Load Curve Research Articles

Experimental behaviour of hybrid carbon steel – stainless steel bolted connections subjected to electrochemical corrosion

To minimise cost in practical applications whilst still obtaining a satisfactory performance, stainless steel is often employed in aggressive marine environments to ensure the durability of the structure, whilst parts of the structure less susceptible to corrosion employ conventional carbon steel. When the two materials are in direct contact in the connection region, galvanic corrosion can occur over time thus leading to an increased level of corrosion of the less noble carbon steel parts and an associated degradation in the structural performance. A comprehensive experimental study on the shear behaviour of hybrid carbon steel – stainless steel bolted connections subjected to electrochemical corrosion is presented in this paper. A total of 30 bolted connection specimens were designed and assembled from hot-rolled carbon steel and stainless steel plates. Two bolt grades, the high strength bolts 10.9 and the precipitation hardening stainless steel bolts 10.9 were used to connect the plates. Employing the electrochemical corrosion method, the specimens were subjected to various levels of corrosion by adjusting the duration over which the specimens were sunk in the corrosive medium, which resulted in both different corrosion morphology as well as different levels of mass loss of the carbon steel plates due to corrosion. The corroded connection specimens were thereafter tested to failure under shear exhibiting 4 different failure modes. The load versus deformation curves were recorded and are reported herein, whilst the observed failure modes were also documented. It was revealed that increased level of corrosions led to decreased levels of bolt pretention forces and ultimate resistances of the connections, albeit the severity of the degradation strongly depended on the adopted joint configuration and resulting failure mode.

Research on Coordinated Optimization of Source-Load-Storage Considering Renewable Energy and Load Similarity

Currently, the global energy revolution in the direction of green and low-carbon technologies is flourishing. The large-scale integration of renewable energy into the grid has led to significant fluctuations in the net load of the power system. To meet the energy balance requirements of the power system, the pressure on conventional power generation units to adjust and regulate has increased. The efficient utilization of the regulation capability of controllable industrial loads and energy storage can achieve the similarity between renewable energy curves and load curves, thereby reducing the peak-to-valley difference and volatility of the net load. This approach also decreases the adjustment pressure on conventional generating units. Therefore, this paper proposes a two-stage optimization scheduling strategy considering the similarity between renewable energy and load, including energy storage and industrial load participation. The combination of the Euclidean distance, which measures the similarity between the magnitude of renewable energy–load curves, and the load tracking coefficient, which measures the similarity in curve shape, is used to measure the similarity between renewable energy and load profiles. This measurement method is introduced into the source-load-storage optimal scheduling to establish a two-stage optimization model. In the first stage, the model is set up to maximize the similarity between renewable energy and the load profile and minimize the cost of energy storage and industrial load regulation to obtain the desired load curve and new energy output curve. In the second stage, the model is set up to minimize the overall operation cost by considering the costs associated with abandoning the new energy sources and shedding loads to optimize the output of conventional generator sets. Through a case analysis, it is verified that the proposed scheduling strategy can achieve the tracking of the load curve to the new energy curve, reducing the peak-to-valley difference of the net load curve by 48.52% and the fluctuation by 67.54% compared to the original curve. These improvements effectively enhance the net load curve and reduce the difficulty in regulating conventional power generation units. Furthermore, the strategy achieves the full discard of renewable energy and reduces the system operating costs by 4.19%, effectively promoting the discard of renewable energy and reducing the system operating costs.

Three-heat-reservoir thermal Brownian heat pump and its performance limits

Three-heat-reservoir (THR) heat pump has the originality of using low-grade heat to achieve pumping heat, and it can recycle waste heat and reduce environmental pollution effectively. Based on the research ideas of macro THR heat pumps and micro three-terminal thermoelectric devices, this paper proposes a THR thermal Brownian heat pump cycle model. This cycle is driven by low-grade “heat” to work, and it may be seen as a combined cycle consisting of a two-reservoir thermal Brownian heat pump and a two-reservoir thermal Brownian engine. Using non-equilibrium thermodynamics, expressions for heating load and coefficient of performance (COP) are derived. The coupling relation between external forces of two motors is obtained by solving the heat balance equations, and cycle performance is further obtained. The maximal heating load is obtained by optimizing barrier height, and the lower limit of COP is also given. Results show that the THR thermal Brownian heat pump can achieve pumping heat by using low-grade “heat”. The internal parameters affect the coupling between two-heat-reservoir thermal Brownian engine and two-heat-reservoir thermal Brownian heat pump, which determines cycle performance directly. The curve of heating load about COP presents a parabolic-like shape, and an optimal COP exists to maximize heating load.

Disaggregation Model: A Novel Methodology to Estimate Customers’ Profiles in a Low-Voltage Distribution Grid Equipped with Smart Meters

In the context of increasingly necessary energy transition, the precise modeling of profiles for low-voltage (LV) network consumers is crucial to enhance hosting capacity. Typically, load curves for these consumers are estimated through measurement campaigns conducted by Distribution System Operators (DSOs) for a representative subset of customers or through the aggregation of load curves from household appliances within a residence. With the instrumentation of smart meters becoming more common, a new approach to modeling profiles for residential customers is proposed to make the most of the measurements from these meters. The disaggregation model estimates the load profile of customers on a low-voltage network by disaggregating the load curve measured at the secondary substation level. By utilizing only the maximum power measured by Linky smart meters, along with the load curve of the secondary substation, this model can estimate the daily profile of customers. For 48 secondary substations in our dataset, the model obtained an average symmetric mean average percentage error (SMAPE) error of 4.91% in reconstructing the load curve of the secondary substation from the curves disaggregated by the model. This methodology can allow for an estimation of the daily consumption behaviors of the low-voltage customers. In this way, we can safely envision solutions that enhance the grid hosting capacity.

Finite element simulation of axial crushing of flat and C-channel composite coupons using a modified mesomodel

The present article investigates the axial crushing behavior of IM7/8552 material system. Two types of specimens are considered-(a) flat coupons with saw-tooth trigger and (b) C-channels with 45° chamfer as a trigger. All the crushing test data (load, displacement and velocity history curves) is shared in the Crashworthiness working group (CWG) consortium of the Composite material handbook (CMH17.org). Flat crush coupons are tested at the University of Utah and the C-channels are tested at the John H. Glenn Research Center of the National aeronautics and space administration (NASA). Three types of layups are considered for flat coupons: QI1-[90/±45/0]2S, HL2-[902/02/±45/02] and HL3-[90/45/02/90/-45/02]. Two types of layups are considered for C-channels: HL2-[902/02/±45/02] and HL3-[90/45/02/90/-45/02]. Both the specimens are subjected to crush loading with an initial velocity and lumped mass attached to the impactor. All the ply and interface material properties come from the current authors' testing campaign except for strength and stiffness in the fiber direction. The crushing test cases are simulated in LS-Dyna software. Ply and interface behavior is described via user-defined material subroutines of LS-Dyna software. Each ply is modeled using 3D solid elements and the interfaces are modeled explicitly using cohesive elements. Intraply behavior is described using a novel modified meso model proposed by the present authors. Intraply model accounts for nonlinear damage, plasticity, ply fracture toughness and finite element mesh size. Interply response is modeled using bi-linear law. Predictions from present FE models showed a good correlation with crush test data. The effect of finite element model parameters e.g., filtering frequency, mesh size, interface properties, ply fracture toughness, element erosion criterion, contact stiffness and friction coefficient on crushing predictions is demonstrated by conducting parametric studies.

Optimization scheduling of power system under load winter and summer double-peak scenario

In recent years, due to people’s need for a better life and the impact of extreme weather factors, the annual load curve shows a winter-summer double-peak pattern in summer and winter. In addition, due to the large-scale grid integration of renewable energy sources such as wind power, the uncertainty of its output has further increased the net load during the double-peak scenario, putting the power system under even greater pressure to supply electricity. In this paper, we study the day-ahead hierarchical optimal scheduling model of the power system under the double-peak scenario, considering the pumped-storage flexibility space and the extreme boundary flexibility demand. We simulate and analyze the impacts of different operation modes of the pumped-storage power plant on the scheduling results. The simulation shows that the proposed method can give full play to the effect of pumped storage peak shaving and valley filling and guarantee the reliability of the power supply under the double-peak scenario.

Predicting electricity demand in Cameroon with enhanced modified exponential models: A focus on the southern integrated grid (S.I.G)

This paper is centered on the prediction of electricity demand within the Southern Integrated Grid (S.I.G) for the forthcoming six years (2022–2027) through the utilization of advanced forecasting techniques. These methodologies encompass modified exponential, probabilistic or stochastic extrapolation and linear regression models. The foundation of these forecasts is established on a comprehensive examination of load curve data spanning 12 years (2010–2021) and an analysis of the factors influencing electricity demand in Cameroon. The models are implemented using Matlab and Excel, enabling the projection of results. The results of this analysis reveal a consistent growth trend in Cameroon's electricity demand between 2022 and 2027. the modified exponential model foresees an increase from 991.16MW to 1189.25MW, the probabilistic model from 969.68MW to 1109.20MW, and the linear regression model from 853.46MW to 975.89MW. Despite the inherent limitations, the outcomes prove to be satisfactory, marked by a high level of accuracy and reliability, as evidenced by mean absolute percentage error (MAPE) values of 0.2% for the modified exponential and probabilistic methods and 0.55% for the linear regression method. These scores fall well below the industry-accepted benchmark of MAPE < 10% outlined by the Lewis benchmark. In a comparative analysis, it is evident that the modified exponential and probabilistic models exhibit superior accuracy.

Power control strategies for modular-gravity energy storage plant

This paper presents the first systematic study on power control strategies for Modular-Gravity Energy Storage (M-GES), a novel, high-performance, large-scale energy storage technology with significant research and application potential. Addressing the current research gap in M-GES power control technology, we propose two corresponding compensation modes and several power control strategies for Hybrid M-GES and M-GES. The effectiveness of these strategies is validated through simulations on the MATLAB/Simulink platform, including tests using sinusoidal power and feasibility verification based on a natural California load curve. Additionally, we analyze the normal and abnormal operating conditions of M-GES. In conclusion, the characteristics of the proposed control strategies are summarized, providing a guideline for further research.

Low-Voltage Distribution Network Loss-Reduction Method Based on Load-Timing Characteristics and Adjustment Capabilities

The primary contributors to elevated line losses in low-voltage distribution networks are three-phase load imbalances and variations in load peak–valley differentials. The conventional manual phase sequence adjustment fails to capitalize on the temporal characteristics of the load, and the proliferation of smart homes has opened up new scheduling possibilities for managing the load. Consequently, this paper introduces a loss-reduction method for low-voltage distribution networks that leverages load-timing characteristics and adjustment capabilities. This method combines dynamic and static methods to reduce energy consumption from different time scales. To commence, this paper introduced a hierarchical fuzzy C-means algorithm (H-FCM), taking into account the distance and similarity of load curves. Subsequently, a phase sequence adjustment method, grounded in load-timing characteristics, was developed. The typical user load curve, derived from the classification of user loads, serves as the foundation for constructing a long-term commutation model, therefore mitigating the impact of load fluctuations on artificial commutation. Following this, this paper addressed the interruptible and transferable characteristics of various smart homes. This paper proposed a multi-objective transferable load (TL) optimal timing task adjustment model and a peak-shaving control strategy specifically designed for maximum sustainable power reduction of temperature-controlled loads (TCL). These strategies aim to achieve real-time load adjustment, correct static commutation errors, and reduce peak-to-valley differences. Finally, a simulation verification model was established in MATLAB (R2022a). The results show that the proposed method mainly solves the problems of three-phase imbalance and large load peak–valley difference in low-voltage distribution networks and reduces the line loss of low-voltage distribution networks through manual commutation and load adjustment.

Multi-source and Multi-level Coordination of Energy Internet under V2G based on Particle Swarm Optimization Algorithm

In order to effectively improve the excessive load of microgrid during peak hours of urban electricity consumption, a multi-source and multi-level coordination method of energy Internet under V2G based on particle swarm optimization algorithm was proposed. First, a mathematical model for V2G energy integration in microgrids was developed and a scheduling concept based on a particle-by-particle optimization algorithm was used. Second, an improved PSO algorithm is proposed and experimentally validated, and the experimental results are compared with previous particle swarm optimization algorithms. Experiments have shown that as the number of iterations increases, the value of the objective function decreases and the optimal solution can be obtained until the maximum number of iterations is reached. The iteration speed and power processing cost of the improved PSO algorithm are better than before.The original load curve is the load trough period from 23:00 to 6:00, and two load peaks occur from 12:00 to 14:00 and 19:00 to 22:00. The V2G technology basically realizes the coordinated control of microgrid electric energy and achieves the effect of peaking and valley filling. The improved algorithm has obvious improvement compared with the original power grid state. Conclusion: The application of EV V2G technology can smooth the daily load curve of power grid and coordinate the electric energy of micro-grid to achieve ``peak cutting and valley filling'', and the effect of this algorithm is more outstanding than the previous algorithm. Finally, the future development direction and suggestions of V2G technology are put forward.The power grid with V2G discharge depth limit has the ability to basically reduce and eliminate the daily peak load, so the technology has broad research space and development prospects.

Optimization of Magnetic Pump Impeller Based on Blade Load Curve and Internal Flow Study

Compared to traditional centrifugal pumps, magnetic pumps are widely used in industries such as chemical, pharmaceutical, and petroleum due to their characteristics of leakage-free operation and the ability to transport toxic and corrosive fluids. However, the efficiency of magnetic pumps is relatively low. Improving the efficiency of pumps helps to reduce energy loss and lower industrial costs. In this study, a magnetic pump was chosen as the research subject. The study aims to improve the efficiency and stability of the magnetic pump by optimizing the impeller blades based on the load curve. A combined approach of a numerical simulation and experimental verification was used to investigate the impact of the anterior loading point (AL), posterior loading point (PL), and slope (SL) in the blade loading curve on the pump’s performance. The slope, which had the most significant impact on pump performance, was selected as the dependent variable to analyze the internal pressure pulsation and main shaft radial force of the magnetic pump. The research found that the hydraulic performance test results of the magnetic pump were in good agreement with the simulation results. When efficiency is used as the optimization objective, the anterior loading point should be moved as far back as possible, and the posterior loading point should be moved as far forward as possible. Through the study of internal pressure fluctuations and radial forces within the pump, the radial force distribution is sequentially as follows: the anterior loading method, posterior loading method, and middle loading method at a rated flow rate. The maximum pressure pulsation amplitude was near the volute casing diffuser area. Compared to the original pump, the optimized magnetic pump showed a 5.05% improvement in hydraulic efficiency under the rated conditions. This research contributes to enhancing the performance and efficiency of magnetic pumps, making them more suitable for various industrial applications.

Realization of an Optimum Load for Wireless Power Transfer System

Wireless power transfer (WPT) system has been an integral part of personal living since its regained interest, especially the magnetic resonance (MR) scheme. MR-WPT scheme suffers, however, change of the coil separation distance and various alignment errors. This paper reports a realization of optimum load for MR-WPT system, which can change the loading impedance accordingly for different coupling coefficients between the Tx and Rx coils. A simple varactor circuit is adopted to realize the optimum load curve. Usefulness of this is demonstrated through both the steady and transient analysis. The proposed realization relies on an open-circuit scheme, and hence it is suitable for scenarios with low-cost and small-size requirements.

Carbon emissions of power transmission and transformation projects in the whole life cycle for smart sustainable energy systems

The study investigates the optimization of life cycle carbon emissions in smart sustainable energy systems through power transformation and transmission project power load predictions. Firstly, a multi-task learning-based short-term user load forecasting technique is developed, where the power load curves of multiple residential customers are grouped and classified using the K-means clustering method. Additionally, the Bidirectional Long Short-Term Memory (BiLSTM) technique is introduced to anticipate the power load intelligently. Secondly, a life cycle carbon emission assessment model for the power transmission and transformation project (PTTP) is constructed based on the life cycle assessment (LCA) method, which divides the project's life cycle into four stages: production, installation and construction, operation and maintenance, and demolition. Finally, an experimental evaluation of this model is conducted. The results demonstrate that compared with the baseline model Long Short-Term Memory (LSTM), this model achieves a significantly lower average Mean Absolute Error (MAE) at 3.62% while achieving significantly higher accuracy in power load forecasting at 94.34%. A comprehensive examination of carbon emissions across all four phases reveals that overall carbon emissions are highest during the operation and maintenance stage followed by the equipment production stage and installation/construction stage, with the lowest overall carbon emissions observed. Hence, this study endeavors to forecast power load demand with precision and identify the principal determinants of carbon emissions in power engineering. By discerning and managing these key factors, an optimal, energy-efficient intelligent power load scheme can be derived.

A deep clustering framework for load pattern segmentation

In recent years, the widespread use of smart meters in power networks has generated a wealth of data from electricity customers. However, much of this real-world smart meter dataset lacks labeled data, posing a significant challenge that can be addressed by clustering. Traditional clustering methods often struggle in high-dimensional spaces, leading to less accurate results and increased computational demands. In response to these challenges, this research introduces a framework that utilizes a deep-learning-based clustering approach to address the issue of accurate customer clustering based on usage patterns in unlabeled data. Using an autoencoder, our approach integrates dimensionality reduction and clustering into an end-to-end unsupervised learning framework. Our algorithm significantly improves load profiling by tackling challenges related to nonlinear decision boundaries at the autoencoder bottleneck. Unlike traditional approaches, we propose separating the optimization of reconstruction and cluster loss, bridging the gap between clustering quality and reconstruction efficiency. We rigorously analyze the performance of our approach by comparing classical and state-of-the-art algorithms using two real-world smart meter data. We provide a comprehensive comparative analysis of our method against five common dimension reduction techniques used in high-dimensional clustering. The experimental analysis concludes that the proposed algorithm significantly enhances load profiling more than others, as confirmed through detailed load curve analysis and clustering validity indexes. This comprehensive assessment highlights the effectiveness and versatility of our proposed methodology when compared to others. Moreover, this research advances load profiling in smart grid analytics, providing practical insights for utilities and stakeholders looking to optimize power network operations.

Research on the Reinforcement Effect and Bearing Characteristics of High-Pressure Jet-Grouting Piles on Covered Road Composite Ground in Landfill Sites

There is a notable difference between garbage pile foundations and general site foundations; due to their uneven particles, complex structure, and diverse composition, there are relatively few cases that can be used for reference. In this study, with the aim of renovating a landfill in Shenzhen, bearing-layer reinforcements were introduced in the overlying road of a garbage heap dominated by construction waste. The bearing capacity of a single-pile composite foundation was studied through a core-pulling test of high-pressure jet-grouting piles, a static load test of the bearing capacity of the single-pile composite foundation, design estimation, and numerical analysis. The results show that the obtained eigenvalue of the design estimate was 267.8 kPa, and the eigenvalue of the field test was between 182.58 kPa and 196.89 kPa, meeting the design requirement of an eigenvalue of no less than 175 kPa. The bearing capacity of the composite foundation of the single jet-grouting pile was analyzed using the ABAQUS numerical simulation software; the characteristic value of the bearing capacity of the single-pile composite foundation was 186.01 kPa, and the variation trend of its settlement–load curve was the same as that of the field test results, which met the design requirements. High-pressure jet-grouting pile technology has achieved remarkable results in the reinforcement of foundations that are mainly composed of construction waste.

Optimization of Topological Reconfiguration in Electric Power Systems Using Genetic Algorithm and Nonlinear Programming with Discontinuous Derivatives

This article addresses a comprehensive analysis of power electrical systems, employing a combined approach of genetic algorithms and mathematical optimization through nonlinear programming with discontinuous derivatives (DNLP) in GAMS. The primary objective is to minimize economic losses and associated costs faced by the network operator following disruptive events. The analysis is divided into two fundamental aspects. Firstly, it addresses the topological reconfiguration of the network, involving the addition of lines and distributed energy resources such as distributed generation. To determine the optimal topological reconfiguration, a genetic algorithm was developed and implemented. This approach aims to restore electrical service to the maximum load within the system. Secondly, an optimal energy dispatch was performed for each generator, considering the variation in load throughout the day. The system’s load curve is taken into account to determine the optimal energy distribution. Thus, the problem of economic losses is approached from two perspectives: the minimization of costs due to nonsupplied electrical energy and the determination of efficient energy dispatch for each generator after network reconfiguration. For the analysis and case studies, simulations were conducted on the IEEE 9- and 30-node test systems. The results demonstrate the effectiveness of the proposed solution, evaluated in terms of reduced load shedding and economic losses.

Impacts of COVID-19 lockdown period on the Algerian power grid demand

The coronavirus disease-2019 (COVID-19) spread out at the end of 2019 has sadly caused millions of human losses and hundreds of millions of cases and stressful health situations. As a result, governments forced the worldwide population to stay confined and change their social activities and working behaviors. Under such conditions all economic sectors have been impacted, therefore global electricity consumption pattern has changed consequently. The object of this study is to calculate energy drop for such circumstances to make strategies to face such events in the future. The study we conducted during the period of confinement aims to identify the effects of the Corona epidemic on electricity consumption in Algeria by emphasizing four months (March, April, May, and June) for four years (2018, 2019, 2020, and 2021) by comparing monthly load curves and calculating load deviation for each month.

Research on load curve fitting method based on K-means algorithm

Some areas of China are affected by the fluctuation of new energy output and load, and the power supply as a whole presents an unbalanced phenomenon of abundant electricity supply but shortage of power supply. To understand the characteristics of the load electricity consumption curve more accurately, this paper proposes a method of fitting the load electricity consumption curve based on K-mea ns algorithm. First, analyze the characteristics of the load electricity consumption based on the industry subdivision, and build the load electricity consumption characteristic index system. Then, the k-mea ns cluster analysis is carried out based on the characteristics of the load power consumption curve of the subdivided industries to determine the number of clusters, and the curve fitting and deviation test are carried out for each cluster group. This paper carries out practical analysis and theoretical research based on the data of typical industry load power consumption curve in Ningxia.

Distributed control of energy storages for multi-time-step peak load shaving in a microgrid

Distributed energy storages (ESs) will be widely used in the future smart microgrids due to their continuous technology improvement and cost reduction. Meanwhile, the distributed control method also gains increasing attention for controlling distributed devices to protect user privacy and reduce computation burden compared with the centralized scheme. In this paper, a distributed control method of ESs is proposed for multi-time-step peak load shaving in a microgrid. Considering the ES efficiency is related to its power, an optimization is constructed to minimize the power loss during ES operations when performing peak load shaving function. By analyzing the net load curve and the ES available capacity, the multi-time-step peak load shaving problem is transformed into single-time-step ES operations at each time step. The Combine-Then-Adapt diffusion strategy is united with the consensus + innovation strategy to realize the peak load shaving in a fully distributed way. Case studies verify the efficiency of the proposed method.

Managed residential electric vehicle charging minimizes electricity bills while meeting driver and community preferences

Transitions to electric vehicles (EV) are expected to increase electricity use in residences, where most tend to recharge. We develop a mathematical programming framework for shifting residential EV charging during low electricity pricing hours to minimize the additional electricity costs that a household incurs from charging their vehicle. The model also aims to meet additional household and their community preferences by adopting four secondary objectives: charging as soon as possible when the user arrives at home, charging as late as possible before the user leaves home, charging for valley filling and peak shaving of the residential load, and charging in a shared community hub by using a fast charging station. We analyze granular residential energy data from a sample of Austin households in 2018. We conduct an empirical analysis to compare households’ electricity bills under four electricity pricing schemes, including flat rates and time-of-use rates, both with and without a separate meter for EV charging. Our findings indicate that if charging behaviors remain unchanged, installing a separate meter for EVs is more expensive than treating EVs as another residential plug load. However, under time-of-use EV charging tariffs, households should adjust their charging behaviors, as validated by our optimization model. Implementing all four secondary charging objectives successfully avoids the on-peak periods of the EV charging rate and reduces households’ overall daily electricity costs by 38.87 % during the summer and by 44.3 % during the winter. Charging as soon as possible, charging as late as possible, and charging at a shared station provide drivers with increased flexibility. Charging as soon as possible and charging as late as possible lead to the lowest charger utilization, with the former having the longest charging stop time before home departure and the latter allowing for the longest charging start time upon home arrival. Charging at a shared station gives rise to the lowest share of charging time over dwell time. Charging for valley filling and peak shaving of the residential load offers less flexibility but has the advantage of flattening the load curve and mitigating high peak loads. This proves crucial in safeguarding the community’s energy distribution infrastructure.