concept of load-volume ratio of metal rock bolts and proves the Ductile-Expansion bolt is capable of better improving the unit volume bearing capacity of the bolt material. Furthermore, laboratory and field tests verify the Ductile-Expansion bolt had better anchoring effect than the traditional rebar bolt, with the expansion structure favorably enhancing the ductility and energy absorption performance of the bolt. Finally, this paper microscopically analyzes the crack propagation and distribution morphology of the bolts by establishing a 3D coupled numerical model based on FDM-DEM. Numerical results illustrate the interface at the variable diameter of the Ductile-Expansion bolt serves as the transition zone between high and low stress levels. The expansion structure can impose radial compression on the medium around the bolt, which can improve the bolt anchorage performance.

[Objective] The development of new LNG terminals in China is currently oriented in the direction of large-scale construction while the existing LNG terminals are already undergoing expansion. Consequently, these terminals are generating an increasing volume of boil-off gas(BOG), with a substantial portion as stable sources, providing a promising opportunity for the application of centrifugal BOG compressors at these LNG terminals. [Methods] In this paper, an expanded LNG terminal in China was taken as the study subject.Its total generation and stable generation of BOG were calculated. A systematic study was performed to explore the feasibility of the widespread application of centrifugal BOG compressors at LNG terminals in China, from various perspectives, including the configuration principles of centrifugal compressors and reciprocating compressors, crucial factors in their parallel operation, and the advantages of centrifugal compressors. [Results] (1) Centrifugal compressors are suitable for application at new or expanded terminals furnished with either five LNG tanks of 16×104 m3 or four LNG tanks of 22×104 m3.(2) Implementing a configuration that combines a centrifugal compressor to handle the base load of BOG generation and a reciprocating compressor to handle the peak load of BOG generation ensures steady operation at the terminals.(3) When centrifugal and reciprocating compressors are in parallel operation, it is crucial to incorporate a buffer tank at the outlet of the reciprocating compressor, in order to mitigate the impact of pressure pulsations at its outlet on the centrifugal compressor.(4) In the same parallel operation mode, it is recommended to reduce the fluctuation range of discharge pressure at the reciprocating compressor's outlet, to reduce the influence of pressure pulsation on the downstream BOG recondenser. [Conclusion] These research findings serve as theoretical guidance and technical support for the application promotion of centrifugal compressors at LNG stations in China, their parallel operation with reciprocating compressors, and relevant engineering design.

When modelling the anchorage performance of rebars with the tri-linear law, the calculation process of the load–deformation relation is complicated. The reason is that when the rebar–grout interface entered the elastic–softening–debonding stage, the softening section length and debonding section length vary simultaneously. To solve this issue, this paper proposes an enhanced numerical calculation method. When the rebar–grout interface entered the elastic–softening–debonding stage, the softening section length was fixed to a specific value. One loop function was created to calculate the debonding section length. With this method, the number of iteration calculations significantly decreased. The credibility of this calculation method was confirmed with experimental results. Two case studies were conducted to compare the load–deformation relation obtained with the original calculation method and enhanced calculation method. The results showed that good consistency existed between the results obtained by those two methods. This finding can significantly improve the calculation efficiency when studying the anchorage performance of rebars. Moreover, this paper provides new insight for users to optimise the modelling process of rebars.

In this research, the behavior of RC columns reinforced with recycled polyethylene terephthalate (PET) fibers and steel fibers (SFs) was experimentally investigated. The experimental work included testing of 8 columns with the dimensions of 150 × 150 × 1000 mm subjected to the axial loading up to failure. Three volume fractions (1%, 2%, and 3%) were considered for both PET fibers and SFs. The axial/lateral displacements of the columns and the transverse/vertical strains versus the loads of the bars were recorded. The peak load, yield load, failure mode, ductility, and stiffness of the columns were studied in detail. The effects of plastic fibers (PFs) and SFs on the concrete characteristics were experimentally examined. Using 2% SFs in the mix increased the compressive strength, tensile strength, and toughness of concrete by 12.7%, 87.6%, and 304.8%, respectively. Furthermore, enhancement rates of the ultimate load capacity, stiffness, and ductility of the columns with 2% SFs were 15.6%, 72.6%, and 34.29%, respectively. The ultimate load capacity, initial stiffness, and ductility of the columns reinforced with 1% PF fiber were 9.43%, 62.6%, and 19.4%, respectively, greater than those of the columns without fibers. The columns’ capacity was decreased with increasing SFs and PFs over 2%. An equation from ACI was used to predict the columns’ capacity and the results agreed well with the experimental results.

A new type of spherical node was used to design a laboratory-scale prototype of a six-leg lattice of steel tubes and concrete for application as a wind turbine tower. Repeated load tests were performed on the prototype tower for several weeks to evaluate its load-carrying capacity, deformation, energy consumption, stress distribution based on damage patterns, hysteresis curves, skeleton curves, strength, and stiffness degradation curves. The findings indicated that the prototype tower underwent thread damage to the high-strength bolts of the inclined web and weld damage between the inclined web and sealing plate. Although the stress differences between different measurement points were significant, the stress values were small at most of the measurement points. The maximum equivalent stress value was 294 MPa, which appeared in the middle layer of the BC surface. The P-Δ hysteresis curve had an inverse “S”-shape, and the bearing capacity was high. The maximum energy dissipation appeared in the 1.75 Δy loading stage. The peak load of the specimen can reach 376.2 kN, and the corresponding peak displacement is 37 mm. However, the average ductility coefficient was only 2.33, indicating little plastic deformation. The maximum strain of the tower column foot is 1800 με, and the force of the inclined web member in the middle layer is the largest. The strain of the transverse web bar increased significantly after the tower yielded, which contributed to maintaining the integrity of the structure.

In this paper, deformations and energy absorption capacities of thin-walled tubes with different section geometries (circle, square, rectangle, hexagon and triangle) under quasi-static in-plane loading are investigated. The tubes have the same material properties, mass, volume, lengths and average section area and the loading conditions are similar for all of the specimens. In order to investigate the behaviour of the tubes, more than 100 tests are carried out and numerical simulations are performed. The numerical results are in good agreement with experimental data and show that the section geometry has an important effect on energy absorption capacity so that the circular and square sections have the least and the most capability of energy absorption, respectively. Furthermore, for a specific tube, the absorbed energy increases with the wall thickness. The first peak load in load-displacement curves has the greatest and the smallest values for rectangular and circular sections, respectively.

This paper investigates the flexural behavior of honeycomb sandwich panels with a toughened/untoughened interface using carbon fiber belt and short aramid fiber tissues. In the first part of the paper, bending response analysis of carbon fiber aluminum honeycomb sandwich plates with an untoughened interface is done using finite element-based first-order shear deformation theory. In the second part, experimental analysis is done to examine the effect of loading rates on the mechanical characteristics of plain and toughened sandwich panels using a three-point bending test. Four types of interface toughening are used as unidirectional and bi-directional stitches of carbon fiber belts, Kevlar short-fiber tissues, and carbon fiber belts combined with Kevlar short-fiber tissues. Experimental result shows that interface toughening improves the maximum load and energy absorbed by sandwich specimens for all loading rates. A scanning electron microscope is used to observe and analyze the failure mode and mechanism of strengthening the interface. It is observed that the stitch of the carbon fiber belt increases the adhesion contact between face sheets and core leading to prevent interfacial debonding. The peak load and energy absorptions of carbon fiber belts toughened interface sandwich are increased by 51 % and 42.5 %, respectively, compared with that of the plain sandwich by a small increment of weight (14%).

Accurate instantaneous electricity peak load prediction is crucial for efficient capacity planning and cost-effective electricity network establishment. This paper aims to enhance the accuracy of instantaneous peak load forecasting by employing models incorporating various optimization and machine learning (ML) methods. This study examines the impact of independent inputs on peak load estimation through various combinations and subsets using multilinear regression (MLR) equations. This research utilizes input data from 1980 to 2020, including import and export data, population, and gross domestic product (GDP), to forecast the instantaneous electricity peak load as the output value. The effectiveness of these techniques is evaluated based on error metrics, including mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), and R2. The comparison extends to popular optimization methods, such as particle swarm optimization (PSO), and the newest method in the field, including dandelion optimizer (DO) and gold rush optimizer (GRO). This comparison is made against conventional machine learning methods, such as support vector regression (SVR) and artificial neural network (ANN), in terms of their prediction accuracy. The findings indicate that the ANN and GRO approaches produce the least statistical errors. Furthermore, the correlation matrix indicates a robust positive linear correlation between GDP and instantaneous peak load. The proposed model demonstrates strong predictive capabilities for estimating peak load, with ANN and GRO performing exceptionally well compared to other methods.

Aerospace vehicle connection constructions are in urgent need of joint structures with excellent aerodynamic profiles and environmental adaptability. To address issues such as poor aerodynamic profile, material thermal expansion coefficient mismatch, and limited joint structure evaluation indexes, a multi-bolt, countersunk C/SiC composite joint structure is presented in this study. The development of a 3D Hashin progressive damage model and its dedicated solver code is presented. The validity of the model is confirmed by comparing simulation results with experimental data. Three evaluation indexes are proposed, peak load, weight increment efficiency, and bolt load distribution, to thoroughly evaluate the mechanical performance of multi-bolt, countersunk C/SiC composite joint structures. Using the proposed model and evaluation indices, we evaluate sixteen different designs of multi-bolt, countersunk C/SiC composite joint structures and analyze how design parameters affect their mechanical properties and damage patterns. The results show that the best mechanical properties of the joint structure are achieved when the ratio of bolt pitch to through hole diameter is 3, the ratio of bolt spacing between columns to through hole diameter is 4, the ratio of the distance between the free edge of the substrate to through hole diameter is 1.5, the ratio of through hole diameter to specimen thickness is 1.7, and the ratio of the distance between the edge of the substrate to through hole diameter is 1.5.

Several load scheduling optimization models have been proposed for smart grid systems. Nevertheless, most of the existing models assume the presence of a pricing mechanism such as time of use (TOU) and consider single objective functions that are suitable for application under specific conditions. This paper proposes a generic load scheduling optimization model that can be employed under different operating conditions and can handle different scheduling criteria. In particular, the model considers TOU and allows the integration of distributed renewable energy systems (DRES). In addition, the load scheduling model is solved using the Cuckoo optimization algorithm. Performance of the Cuckoo algorithm is validated by formulating and solving an equivalent MILP model to the load scheduling problem. A set of experiments is designed to compare optimality and time performance of the Cuckoo and its equivalent MILP model. The Cuckoo results has also shown superior or at least comparable results to the published results in the literature. A case study has been performed using real data taken from an academic building in Egypt to demonstrate the model applicability under different conditions. The results show that the building under specific conditions can achieve energy cost savings that range from 57% to 80%. The results have also provided useful managerial implications.

This paper investigates the impact of uncertain shade behavior on three facades (east, south and west) on energy performance. A previously developed stochastic shade behavior model and an occupancy model were used to conduct building co-simulation for determining energy-related performance uncertainty (annual energy demand and peak heating/cooling loads) at different building scales. Statistical test and Monte Carlo random sampling techniques were used to determine the required minimum simulation runs for obtaining reliable annual energy performance and peak loads. The results show that there is no significant difference in shading performance among seasons and orientations. In China, most newly-built office buildings are high-rise ones and thus, the influence of manual shades on the energy uncertainty of these office buildings can be neglected and a single simulation run is enough to obtain reliable annual heating, cooling and total energy demands. While for peak heating/cooling loads, the conclusion is different from annual energy demand and at least five simulation runs are required to obtain peak heating/cooling loads at building scales. For a smaller building, 120 simulation runs are suggested for peak load determination.

The properties of energy absorption can be effectively improved by different induced structures for thin-walled straight tube.Finite element software（LS-DYNA） was used to simulate the transient response of thin-walled circular tube with internal and outer staggered grooves under axial impact.Axial shock characteristics of force for thin-walled circular tubes with different grooves were compared.The mean load formula considering strain rate effect was derived based on Hosseinipour ’s deformation model.Geometric parameter was introduced to predict peak load.The results show that the thin-walled circular tube with internal and outer staggered grooves can reduce 62.24% peak load and 56.26% mean load compared with thin-walled straight tube.The fold deformation is symmetrical and consistent with Hosseinipour’s deformation model.The prediction results of mean load and peak load are consistent with the finite element results.

The increase in energy consumption and energy bills in Jordan have been escalating rapidly, which requires a special concern as a large percent of the energy is imported. The need for the reducing peak demand of the distribution network is essential to decrease the overall electricity generation cost. This study was aimed at presenting a model for a home that manages its energy consumption, and the maximum savings possible if the load shifting to off-peak times was applied. It also introduced a tariff that is more concerned in time of use rather than consumption only. The power consumption profile is collected for a sample house. The profile for a week was registered and graphed. The pricing suggested was calculated per day. Moreover, some samples applied worldwide were discussed to find a suitable model. It was found that a saving rate of 16% is achievable if the time of use charge is applied. Additionally, a peak load reduction of 3.5 kWH average per day (in the peak hours) is possible.

The high potential for implementing demand management approaches across multiple objectives has been significantly enhanced. This study proposes a cooperative energy management strategy based on the end-user sharing of energy. The proposed method promotes the intelligent charging and discharging of EVs to achieve vehicle-to-anything (V2X) and anything-to-vehicle (X2V) operating modes for both integrated and nonrenewable residential applications. These sharing modes have already been discussed, but resolution approaches are applicable to a specific use case. Other application cases may require additional metrics to plan the fleet of electric vehicles. To avoid that problem, this study proposes the MIP method using a robust Gurobi optimiser based on a generic framework for cooperative power management (CPM). Moreover, the CPM ensures an overall target state of charge (SoC) at leaving time for all the vehicles without generating a rebound peak in total grid power, even without introducing photovoltaic power. Two different methods are proposed based on the flow direction of the EV power. The first method only includes the one-way power flow, while the second increases the two-way power flow between vehicles, operating in vehicle-to-vehicle or vehicle-to-loads modes. A thorough analysis of the findings of the proposed model was conducted to demonstrate the robustness and efficiency of the charging and discharging schedule of several EVs, favouring a sharing economy concept, reducing peak power, and increasing user comfort.

Increased focus on sustainability and energy decentralization has positively impacted the adoption of nanogrids. With the tremendous growth, load forecasting has become crucial for their daily operation. Since the loads of nanogrids have large variations with sudden usage of large household electrical appliances, existing forecasting models, majorly focused on lower volatile loads, may not work well. Moreover, abrupt operation of electrical appliances in a nanogrid, even for shorter durations, especially in “Peak Hours”, raises the energy cost substantially. In this paper, an ANN model with dynamic feature selection is developed to predict the hour-ahead load of nanogrids based on meteorological data and a load lag of 1 h (t-1). In addition, by thresholding the predicted load against the average load of previous hours, peak loads, and their time indices are accurately identified. Numerical testing results show that the developed model can predict loads of nanogrids with the Mean Square Error (MSE) of 0.03 KW, the Mean Absolute Percentage Error (MAPE) of 9%, and the coefficient of variation (CV) of 11.9% and results in an average of 20% daily energy cost savings by shifting peak load to off-peak hours.

It is important to develop a suitable model to calculate electricity demand forecasting requested by decision makers. The present study deals with the electrical long-term peak load demand forecasting using a developed Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) methods. The MLR model is formulated as a function of population and Gross Domestic Product (GDP) for the Gulf Cooperation Council (GCC) region. The Neuro-Fuzzy is thereafter trained using previous sets of data. This training gives a future annual electricity load prediction. The results obtained from the developed models have an acceptable level of mean errors. In general, the GCC region has high-energy consumption influenced by a number of factors, such as population and GDP. The annual variation of both population and GDP growth scenarios is based on development in the country. The obtained results will encourage the GCC through the energy field development and setting the future plans for it. The novelty of the present study is to avoid an increase in generation capacity in mid-term and long-term plans, which will help the GCC countries to avoid load shedding and meet the energy demands in different sectors. The developed models will help the economic development of the GCC countries. It also helps with finding the optimum time for electrical energy trading. The obtained results for the GCC illustrate the average percentage error calculated which was found to be close to 2% and 0.53% in multilinear regression and Neuro-Fuzzy, respectively. These results reduce capital investment, limiting the equipment installed and the expected load needed for better load distribution in the region. In conclusion, the Neuro-Fuzzy is the most accurate technique compared with MLR to estimate future electricity demand and, at the same time, it can be used in power system planning and development.

The power-sharing possibility amongst microgrids (MGs) in networked microgrids (NMGs) offers multiple profits to the NMG by employing an applicable energy management system. An efficient energy management system can provide an adequate compromise in terms of the component sizing of NMGs through MG collaboration. This paper proposes a procedure to size the component for an isolated networked hybrid microgrid. The proposed design procedure relies on the optimum operation of individual MGs. The defined Reduced Factor (RF) identifies the possible size reduction for the dispatchable components, such as diesel generators and the energy storage system of each MG. The introduced RF is based on the operating reserve evaluation obtained from the optimal operation of individual MGs and the correlation between load profiles. Eventually, the simulation and practical results of a networked hybrid MG consisting of three MGs are presented to verify the proposed component sizing procedure. The practical results verify the theoretical expectations. The results show that NPC and capital costs are reduced up to 13% and 17%, respectively.

With the wide spread of new variants of coronavirus that cause the infectious disease COVID-19, governments around the world typically respond by imposing restrictions on people’s activities that range from partial to full lockdowns. This has severe implications on all economic activities, which is manifested by the changes in energy demand. In this study, the impact of COVID-19 on the electricity sector in Jordan is analysed through quantifying the strictness of the government response measures to contain the spread of the pandemic, as calculated by the stringency index, with the electricity demand by the different sectors. Results showed that the minimum peak load in 2020 decreased by 13% as compared to that of 2019. The most affected sectors were the domestic sector, whose share in consumption increased by 8%, and the commercial and hotel sector, whose share decreased by 19%. The concept of an energy-weighted stringency index was introduced to account for the impact of government response measures on the different sectors. The analysis was applied for all Jordan as well as for the three electricity distribution regions. Results also showed that despite measures taken to contain spread of the pandemic, the share of electricity generation by renewables increased from 15% in 2019 to 24% in 2020.

The complexity and uncertainty of the distribution system are increasing as the connection of distributed power sources using solar or wind energy is rapidly increasing, and digital loads are expanding. As these complexity and uncertainty keep increasing the investment cost for distribution facilities, optimal distribution planning becomes a matter of greater focus. This paper analyzed the existing mid-to-long-term load forecasting method for KEPCO’s distribution planning and proposed a mid- to long-term load forecasting method based on ensemble learning. After selecting optimal input variables required for the load forecasting model through correlation analysis, individual forecasting models were selected, which enabled the derivation of the optimal combination of ensemble load forecast models. This paper additionally offered an improved load forecasting model that considers the characteristics of each distribution line for enhancing the mid- to long-term distribution line load forecasting process for distribution planning. The study verified the performance of the proposed method by comparing forecasting values with actual values.

Ultrasonic welding of thermoplastics is widely applied in automobile and aerospace industries. Increasing the weld area and avoiding thermal decomposition are contradictory factors in improving strength of ultrasonically welded polymers. In this study, relations among the loss modulus of carbon-fiber-reinforced polyamide 66 composite (CF/PA 66), time for obtaining stable weld area, and time for CF/PA 66 decomposition are investigated systematically. Then, a double-pulse ultrasonic welding process (DPUW) is proposed, and the temperature evolutions, morphologies and structures of fractured surfaces, and tensile and fatigue properties of the DPUWed joints are measured and assessed. Experimental results show the optimal welding parameters for DPUW include a weld time of 2.1 s for the first pulse, a cooling time of 12 s, and a weld time of 1.5 s for the second pulse. The DPUW process enlarged the weld area while avoided decomposition of CF/PA 66 under appropriate welding parameters. Compared to the single-pulse welded joint, the peak load, weld area, and endurance limit of the DPUWed joint increased by about 15%, 23% and 59%, respectively. DPUW also decreases the variance in strengths of the joints.

Abstract The main purpose of this research paper is to investigate the long-term effects of the proposed demand-side program, and its impact on annual peak load forecasting important for strategic network planning. The program comprises a particular set of demand-side measures aimed at reducing the annual peak load. The paper also presents the program simulations for the case study of the Electricity Distribution Company of Belgrade (EDB). According to the methodology used, the first step is to determine the available controllable load of the distribution utility/area under consideration. The controllable load is presumed constant over the analyzed time horizon, and the smart grid (SG) infrastructure available. The saturation of positive effects during intense program application is also taken into account. Technical and economic input data are taken from the real projects. The conducted calculations indicate that demand-side programs can bring about the same results as the energy storage in the grids with a strong impact of distributed generation from variable renewable sources (V-RES). In conclusion, the proposed demand-side program is a good alternative to building new power facilities, which can postpone investment costs for a considerable period of time.

Electrical power control and management is the key towards improving the efficiency and security of energy supply. Proper management would help in safely diversifying energy supplies, reducing energy consumption during peak load, and improving grid integration condition and reliability. Therefore, this study presents a novel real-time and smart power management system for monitoring and controlling power flow between substations, distributed renewable sources, and loads towards optimising energy consumption and balancing electric power supply. The concept and architecture of the proposed system are discussed in this study within the context of the smart grid principle. The proposed energy management system does not require any changes in the traditional substation. Simulation results and implementation are conducted to demonstrate the validity of the proposed system.

This paper provides further understanding of the peak load effect on micro-crack formation and residual stress relaxation. Comprehensive numerical simulations using the finite element method are applied to simultaneously take into account the effect of the surface roughness and residual stresses on the crack formation in sandblasted S690 high-strength steel surface under peak load conditions. A ductile fracture criterion is introduced for the prediction of damage initiation and evolution. This study specifically investigates the influences of compressive peak load, effective parameters on fracture locus, surface roughness, and residual stress on damage mechanism and formed crack size. The results indicate that under peak load conditions, surface roughness has a far more important influence on micro-crack formation than residual stress. Moreover, it is shown that the effect of peak load range on damage formation and crack size is significantly higher than the influence of residual stress. It is found that the crack size develops exponentially with increasing peak load magnitudes.

The objective of this work is to analyze a gas injection borehole heat exchanger coupled with a desiccant assisted air conditioning system during cooling and heating operation. A common problem that occurs in air conditioning systems is peak loads, during which the cooling or heating power of the soil can be exceeded. To counteract this drawback, a gas injection borehole heat exchanger, which is capable of creating artificial groundwater flow along the heat exchanger by inducing a pressure difference inside the well, is used. Experimental results of the performance differences between a conventional and a gas injection borehole heat exchanger are presented. Under the same inlet conditions, a reduction in the outlet temperature of up to 2 °C is achieved compared with an equivalent conventional borehole heat exchanger in cooling mode. The maximum cooling power is increased by 26%. As a result, a fast and dynamic responding control of the heat transfer between the heat exchanger and the soil is possible. During winter operation, despite the lower drilling depth of the gas injection borehole heat exchanger system, the performance is within the range of a conventional system. The power increase is limited to around 0.2 kWth at a steady state. In conclusion, gas injection borehole heat exchangers can be promising in terms of reliable peak load handling within large geothermal fields.

The existing literature has shown the important role of electrification in deep decarbonization pathways, increasing electricity demand as end uses decarbonize. However, studies have not focused on the effects of electrification on aggregate load shapes and peak demand, which influence power sector investments, operations, and costs. Here we investigate potential impacts of deep decarbonization on regional load shapes and peak electricity demand using a detailed end-use simulation model linked to an electric sector capacity planning model. Scenario results suggest that electrification may contribute to peak load increases and shifts from summer peaks to winter ones, especially in cooler climates due to space heating electrification. We illustrate how net-zero emissions goals can amplify electrification and may entail 120%–165% increases in electric system capacity by 2050 due to a combination of electrification and high renewables deployment. The intensity and frequency of peak demand can be limited by load flexibility (providing incentives for electric end uses to shift away from periods of high demand, e.g. through deferrable electric vehicle charging), alternate end-use technology configurations (deploying higher efficiency end-use equipment to lower electricity consumption during peaks or using dual-fuel systems such as heat pumps paired with gas furnaces), and carbon removal (displacing higher marginal abatement cost electrification while reaching an equivalent emissions cap). This analysis is a first step toward systematically exploring load curves for electrified and decarbonized energy systems, and the results highlight opportunities for future research to better understand load shape impacts and flexibility.

The Mining and Smelting Complex Bor (RTB) is one of the biggest electrical energy consumers in Serbia, with costs that exceed 3 million USD per month, or between 30 and 40 million USD per year. A complex technological process of copper production comprises mining, concentrate production (flotation), smelting and electrolytic refining. In all these stages a large amount of energy is consumed. The electricity is supplied from the power plants using 5 substations: two of them located in Bor, and the remaining three in Majdanpek. The conditions for delivery and billing of the electrical energy were defined by the contract with the supplier. The methods of billing (calculation) the electrical energy costs were changed several times in the previous 25 years. Although the consumed energy is the largest part of the overall costs, there were also some indirect costs affecting the final monthly price. It is clear that even a minor percentage decrease of costs can lead to substantial savings in the total amount. Hence, different technological and organizational activities were undertaken to reduce these costs. The paper presents primarily the effects of administrative measures on the amount of total electrical energy costs in copper production.

The Air-conditioning System (ACS), used in office buildings in the hot summer and cold winter zone of China, are always operate intermittently. The dynamic thermal behaviors of building walls with real climate conditions may be different from those with only the representative day’s climate conditions, due to the time varying nature of the climate, which will lead to the variation of the ACS loads. A numerical calculation was performed to analyze the effects of insulation form on heat behavior of external walls and ACS loads. The results indicate that cooling transmission load with inside insulation reaches its maximum value when the solar-air temperature in daytime is the highest, while that with outside insulation occurs at the time when the air temperature at night is the highest during summer. Heating transmission load for the wall with external and internal insulation both peaks in the day with lowest mean outdoor temperature during the last non-working period. Inside insulation can be considered a better way to reduce the peak load, peak-valley load difference and energy consumption.

The rapidly increasing population growth and expansion of urban development are undoubtedly two of the main reasons for increasing global energy consumption. Accurate long-term forecasting of peak load is essential for saving time and money for countries’ power generation utilities. This paper introduces the first investigation into the performance of the Prophet model in the long-term peak load forecasting of Kuwait. The Prophet model is compared with the well-established Holt–Winters model to assess its feasibility and accuracy in forecasting long-term peak loads. Real data of electric load peaks from Kuwait powerplants from 2010 to 2020 were used for the electric load peaks, forecasting the peak load between 2020 and 2030. The Prophet model has shown more accurate predictions than the Holt–Winters model in five statistical performance metrics. Besides, the robustness of the two models was investigated by adding Gaussian white noise of different intensities. The Prophet model has proven to be more robust to noise than the Holt–Winters model. Furthermore, the generalizability test of the two models has shown that the Prophet model outperforms the Holt–Winters model. The reported results suggest that the forecasted maximum peak load is expected to reach 18,550 and 19,588 MW for the Prophet and Holt–Winters models by 2030 in Kuwait. The study suggests that the best months for scheduling the preventive maintenance for the year 2020 and 2021 are from November 2020 until March 2021 for both models.

The present paper reports the impact of the Covid-19 pandemic on the electricity peak load and power generation in the State of Kuwait during the partial and full curfews imposed in March, April and May 2020 using historic data measured data and the predictions provided by a statistical genetic algorithm model. A quantitative assessment is made of the economic and environmental impacts caused by partial and full lockdowns. Comparison of measured peak demand for 2019 and 2020 with predicted peak demand for 2020 has: (i) enabled an accurate evaluation of residential energy consumption in the state of Kuwait at nearly 18 MWh yearly the highest energy consumption per capita in the world, (ii) shown that the imposition of the curfews to reduce the spread of COVID-19 caused a fall in the demand for electrical power of 17.6% compared with the expected demand and (iii) quantified the reduction in CO2, NOx and CO pollutant emissions produced by power plants due to less fuel being consumed. A mathematical model has been developed to predict the peak electric load in the national grid according to climatic data supplied by the Meteorological Department of Civil Aviation of Kuwait and National Control Center (NCC).

Distribution planning refers to the act of estimating the risks of distribution systems that may arise in the future and establishing investment plans to cope with them. Forecasted loads are one of the most typical variables used to analyze the risk of the distribution system, thus the efficiency of distribution planning may vary depending on its accuracy. For these reasons, a lot of studies are also being conducted to perform load prediction by incorporating the latest methods, such as machine learning (ML). However, the unchangeable fact is that no matter what prediction method is used, the accuracy and reliability of the predicted load can vary depending on the reliability of the data used. In particular, the detection of temporary load increases, due to load transfer that can occur frequently in the distribution system are essential for securing high-quality data. Therefore, in this study, a LSTM (Long Short-Term Memory) based load transfer detection model was proposed, and the appropriateness and reliability of the proposed method were analyzed by comparing actual planned load transfer data with the estimated load transfer results from the proposed model. It was also shown that the proposed model can improve the efficiency and reliability of the distribution planning by reasonably removing load variations, due to load transfer.

In recent years, many buildings have been fitted with smart meters, from which high-frequency energy data is available. However, extracting useful information efficiently has been imposed as a problem in utilizing these data. In this study, we analyzed district heating smart meter data from 61 buildings in Copenhagen, Denmark, focused on the peak load quantification in a building cluster and a case study on load shifting. The energy consumption data were clustered into three subsets concerning seasonal variation (winter, transition season, and summer), using the agglomerative hierarchical algorithm. The representative load profile obtained from clustering analysis were categorized by their profile features on the peak. The investigation of peak load shifting potentials was then conducted by quantifying peak load concerning their load profile types, which were indicated by the absolute peak power, the peak duration, and the sharpness of the peak. A numerical model was developed for a representative building, to determine peak shaving potentials. The model was calibrated and validated using the time-series measurements of two heating seasons. The heating load profiles of the buildings were classified into five types. The buildings with the hat shape peak type were in the majority during the winter and had the highest load shifting potential in the winter and transition season. The hat shape type’s peak load accounted for 10.7% of the total heating loads in winter, and the morning peak type accounted for 12.6% of total heating loads in the transition season. The case study simulation showed that the morning peak load was reduced by about 70%, by modulating the supply water temperature setpoints based on weather compensation curves. The methods and procedures used in this study can be applied in other cases, for the data analysis of a large number of buildings and the investigation of peak loads.

Demand response can reduce peak load, transfer part of peak load to valley load, and reduce system risk at peak time. In this study, a calculation model of power grid loss risk is proposed, which is suitable for evaluating the risk changes caused by the demand response, and calculating the corresponding risk value under the demand response load curve. In the calculation of demand response load, considering the impact of two measures of time-of-use (TOU) price and incentive measure, the TOU and incentive load curve are obtained based on the user's maximum benefit. In order to gain the corresponding load risk index, Monte Carlo simulation method is used, demand response load curve is taken as input, and load point fault time, load point fault rate and expected energy not serve indexes are introduced. Moreover, compared with the risk ratio of the original load curve, the simulation results verify the effectiveness of the proposed risk model, and prove that the demand response measures can transfer the risk of partial high load and balance the risk distribution.

Highly-dynamic floating bodies such as wave energy convertors require mooring lines with particular mechanical properties; the mooring system must achieve adequate station keeping whilst controlling mooring tensions within acceptable limits. Optimised compliant mooring systems can meet these requirements but where compliance is achieved through system architecture, the complexity of the system increases together with the mooring footprint. This work introduces the “Exeter Tether”, a novel fibre rope mooring tether providing advantages over conventional fibre ropes. The tether concept aims to provide a significantly lower axial stiffness by de-coupling this attribute from the minimum breaking load of the line. A benefit of reduced axial stiffness is the reduction of mooring system stiffness providing a reduction of peak and fatigue loads, without increasing mooring system complexity. Reducing these loads improves system reliability and allows a reduction in mass of both the mooring system and the floating body, thus reducing costs. The principles behind the novel tether design are presented here, along with an outline of eight prototype tether variants. Results from the proof of concept study are given together with preliminary findings from sea trials conducted in Falmouth Bay. Results demonstrate that the Exeter Tether can be configured to achieve a significantly lower axial stiffness than conventional fibre rope and that the stiffness is selectable within limits for a given breaking strength. Strain values greater than 0.35 are achieved at 30% of line breaking strength; this represents more than a threefold increase of the strain achievable with a conventional rope of the same material. The tether was subjected to six months of sea trials to establish any threats to its own reliability and to inform future design enhancements in this respect.

The reasons for high carbon emissions from domestic buildings are complex, and have both social and technical dimensions. At the same time, it is costly and very time-consuming to gather reliable data on energy use in the home. The authors had early access to data from the Household Electricity Survey—the most detailed survey of electricity consumption in UK homes ever undertaken—which monitored 250 homes. The data enabled the authors to investigate a series of socio-technical questions drawn up by the UK Government: Why do some households use far more energy than average, whereas others use much less? What potential is there for shifting “peak load” so that electricity demand is more even through the day? Why is base load electricity use so high? The answers were seldom definitive, but statistical tests found significant correlations between high electricity use and social grade, large household size, unemployment and middle age; and between low electricity use and single-person households, small dwellings, and retirement. This paper draws out key findings from the work, and examines how these insights affect our broader understanding of carbon emissions from the built environment.

In this study, the performance of an islanded gas turbine power generation system in Malaysia was investigated. Considering the low fuel efficiency of the plant during peak and part-load operations, an economic analysis was also carried out, over the period of one year (2017). The case study was conducted on the isolated electrical network of the Universiti Teknologi PETRONAS (UTP), which consists of two gas turbine units with a total capacity of 8.4 MW. Simple performance indicators were developed to assess the performance, which can also be applied to other power stations in Malaysia and elsewhere. Meanwhile, the economy of variable load operations was analyzed using the statistical data of generation, fuel consumption, and loads. The study reveals that the capacity factor of the microgrid in the period was between 52.77−63.32%, as compared to the industrial best practice of 80%. The average plant use factor for the period under review was 75.04%, with a minimum of 70.93% and a maximum of 78.61%. The load factor of the microgrid ranged from 56.68−65.47%, as compared to the international best practice of 80%, while the utilization factor was between 44.22−67.655%. This study further reveals that high fuel consumption rates, due to the peak and part-load operations, resulted in a revenue loss of approximately 17,379.793 USD per year. Based on the present performance of the microgrid, suggestions are made for the improvement of the overall performance and profitability of the system. This work can be valuable for microgrid utility research to identify the most economical operating conditions.

Rapid growth in electricity network peak demand is increasing pressure for new investment which may be used for only a few hours a year. Residential air-conditioning is widely believed to be the prime cause of the rise in peak demand but, in the absence of detailed residential demand research, there is no bottom-up empirical evidence to support this supposition or to estimate its impact. This paper first examines the developments in network peak demand, at a national, network distribution, and local distribution feeder level to show recent trends in peak demand. Secondly, this paper applies analytics to the half-hourly consumption data of a sample of Ausgrid's interval metered customers, combined with local weather data, to develop an algorithm which can recognize air-conditioner use and can identify consumption patterns and peak load. This estimate is then compared to system peaks to determine residential air-conditioning's impact on overall demand. Finally, this paper considers the future impacts of air-conditioning load on peak demand as penetration rates reaches saturation levels and new minimum energy performance standards take effect reducing new units peak impacts.

In a way to counter criticism on low cost-effective conventional activated sludge (AS) technology, waste stabilization ponds (WSPs) offer a valid alternative for wastewater treatment due to their simple and inexpensive operation. To evaluate this alternative with respect to its robustness and resilience capacity, we perform in silico experiments of different peak-load scenarios in two mathematical models representing the two systems. A systematic process of quality assurance for these virtual experiments is implemented, including sensitivity and identifiability analysis, with non-linear error propagation. Moreover, model calibration of a 210-day real experiment with 31 days of increased load was added to the evaluation. Generally speaking, increased-load scenarios run in silico showed that WSP systems are more resilient towards intermediate disturbances, hence, are suitable to treat not only municipal wastewater, but also industrial wastewater, such as poultry wastewater, and paperboard wastewater. However, when disturbances are extreme (over 7000 mg COD·L−1), the common design of the natural system fails to perform better than AS. Besides, the application of sensitivity analysis reveals the most influential parameters on the performance of the two systems. In the AS system, parameters related to autotrophic bacteria have the highest influence on the dynamics of particulate organic matter, while nitrogen removal is largely driven by nitrification and denitrification. Conversely, with an insignificant contribution of heterotrophs, the nutrient removal in the pond system is mostly done by algal assimilation. Furthermore, this systematic model-based analysis proved to be a suitable means for investigating the maximum load of wastewater treatment systems, and from that avoiding environmental problems and high economic costs for cleaning surface waters after severe overload events.

To reduce the peak load and electricity bill while preserving the user comfort, a quality of experience (QoE)-aware smart appliance control algorithm for the smart home energy management system (sHEMS) with renewable energy sources (RES) and electric vehicles (EV) was proposed. The proposed algorithm decreases the peak load and electricity bill by deferring starting times of delay-tolerant appliances from peak to off-peak hours, controlling the temperature setting of heating, ventilation, and air conditioning (HVAC), and properly scheduling the discharging and charging periods of an EV. In this paper, the user comfort is evaluated by means of QoE functions. To preserve the user’s QoE, the delay of the starting time of a home appliance and the temperature setting of HVAC are constrained by a QoE threshold. Additionally, to solve the trade-off problem between the peak load/electricity bill reduction and user’s QoE, a fuzzy logic controller for dynamically adjusting the QoE threshold to optimize the user’s QoE was also designed. Simulation results demonstrate that the proposed smart appliance control algorithm with a fuzzy-controlled QoE threshold significantly reduces the peak load and electricity bill while optimally preserving the user’s QoE. Compared with the baseline case, the proposed scheme reduces the electricity bill by 65% under the scenario with RES and EV. Additionally, compared with the method of optimal scheduling of appliances in the literature, the proposed scheme achieves much better peak load reduction performance and user’s QoE.

Lightweight structural applications of magnesium and aluminum alloys inevitably necessitate welding and joining, especially dissimilar welding between these alloys. The objective of this study was to examine the feasibility of joining ZEK100 Mg alloy to Al6022 alloy via ultrasonic spot welding, focusing on effects of welding energy. An interface diffusion layer consisting of α-Mg and Al12Mg17 eutectic structure was observed to form, with its thickness increased from ~0.5 µm to ~30 µm with increasing welding energy from 500 J to 2000 J. The tensile lap shear peak load or strength and critical stress intensity of the welded joints first increased and then decreased with increasing welding energy, with their peak values achieved at 750 J. Fatigue life of the joints made at 750 J and 2000 J was equivalent at the lower cyclic loading levels, while it was longer for the joints made at 750 J at the higher cyclic loading levels. Fatigue fracture mode changed from interfacial failure to mainly transverse-through-thickness crack growth with decreasing cyclic loading level, which corresponded well to the bi-linear characteristic of S-N curves. Crack initiation basically occurred at the weld nugget border and at the interface between the two sheets, which can be understood via a theoretical stress analysis.

Photovoltaic (PV) became the new competitive energy resources of the planet and needs to be engaged in grid to break up the congestion in both Distribution and Transmission systems. The objective of this research is to reduce the load flow through the distribution and transmission equipment by 20%. This reduction will help in relief networks loaded equipment’s in all networks. Many projects are starting to develop in the GCC countries and need to be organized to achieve maximum benefits from involving the Renewable Energy Sources (RES) in the network. The GCC countries have a good location for solar energy with high intensity of the solar radiation and clear sky along the year. The opportunities of the solar energy is to utilize and create a sustainable energy resource for this region. Moreover, the target of this research is to engage the PV technology in such a way to lower the over loaded equipment and increases the electricity demand at the consumer’s side.

Thin-walled structures like crash boxes may be used as energy absorption members in automotive chassis. There have been many studies addressing the behaviors of energy absorption members on frontal crash. These researches have attempted to predict the energy absorption and maximum impact load in shell structures. The energy absorption and maximum impact load depend on many parameters including boundary condition, history of plastic deformation during metalworking, geometry; and material and impact energy (i.e. mass and velocity of the striker). This study examined the crash behavior of square tube made of the extruded aluminum alloy 6063T4 using an elastic boundary condition -instead of rigid boundary condition- on the bottom of a crash box, In addition, the effect of elastic boundary condition on energy absorption of square tubes under the impact load was investigated both numerically and experimentally. Results showed that using elastic boundary could change the deformation mode and decrease the maximum impact load. Further, a high correlation between the numerical and experimental results validated the findings of the study.