Network Loss Research Articles

Investigation of the effects of electric vehicle charging stations and solar energy integration on grid performance

In this study, the negative effects of electric vehicle charging stations, whose use has rapidly increased all over the world in the last few years, on the electricity network were analyzed. The charging station was purchased and installed in a suitable area. Measurements taken using the energy analyzer at this charging station were examined. An electrical network model has been prepared. IEEE 6-bus power test system data was used for this model. The model was built using Electrical Transient Analyzer Program (ETAP). In addition to the existing loads on the electricity grid, the loads that will occur with the increase in electric vehicle charging stations have also been added. In this case, this electrical network was analyzed using the Newton-Raphson load flow algorithm with the existing load data of the generators, buses and data of the added charging station loads in the IEEE 6-bus test system network. Considering that one thousand additional charging stations come as load for each load bus, 22 MW load was added. In order to reduce the power losses detected here, grid-connected solar power plants have been proposed. By adding these power plants, it is aimed to reduce the negative effects of the load increase caused by electric vehicle charging stations. For the case of adding solar power plants (SPP), the results were examined by making load flow and it was shown that the network losses decreased. As a result of the study, as a result of adding electric vehicle charging station loads (EVCS) to each load bus in addition to the existing loads, active power loss doubled and reactive power loss reached more than three times. In the buses with the recommended consumption, these losses, which increased with the energy production by load solar power plants, decreased by more than half.

Influence of the leakage in air supply networks on the efficiency of application of pneumatic backfill equipment

Purpose. Determining the influence of the degree of pneumatic energy losses in the non-hermetic air supply network on the efficiency of using installed equipment in the pneumatic method of waste utilization from mining and beneficiation of minerals. To achieve this purpose, a tightness control device was developed using the method of fixed volumes. Methods. Using the molecular-kinetic theory of gases, the thermodynamics and hydrodynamics laws, the characteristics of an open thermodynamic system under excess pressure and with a degree of a capillary-type leakage are investigated. Pneumatic energy losses in the pneumatic supply pipeline are determined by the mass of compressed air leaked due to its poor tightness. Findings. The peculiarities of the tightness control of hollow products by the method of fixed volumes have been summarized. The dependences of non-production losses of compressed air and the corresponding pressure drop on the geometric characteristics of the air supply pipeline, the leakage in supply pipeline, and the time of assessing the degree of leakage have been determined. Originality. For the first time, the relationship between the parameters of compressed air in an open thermodynamic system under excess pressure during air leakage and its heat exchange with the environment has been revealed. The influence of compressed air leakage in air supply networks on the efficiency of using vibration-pneumatic machines with an annular ejector in backfill technologies has been determined. Practical implications. The research results can be used to improve the existing or develop the new technological schemes of air supply equipment that use pneumatic energy at mining and metallurgical enterprises. Minimization of energy consumption can significantly expand the scope of application of pneumatic backfilling method.

The power flow analysis for electric power network: A tutorial article

The power system network is complex. This makes it more challenging to obtain valuable information because of its hundreds of buses and transmission lines. In addition, solving the steady-state equations of the power system network requires substantial mathematical computations. The power flow analysis is a study of the electrical power system. Engineers and utility companies use it for the design, control, planning, and future improvement of the electrical power network. Power flow analysis calculates the voltage magnitude, phase angles, active and reactive power flows, losses of the power system network under various loads, and generation conditions. This paper is a tutorial article aimed to present the power flow analysis techniques of Gauss-Seidel, Newton-Raphson, and Fast-Decoupled approaches. The Power World Simulator is used to demonstrate the software implementation of the approaches. The results show that Newton Raphson and Fast Decoupled have a faster convergence. The Newton Raphson method is the popular technique used for the analysis. An illustrative example for an IEEE 3-bus system on each technique is also presented in the article.

Toward optimal voltage/VAR control with smart PVs in active distribution networks

Rapidly increasing penetration of distributed PVs brings great challenges to distribution networks. Driven by the revised IEEE 1547 standard, inverter-based smart PVs provide customized control and voltage regulation capabilities. This paper presents a comprehensive study investigating voltage/VAR control considering network reconfiguration, VAR optimization, and smart PVs. To do so, a second-order cone programming-based VAR optimization and network reconfiguration (VONR) model is formulated. The enhanced necessary radiality condition, and the extensions for multiple smart PV operation modes and multi-period operation are provided. A comprehensive case study considering various network and smart PV operation modes, and hourly VONR operation is conducted. The results show that implementing VONR with smart PV reactive power dispatching optimizes the scheduling plans, resulting in a reduction of network loss by 59.97 % and 30.37 % for the 33-bus and 123-bus test systems, respectively, compared to the no-control operation.

Determination of the Optimal Level of Reactive Power Compensation That Minimizes the Costs of Losses in Distribution Networks

The objective of the presented paper is to verify economically justified levels of reactive energy compensation in the distribution network in the new market conditions, including the extensive use of smart metering systems, new types of load, or distributed generation. The proposed methodology is based on the minimization of annual costs of losses caused by the flow of reactive energy to the supplied loads through the equivalent resistance of the distribution system determined on the basis of statistical energy losses in this network. The costs of losses are compared to the costs of using compensating devices expressed by the levelized costs of reactive energy generation. The results are the relations describing the optimal annual average value of the tgφ factor to be maintained by customers to optimize the cost of loss of the distribution network caused by reactive energy flows. The dependence of the optimal tgφ value on the analyzed load and network parameters is also discussed. The resulting optimal tgφ levels should be considered in the tariffication process of services offered by distribution system operators to improve capacity and limit the costs of power network operation due to reactive energy transmission.

Power flow traceable P2P electricity market segmentation and cost allocation

This study explores peer-to-peer (P2P) electricity trading, emphasizing not just the export and consumption, but also the feasible physical supply of electricity and the use of distribution network assets. Building on a transaction-oriented dynamic power flow tracing model, a novel P2P market architecture is proposed. This architecture integrates the electricity market with the power network, considering technical constraints, network losses, and asset usage. The network is segmented into potential markets using second-order cone programming (SOCP), with an optimization problem introduced for loss-allocation. This problem merges network physical analysis and variable outputs from distributed energy resources (DERs). A graph-based P2P electricity trading model is designed to determine optimal transaction cost allocation and maximize benefits for both DERs and consumers. A case study on a modified IEEE 33-node test feeder substantiates the benefits of this market structure, demonstrating increased revenues for DERs and reduced bills for consumers compared to traditional feed-in-tariffs.

Performance assessment of upstream and downstream losses in a passive optical network utilizing a 19-core multicore fiber

Performance assessment of upstream and downstream losses in a passive optical network utilizing a 19-core multicore fiber

Research on the operation optimization of Integrated Energy System based on dual model refined simulation of thermal network

Accurately assessing energy flow is of paramount importance for achieving the dual objectives of economic efficiency and energy efficiency in the operation of electric-thermal integrated energy systems. The generic thermal network model is commonly used to describe operating characteristics under baseline conditions. However, due to the high-dimensional nonlinear nature of solving its parameters, traditional methods have limitations. In this study, to address this issue, we conduct dual-mode detailed simulation analysis for the hydraulic and thermal processes of the thermal network. The finite difference method is employed to calculate dynamic parameters. Based on this, a network loss model is constructed using the mean square deviation method. To achieve the dual objectives of integrated energy system operation optimization, the NSGA-II algorithm and VIKOR decision rules are applied to obtain the optimal solution. Using these methods and taking a specific electric-thermal integrated energy system in the northern region as an example, we simulate and analyze the data of the system operation under three thermal network modes. The results indicate that the refined thermal network model can reduce the total operating cost to $16,871 and achieve an energy efficiency of 83.219 %, outperforming the results of other operational modes.

Analisis Ketidakstabilan Beban Arus Netral dan Rugi-Rugi Daya Pada Trafo Distribusi Penyulang Pas 21 PT. PLN UPB Banda Aceh

This research focuses on analyzing power losses due to load imbalance in the medium-voltage distribution network in Pasar Aceh. The aim is to determine the magnitude of power losses that occur at the distribution substation in Pasar Aceh. The research method involves analysis, calculations, and simulations using the ETAP 12.6 software. The results of the analysis indicate that load imbalance in the Pasar Aceh substation leads to power losses. The Trafo Samping Pasar Aceh experiences power losses of 0.77% with a load imbalance of 16.33%. The second transformer in the BNI 46 area incurs power losses of 0.98% with a load imbalance of 30.33%, and the transformer in the BLKG KOTTY area has power losses of 1.17% with a load imbalance of 50.33%. These findings affirm that the greater the load imbalance, the higher the resulting power losses in the electrical distribution network.

Reconfiguration and displacement of DG and EVs in distribution networks using a hybrid GA–SFLA multi-objective optimization algorithm

A combined algorithm for reconfiguring and placement of energy storage systems, electric vehicles, and distributed generation (DG) in a distribution network is presented in this paper. The impact of this technique on increasing the network’s resilience during critical periods is investigated, as well as improvements in the network’s technical and economic characteristics. The three objective functions in this regard are the network losses, voltage profile, and running costs, which include the costs of purchasing electrical energy from the upstream network and the devices used, as well as the cost of load shedding. In addition, the impacts of the presence of DG on power flow and the modeling of the problem’s objective function are examined. To solve the problem of reconfiguration and placement of a multi-objective distribution feeder, a genetic algorithm (GA) and shuffled frog leaping algorithm (SFLA) hybrid algorithm is used. The proposed GA–SFLA algorithm is used to solve the problem with changes in its structure and its combination in three stages. Finally, the proposed method is implemented on the 33-bus distribution network. The simulation results show that the proposed method has an effective performance in improving the considered objective functions and by establishing a suitable fit between the different objective functions based on the three-dimensional Pareto front. Moreover, it introduced a more optimized architecture with lower loss, lower operating costs, and greater reliability compared to other optimization algorithms.

Multi-Objective Optimal Power Flow Calculation Considering Carbon Emission Intensity

In keeping with China’s dual carbon goals, optimal low-carbon power system dispatch has become a necessary component of the greening of the power system. However, typically, research considers only the economics of such efforts. Based on our power flow analysis of the power grid and the correlation properties of carbon emission flow, an optimal power flow calculation model targeting the total carbon emission rate of the power system’s power generation cost, active network loss, and load and network loss was constructed. Next, the NSGA-III algorithm was used to solve the model, and the decision was to coordinate and optimize the output schemes of various types of power plants, such as wind, water, and thermal. The modified IEEE39 node simulation system was built with Matlab software (MATLAB R2020b). The results of the calculation showed that, compared to the traditional method of determining the optimal power flow, the proposed method reduced the system carbon emissions by 20% while the power generation cost increased by less than 2%, which proves the effectiveness and practicability of the proposed method.

A Whole-Segment Carbon Measurement Approach for Energy Systems Incorporating Knowledge Mapping Techniques

Abstract As the current carbon measurement method makes it difficult to reflect the time-space variability of carbon emission factors, it leads to the inability to clarify the carbon emission responsibility of grid-side losses. In this paper, starting from the internal power system, based on the power system carbon emission flow analysis theory and knowledge graph technology, a trend-tracking analytical algorithm based on the node conductivity matrix operation is proposed to solve the complex power allocation relationship between each generating unit and each node load, and between each generating unit and the line network loss. On the basis of this algorithm, the carbon flow tracking model of the whole power system is established according to the conversion relationship between the current and the carbon flow, and the analytical expressions of the carbon emission distribution among the power generation side, the load side and the line loss are obtained by combining with matrix operation, so as to measure the carbon emission measurement results of the whole power system and to provide data references for the responsibility sharing of carbon emission. Finally, simulation verification is carried out based on the actual system operation and load data for C city. The total amount of carbon emissions from electricity consumption is 98.8 million tCO2 when C city is used as the minimum spatial scale, while the total amount of carbon emissions from electricity consumption is 67.9 million tCO2, 14.0 million tCO2, 16.9 million tCO2, and the total amount of the three regions is 98.8 million tCO2, which is consistent with that calculated when C city is used as the minimum spatial scale. The calculation results are consistent in real-time. The carbon measurement method proposed in this paper can obtain higher spatial resolution carbon measurement results for electricity consumption.

Снижение экономических потерь в контактной сети и воздушных линиях, электрифицированных на переменном токе

Purpose: To reduce economic losses in the contact network (CN) and overhead lines (OL) electrified with alternating current. The existing electromagnetic influences on the alternating current railway lead to significant economic losses. Additionally, there is a problem of the contact wire (CW) burnout during the passage of a neutral section with a raised pantograph under voltage on a high-speed railway line (HSRL). CW burnout leads to failure of the contact network, repair costs, possible damage to other people’s property and rolling stock. As a whole, all this represents significant economic losses. Methods: The analysis of material and time costs for the elimination of problems that arise due to the CW burnout or due to the use of manual labor during the installation of OL has been carried out, in comparison with the proposed solutions to reduce economic losses. Results: In solving the problem of the negative electromagnetic influence of the contact line (CL) on the OL, a mobile mechanized complex (MMC) is designed to help, which performs the transposition of the OL wires in 6 steps with scrolling by 60° each support. This method will allow us to place the OL wires equidistantly from the contact wire. Scrolling with once-per-support frequency will align the asymmetry of the linear electrical parameters. All in all, MMC performs installation in an automated mode. The problems of the CW burnout can be reduced by the insulating coupling device (ICD) of the CL. ICD CL is a set of step resistances, which are located at the outer sections of the phase wires before and after the neutral section. It is aimed at reducing the current to a safe value, which will not lead to the contact wire burnout, failure of the CN and/or rolling stock and possible damage to other people’s property. Practical significance: The result is a set of measures in the form of MMC to reduce economic losses in overhead lines from the influence of CN and ICD CL to reduce losses in the contact network by maintaining the operability of the contact wire.

A voltage control method for distribution networks based on TCN and MPGA under cloud edge collaborative architecture

A distribution network voltage control method based on TCN and MPGA under cloud edge collaborative architecture is proposed to address the issues of heavy computational burden and inability to balance economic efficiency in traditional decentralized and centralized voltage control methods. Firstly, under the cloud edge collaboration architecture, the power prediction model based on time convolutional networks is trained in the cloud, and the edge predicts the load demand and new energy active output in each region based on the TCN Attention power prediction model issued by the cloud. Then, considering safety and economy comprehensively, with the goal of minimizing the daily network loss and voltage regulation cost in the distribution network, the Pareto solution set for the optimal operation point of each voltage regulation equipment in the entire distribution network was solved based on an improved multi population genetic algorithm at the distribution cloud main station. Finally, the optimal distribution network voltage control scheme is obtained based on the Pareto solution set. Based on the simulation system, experimental testing was conducted on the proposed method, and the results showed that its daily voltage regulation cost and network loss were 863.4 yuan and 43.73 MW h, respectively. The average absolute percentage error of the prediction model was 4.51 %, which was superior to other comparative methods.

Research on line loss analysis and intelligent diagnosis of abnormal causes in distribution networks: artificial intelligence based method.

The primary source of energy losses in distribution networks (DNs) is rooted in line losses, which is crucial to conduct a thorough and reasonable examination of any unusual sources of line losses to guarantee the power supply in a timely and safe manner. In recent studies, identifying and analyzing abnormal line losses in DNs has been a widely and challenging research subject. This article investigates a key technology for the line loss analyses of DNs and intelligent diagnosis of abnormal causes by implementing artificial intelligence (AI), resulting in several prominent results. The proposed algorithm optimizes the parameters of the support vector machine (SVM) and suggests an intelligent diagnosis algorithm called the Improved Sparrow Search Algorithm and Support Vector Machine (ISSA-SVM). The ISSA-SVM algorithm is trained to calculate the data anomalies of line losses when changing loads and exhibiting exceptional performance to identify abnormal line losses. The accuracy of abnormality identification employing the ISSA-SVM algorithm reaches an impressive 98%, surpassing the performances of other available algorithms. Moreover, the practical performance of the proposed approach for analyzing large volumes of abnormal line loss data daily in DNs is also noteworthy. The ISSA-SVM accurately identifies the root causes of abnormal line losses and lowers the error in calculating abnormal line loss data. By combining different types of power operation data and creating a multidimensional feature traceability model, the study successfully determines the factors contributing to abnormal line losses. The relationship between transformers and voltage among various lines is determined by using the Pearson correlation, which provides valuable insights into the relationship between these variables and line losses. The algorithm's reliability and its potential to be applied to real-world scenarios bring an opportunity to improve the efficiency and safety of power supply systems. The ISSA that incorporates advanced techniques such as the Sobol sequence, golden sine algorithm, and Gaussian difference mutation appears to be a promising tool.

Optimizing Technical Losses of the PLN Distribution Network with Changes in Operational Patterns in 2023 at PLN ULP Lhokseumawe

Technical shrinkage is shrinkage caused by impedance in generation equipment or transmission equipment to the distribution network so that there is energy loss. There are several technical shrinkage problems at PT. Perusahaan Listrik Negara (PLN) especially in Customer Service Unit (ULP) Lhokseumawe City and for now the cause is still unsolved, because this technical shrinkage problem will harm and have an impact on consumers and PLN itself. The purpose of the study was to optimize technical shrinkage in the distribution network to reduce energy losses that occurred during the electrical power distribution process in the distribution network of PT. PLN (Persero) ULP Lhokseumawe City. This study uses the help of Electrical Transient Analyzer Program (ETAP) software to simulate the power flow so that the depreciation value that occurs is obtained, then re-load adjustment is carried out to simulate again to determine the change in the depreciation value obtained after load adjustment. The results of the ETAP simulation show the depreciation value that occurred before the load adjustment was made by 76.7 kW after the depreciation load adjustment was adjusted to 59.6 kW. This means that this technical shrinkage can be suppressed by changing the feeder operation pattern and voltage drop value in accordance with the limitation provisions set in SPLN 72:1987.

Gradient-enhanced physics-informed neural networks based on transfer learning for inverse problems of the variable coefficient differential equations

We propose gradient-enhanced PINNs based on transfer learning (TL-gPINNs) for inverse problems of the function coefficient discovery in order to overcome deficiency of the discrete characterization of the PDE loss in neural networks and improve accuracy of function feature description, which offers a new angle of view for gPINNs. The TL-gPINN algorithm is applied to infer the unknown variable coefficients of various forms (the polynomial, trigonometric function, hyperbolic function and fractional polynomial) and multiple variable coefficients simultaneously with abundant soliton solutions for the well-known variable coefficient nonlinear Schrödinger equation. Compared with the PINN and gPINN, TL-gPINN yields considerable improvement in accuracy. Moreover, our method leverages the advantage of the transfer learning technique, which can help to mitigate the problem of inefficiency caused by extra loss terms of the gradient. Numerical results fully demonstrate the effectiveness of the TL-gPINN method in significant accuracy enhancement, and it also outperforms gPINN in efficiency even when the training data was corrupted with different levels of noise or hyper-parameters of neural networks are arbitrarily changed.

Analysis of Reactive Power in Electrical Networks Supplying Nonlinear Fast-Varying Loads

This study concerns problems related to the assessment of reactive power in power networks with nonlinear fast-varying loads, such as electric arc furnaces, rolling mill drives, etc. The operation of this type of load is characterized by the introduction of interharmonic currents (including higher harmonics) into the power supply network and a relatively low power factor. Rapid changes in the RMS value of the current also cause voltage fluctuations and the related phenomenon of light-flickering. Therefore, there is a need to evaluate the power selection of compensating devices, taking into account the random nature of load changes and the distortion of current and voltage waveforms, in particular, interharmonic components, the impact of which has not been fully investigated so far. To analyze the random nature of load changes, autocorrelation functions were used, which allowed for the estimation of the expected values of the arc furnace current distortion coefficient (based on the recorded waveforms). In order to determine the parameters of reactive power compensating devices, and in particular capacitor banks, an autocorrelation function in the exponential-cosine-sine form was used, which meets the conditions of differentiation. This study contains comparative results of the reactive power of capacitor banks determined using different methods. The criterion for selecting capacitor bank parameters was the minimization of energy losses in the power supply network. The calculations presented in this study show that by taking into account higher harmonics and interharmonics in the voltage and currents of fast-varying loads, the installed power of the capacitor bank can be reduced by approximately 7%, and energy losses in the power grid can be reduced by 3–5%.

Evaluation of physical education teaching effect using Random Forest model under artificial intelligence

This work aims to optimize the physical education (PE) teaching effect based on deep learning (DL) to cultivate high-level college students better. Firstly, the present situation of college teachers' teaching ability is surveyed to realize the deficiencies in teaching. Secondly, an optimization algorithm is proposed to improve the node splitting mode. This algorithm can solve the problem of single and similar node splitting modes in the Random Forest (RF) algorithm. The independent node splitting method Iterative Dichotomiser 3 and Classification and Regression Tree in the algorithm are recombined, and new splitting rules are obtained through adaptive parameter selection. Finally, the scheme designed is tested. The results suggest: The results suggest: (1) During the training of the proposed algorithm, although the loss curve at 4550 and 6800 points has a small crest, the error of the network loss function shows a downward trend and tends to be flat; (2) Compared with unoptimized Genetic Algorithm (GA) and Genetic Algorithm-Back Propagation (GA-BP), the proposed algorithm shows better performance both in terms of time consumption and accuracy (time consumption is less than 5.4 ms, and accuracy is more than 95 %). In a word, using the GA-BP-RF algorithm proposed to improve the PE teaching effect is feasible. The proposed model provides ideas for applying DL technology to improve teachers' teaching abilities.

Reconstructing higher-resolution four-dimensional time-varying volumetric data

We have witnessed substantial growth in super-resolution research within the computer vision community. Unlike previous works that mainly focus on the super-resolution synthesis of images, videos, or single volumes, our research is dedicated to the super-resolution synthesis of time-varying volumetric data, which are generated from scientific simulations and are crucial for domain scientists to understand and analyse complex scientific phenomena. Compared to previous works, our research presents a greater challenge: the time-varying volumetric data have higher dimensions, making it more difficult to synthesise super-resolution that maintains good spatio-temporal consistency while achieving high visual quality. To tackle this challenge, we introduce a new GAN-based network called SSR-DoubleUNetGAN, which includes novel network architecture and loss functions, allowing for accurate synthesis of spatial super-resolution for time-varying volumetric data with relatively fast training time. Our method can be applied in the context of in-situ visualisation to aid domain scientists in analysing more time-varying volumetric data more efficiently. In addition, it can be used in the compression-decompression pipeline to recover the super-resolution time-varying volumetric data from their low-resolution counterpart. To demonstrate its effectiveness, we applied various time-varying volumetric datasets from different scientific simulations to it. To demonstrate its advantages, we compared it qualitatively and quantitatively with five state-of-the-art super-resolution techniques, namely SSR-TVD, Tricubic, SRResNet, Cubic, and Linear. Furthermore, we conducted an ablation study to validate its important modules. The experimental results show that our method outperforms the compared state-of-the-art techniques.