Line Losses Research Articles

Superhydrophobic and Thermally Conductive Coating for Restraining Corona Loss and Audible Noise of High-Voltage Transmission Lines

In recent years, the number of high-voltage transmission lines has sharply increased with the rapid development of modern industry. However, a corona discharge phenomenon often occurs on the exposed high-voltage transmission lines, leading to energy loss and noise pollution. Herein, we have proposed a facile spraying method to prepare a superhydrophobic and thermally conductive coating to restrain the corona discharge phenomenon of high-voltage transmission lines, with vinyl silicone oil and hydrogen silicone oil as the main materials and modified boron nitride (BN) as a thermal conductive filler. The obtained composite coating exhibited superhydrophobicity, with a high water contact angle of 162°. In addition, the coating also showed a good self-cleaning capability, non-adhesion capability, mechanical stability, and chemical stability. Owing to the construction of the thermally conductive pathways with BN, the thermal conductivity of the coating reached 1.05 W/m·K, which was beneficial to quickly dissipating the heat generated by the current heating effect. Moreover, the corona losses of the positive and negative electrodes under simulated rainy conditions were decreased by 7.43% and 8.05%, respectively. The findings of our work have provided a new strategy to effectively restrain the corona discharge phenomenon of transmission lines, showing great application potential in the field of high-voltage power networks.

A simple, rapid fluorescent reporter-based method for detection of ectopic cre recombinase expression in presumed retinal cell type-targeted mouse lines

Although cell type-specific Cre recombinase-expressing mouse lines are commonly used to generate conditional knockout of genes of interest, germline recombination and ectopic “leakiness” in Cre recombinase expression in non-specific cell types has been observed in several neuronal and glial-specific Cre lines. This often leads to inadvertent loss of conditional mouse lines, requiring rederivation. It is therefore imperative to be able to monitor and validate cell type-specific Cre recombinase-mediated gene editing. Herein, we describe a simple, inexpensive, rapid ZsGreen fluor-reporter-based strategy for genotype-free identification of ectopic leakiness using a custom-designed, 3-D blue LED light box. We assessed cell type-specific expression in several allegedly specific Cre recombinase mouse lines commonly used in vision research: retinal pigment epithelium (RPE)-specific (VMD2 (Best1) Cre, RPE65 Cre); astrocyte-specific (GFAP Cre); as well as photoreceptor-bipolar progenitor cell-specific (CRX Cre). Our standardized workflow allows facile, rapid identification of ectopic and non-specific Cre recombinase expression in any presume specific Cre mouse line, without the need for genotyping and without causing animal distress.

Robust coordination of overcurrent relays for several simultaneous faults in wind power generation systems

This work proposes a new robust coordination procedure for the coordination of overcurrent relays that takes into account the specific requirements and conditions that arise in wind power generation plants. It is discussed here that, in those systems, coordination schemes that guarantee that protection coordination is maintained in the face of the simultaneous loss of several lines in the network can be of great importance, although this characteristic is not considered relevant in the usual transmission and distribution systems. Several new features have been incorporated in the proposed procedure, for achieving the computational efficiency that is necessary for performing the proposed task. A new mathematical model is presented, considering the characteristic curves of relays as design variables and including new constraints/penalties related to the maximum desirable relay actuation time. The proposed tool integrates a Differential Evolution algorithm with Linear Programming and a new Discrete Feasibility Search operator to minimize the sum of relay actuation times in scenarios for multiple fault conditions and to maximize robustness of the solution under scenarios with changes in network topology. The proposed robust coordination procedure with those features constitutes the first coordination algorithm designed for dealing with the reconfigured topology after the simultaneous loss of multiple lines in a network. A set of Pareto-optimal solutions is delivered, allowing the designer to perform a trade-off analysis between smaller actuation times and smaller disconnected areas after the occurrence of a fault. In the specific case of wind power generation plants, those robust solutions allow a more efficient use of resources allocated to network maintenance. A case study of the application of the proposed methodology to the protection of a real wind farm is presented, showing that the proposed procedure allows a reduction of 44.9% of the non-delivered energy in the case of the simultaneous occurrence of two faults, and of 51.9% of the non-delivered energy in the case of three simultaneous faults.

ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ РОЗПОДІЛЬНИХ МЕРЕЖ РАЙОНІВ МАЛОПОВЕРХОВОЇ ЗАБУДОВИ МІСТ

The article analyzes the methods of estimating the parameters and regimes of electrical distribution networks in areas of low-rise urban development. A method for optimizing the parameters of distribution networks of 0.38 kV under conditions of uncertainty of the initial information is proposed. It is shown that one of the unsolved problems of calculating the parameters of distribution electric networks of cities is the determination of the estimated load of utility consumers. Its dependence on the characteristics of consumers and their number connected to the lines of city transformer substations causes the low accuracy of calculations. In this case, the error of calculating the transformer substations load can reach 10-25%, and the error of calculating the cross-sections of 0.38 kV lines reaches 40%. In the assumption of the constancy of the total length of the lines of urban electric networks and the invariance of the distance between loads, calculation formulas for linear moments and load distribution coefficients are proposed, and based on them, models of intersection and power losses in lines are developed. Taking into account the developed models, we offer discounted cost models for 0.38 kV networks and transformer substations when calculating economic current density and allowable voltage costs. Keywords: optimization, distribution networks, uncertainty, efficiency, low-rise building, scheme.

Integrated Reactive Power Optimisation for Power Grids Containing Large-Scale Wind Power Based on Improved HHO Algorithm

Large-scale wind power grid integration will greatly change the system current distribution, making it difficult for the reactive power regulator to adjust to the optimal state. In this paper, an integrated reactive power optimisation method based on the improved Harris Hawk (HHO) algorithm is proposed. Firstly, a reactive power regulation model is constructed to solve the reactive power regulation interval of wind turbines, and the reactive power margin of wind turbines is used to participate in the system’s reactive power optimisation. Finally, a reactive power compensation capacity allocation optimisation model considering nodal voltage deviation, line loss and equipment investment cost, is established, and a reactive power optimisation scheme is obtained using the Harris Hawk optimisation algorithm on the basis of considering the constraints of the wind turbine reactive power output interval. The improved HHO algorithm is used to solve the reactive power optimisation scheme considering the constraints of tidal power, machine end voltage, a conventional generator and wind farm reactive power. In the simulation, the effects of the improved Harris Hawk optimisation algorithm and the particle swarm optimisation algorithm are compared, and the experimental results prove that compared to the particle swarm algorithm, the optimisation result of the improved Harris Hawk optimisation algorithm reduces the average loss of the system by 42.6% and reduces the average voltage deviation by 30.3%, which confirms that the improved Harris Hawk intelligent optimisation algorithm is effective in proving its superiority and solving the multi-objective model for reactive power optimisation.

Water consumption of electric power system in China: from electricity generation to consumption.

Understanding the water requirement of electricity generation is critical to the development of both electricity and water systems, while the water consumption of the whole electric power system remains unrevealed. Here, we examine the water consumption driven by electricity generation, transmission, and consumption in China, finding that 14 billion m3 of freshwater is consumed by electricity generation in 2019 and that 2.5 billion m3 of freshwater was virtually transferred via electricity transmission. Nationally, the freshwater consumption per unit of electricity generation was 1.9 m3/MWh. Based on the state-of-the-art electricity transmission data, we find that 59% of the transported electric power was from water-scarce provinces and 0.7 billion m3 of freshwater was lost due to the electricity loss in transmission lines in China. It is essential to link water resources with the whole electric power system (production, transmission, and consumption) rather than only part of the power system as in previous research.

ПОКАЗНИКИ ЯКОСТІ ЕЛЕКТРОЕНЕРГІЇ ТА ЇХНЄ ПОЛІПШЕННЯ АКТИВНИМИ ФІЛЬТРАМИ

This article discusses power quality and the role of active filters in improving power quality. The characteristic of this article discusses power quality and the role of active filters in improving power quality. Power quality characteristics, including voltage non-sinusoidality, voltage unbalance, input current harmonic distortion and the ratio of active to full power, are considered. Various technical means of compensating reactive power and improving current and voltage spectra, including active filters, and current standards governing their requirements are considered. The benefits of active filters, such as reduced energy losses, suppression of higher harmonics, power factor correction and flicker re-duction, are described. A correct definition of apparent power is presented to justify an energy efficient control strategy for a shunt active filter to minimize power losses in the transmission line. To facilitate the distributed compensation of unbalance power, the corresponding current vector is represented by four orthogonal components, which produce separate contributions to the transmission line loss power. The use of active filters in power systems can improve power quality and ensure efficient use of electrical equipment. Ref. 14. Keywords: power quality, efficiency, technical means, active harmonic filters, nonlinear load.

Diffuse retinal dysfunction following immune reconstitution uveitis in patients with prior cytomegalovirus retinitis: a novel observation.

To report continuing diffuse retinal dysfunction following resolution of immune reconstitution uveitis (IRU) in patients with cytomegalovirus retinitis (CMVR). Retrospective case series describing two patients with IRU following CMVR who underwent serial fundus photography and macular optical coherence tomography. One patient had serial electrophysiology. Both patients had CMVR successfully treated with antiviral medication. The affected eyes later developed IRU that resolved with steroids. However, following resolution, chronic retinal damage was evidenced by ellipsoid line loss in one case and gradual optic disc cupping in the other. Electrophysiology in both cases revealed generalized retinal dysfunction worse in the eye with more severe IRU and demonstrated objectively the efficacy of treatment intervention in the patient with serial recordings. Patients with IRU following CMV retinitis may have continuing diffuse retinal dysfunction despite apparent recovery and normal visual acuity. An aggressive approach to inflammation control may be warranted in such patients.

Application of Data Mining Algorithms in Power Marketing Predictive Analytics

Abstract The electric power industry has accumulated a large amount of historical data, and the analysis based on data mining can provide an effective reference for the electric power marketing of enterprises. In this paper, according to the analysis architecture of electric power marketing and its functional modules, the electric power marketing analysis system based on Bayesian algorithm is constructed through data extraction and transformation, modeling of Bayesian network, and simulation operation. At the same time, for the shortcomings of Bayesian algorithm with the large error of classification results under the condition of strong sample correlation, mutual information is introduced to modify the number of Laplace smoothing. The MI-NB model predicts the probability of electricity risk for company A to be 60% and the probability of electricity risk for company B to be 40%, with an error rate of 9.65% and 8.37%, respectively. In line loss rate analysis, the MI-NB model predicts an average line loss rate of 60.46% for station 1 and 60.43% for station 2, both in the high line loss rate range. The Bayesian algorithm based on mutual information can improve the practicality and intelligence of the power marketing decision analysis system, which makes the decision management of power supply enterprises more scientific and reasonable, and is of great practical significance in reducing the operational risk of enterprises.

Performance Analysis of Marine-Predator-Algorithm-Based Optimum PI Controller with Unified Power Flow Controller for Loss Reduction in Wind–Solar Integrated System

Transmission line losses are a crucial and essential issue in stable power system operation. Numerous methodologies and techniques prevail for minimizing losses. Subsequently, Flexible Alternating Current Transmission Systems (FACTSs) efficiently reduce transmission losses, and the Unified Power Flow Controller (UPFC) is a reactive power compensation controller. The parameter strength of the proportional–integral (PI) controller was calibrated with the Marine Predator Algorithm (MPA), a recent metaheuristic algorithm. An MPA-based optimum PI controller with a UPFC evaluates the optimal location of the UPFC and PI controller parameters to accomplish the desired research objective. The power rating of the UPFC was determined depending on the voltage collapse rating and power loss and an evaluated performance analysis of the MPA–PI-controlled UPFC on a modified IEEE-30 bus transmission network in MATLAB Simulink code. The Newton–Raphson method was used to perform the load flow analysis. Hence, the proposed MPA–PI controller was examined in contrast to preferred heuristic algorithms, the Artificial Bee Colony (ABC) and Moth Flame Optimization algorithms (MFO); the results showed that the MPA–PI controller exhibited better performance with an improved voltage profile and surpasses active power losses with the optimal placement of the UPFC device under different loading conditions. The active power loss, considering a UPFC with the proposed algorithm, reduced from 0.0622 p.u to 0.0301 p.u; consequently, the voltage profile was improved in the respective buses, and the loss percentage reduction during a 100% base load was 68.39%, which was comparatively better than the ABC and MFO algorithms.

Use of super resolution reconstruction MRI for surgical planning in Placenta accreta spectrum disorder: Case series

IntroductionComprehensive imaging using ultrasound and MRI of placenta accreta spectrum (PAS) aims to prevent catastrophic haemorrhage and maternal death. Standard MRI of the placenta is limited by between-slice motion which can be mitigated by super-resolution reconstruction (SRR) MRI. We applied SRR in suspected PAS cases to determine its ability to enhance anatomical placental assessment and predict adverse maternal outcome. MethodsSuspected PAS patients (n = 22) underwent MRI at a gestational age (weeks + days) of (32+3±3+2, range (27+1-38+6)). SRR of the placental-myometrial-bladder interface involving rigid motion correction of acquired MRI slices combined with robust outlier detection to reconstruct an isotropic high-resolution volume, was achieved in twelve. 2D MRI or SRR images alone, and paired data were assessed by four radiologists in three review rounds. All radiologists were blinded to results of the ultrasound, original MR image reports, case outcomes, and PAS diagnosis. A Random Forest Classification model was used to highlight the most predictive pathological MRI markers for major obstetric haemorrhage (MOH), bladder adherence (BA), and placental attachment depth (PAD). ResultsAt delivery, four patients had placenta praevia with no abnormal attachment, two were clinically diagnosed with PAS, and six had histopathological PAS confirmation. Pathological MRI markers (T2-dark intraplacental bands, and loss of retroplacental T2-hypointense line) predicting MOH were more visible using SRR imaging (accuracy 0.73), in comparison to 2D MRI or paired imaging. Bladder wall interruption, predicting BA, was only easily detected by paired imaging (accuracy 0.72). Better detection of certain pathological markers predicting PAD was found using 2D MRI (placental bulge and myometrial thinning (accuracy 0.81)), and SRR (loss of retroplacental T2-hypointense line (accuracy 0.82)). DiscussionThe addition of SRR to 2D MRI potentially improved anatomical assessment of certain pathological MRI markers of abnormal placentation that predict maternal morbidity which may benefit surgical planning.

Barriers to Adherence of Early Mobilization Protocols in the Pediatric Intensive Care Units

AbstractChildren who survive critical illness suffer many sequelae of prolonged hospitalization. National guidelines recommend pediatric intensive care units (PICUs) employ ICU care bundles to combat acquired delirium, pain, and weakness. While the use of early mobility (EM) protocols has increased in PICUs, there remain challenges with adherence. The aim of this study is to better understand perceived barriers to EM in the PICU before and after introducing an EM protocol. We hypothesized that providers would be most concerned about the safety of EM. This pre–post-survey study was conducted at a single-center tertiary PICU. A total of 94 PICU providers were included in this study, including nurses, physicians, and therapists. Responses were collected anonymously. Survey respondents consented to participation. The initial survey was conducted prior to enacting an EM protocol to gauge knowledge and opinions surrounding EM. Based on the results, education regarding EM was performed by a multidisciplinary team. An EM protocol “Move Jr.” was initiated. Four months postinitiation, a follow-up survey was sent to the same cohort of providers to determine knowledge of the protocol, changes in opinions, as well as barriers to the implementation of EM. While providers believed that EM was beneficial for patients and were interested in implementing an EM protocol, the initial top three perceived barriers to EM were risk of inadvertent extubation, risk of inadvertent loss of central lines, and time constraints. Four months after the initiation of the EM protocol, a follow-up survey revealed that the top three perceived barriers of EM had changed to time constraints, increased workload, and level of sedation. After 4 months, the change in perceived barriers suggests greater acceptance of the safety of EM but challenges in application. Survey responses describe a desire to perform EM exercises but difficulty finding time. Understanding of the protocol also differed among providers. Greater collaboration among providers could lead to more cohesive therapy plans. There was a clear benefit in educating providers to consider EM as a priority in patient care.

On the Distributed Energy Storage Investment and Operations

Problem definition: Energy storage has become an indispensable part of power distribution systems, necessitating prudent investment decisions. We analyze an energy storage facility location problem and compare the benefits of centralized storage (adjacent to a central energy generation site) versus distributed storage (localized at demand sites). This problem encompasses optimizing storage capacities across all locations, with the objective of minimizing the total storage investment and energy generation costs. Methodology/results: We employ a stylized model that captures essential features of an energy distribution system, including convex costs, stochastic demand, storage efficiency, and line losses. Using dynamic programming, we optimize storage operations and derive value function properties that are key to analyzing the storage investment decisions. We discern fundamental differences between centralization/localization decisions at the capacity investment stage and the centralization/localization decisions at the storage operations level. Operationally, centrally stored energy offers more flexibility, which is consistent with the conventional understanding of inventory pooling. However, we find that localized storage often emerges as the preferred option at the investment stage under various circumstances. Managerial implications: Storage investment should first be made at the demand locations with positive minimum demand regardless of the level of demand variability. Subsequent storage investment should consider the tradeoffs between centralized versus localized investment. Operationally, the relative magnitudes of storage and line losses drive different optimal storage policies. Despite the differences, these policies are guided by common principles such as pooling inventory and balancing local storage levels. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2020.0652 .

Steady state Contingency Analysis of IEEE-39 Bus system Using PSS/E

Nowadays the stable, secure and reliable power system operation is the main objective of anyelectric power utility. The Power system is vulnerable to any contingency i.e. loss of Transmission line,Transformer, Generating unit etc. The power system control engineer should have detail analysis of at leastthe most probable and sever contingencies of the system and accordingly devise the remedial action plan.Thus, Power system contingency analysis and evaluation play vital role in ensuring power system stabilityand reliability. Keeping in view the importance of contingency analysis in power system operation andcontrol, this research focuses on steady state Contingency analysis of IEEE-39 bus system using newtonRaphson method. PSS/E is used for the analysis, different contingencies are evaluated and ranked basedupon the Power flow (Overloading), Voltage and phase angle deviation. Once the ranking is performed, thePV and QV analysis is performed for most severe contingencies. Remedial Actions is proposed for themost severe contingencies and this research could be applied to any real time network.

Deep learning techniques for transmission line fault classification – A comparative study

Despite advancements in technology, power system faults leading to electric power interruption remain a significant issue. Efficient restoration of the power system relies on the swift classification and clearance of faults. Among the various types of faults in transmission lines, open circuit and short circuit faults are commonly encountered. This study specifically focuses on the analysis of five types of short circuit faults: line-to-line, line-to-ground, double line-to-ground, triple line, and triple line-to-ground faults. Faults can cause both power failure and power loss in transmission lines. Once a fault occurs, it is crucial to restore electricity supply promptly to prevent further losses. Therefore, the development of a system capable of accurately and swiftly detecting and removing faults is essential. Traditionally, categorizing transmission line faults required sophisticated mathematical modeling, intricate signal processing techniques, and expert knowledge to interpret the output signals. In this paper, an alternative approach is proposed, utilizing deep learning techniques for transmission line fault classification. Specifically, the paper employs techniques such as artificial neural network (ANN), long short-term memory (LSTM), with and without window regression (WR). By implementing these deep learning techniques, automatic feature extraction and signal processing are achieved, streamlining the fault classification process. The experimental results obtained from this study demonstrate an accuracy of 42.98% for ANN, 99.98% for LSTM, and 99.99% for LSTM-WR. These outcomes underscore the effectiveness of the deep learning techniques employed in accurately classifying transmission line faults, with LSTM and LSTM-WR outperforming ANN in terms of accuracy.

Application of Comprehensive Evaluation of Line Loss Lean Management Based on Big-Data-Driven Paradigm

Effective line loss management necessitates a model-driven evaluation method to assess its efficiency level thoroughly. This paper introduces a “model-driven + data-driven” approach based on collective intelligence theory to address the limitations of individual evaluation methods in conventional line loss assessments. Initially, eight different evaluation methods are used to form collective intelligence to evaluate the line loss management of power grid enterprises and generate a comprehensive dataset. Then, the data set is trained and evaluated using the random forest algorithm, with Spearman rank correlation coefficient as the test metric, to assess the power grid enterprise’s line loss management level. Combining model-driven and data-driven methods, this integrated approach efficiently leverages the informational value of indicator data while thoroughly considering the causal and associative attributes within the dataset. Based on data from 61 municipal grid enterprises, both the comparison of multiple AI methods and correlation tests of results verify the superiority of the proposed method.

Congestion Management Using an Optimized Deep Convolution Neural Network in Deregulated Environment

The technical issue of congestion, which is predominantly found in deregulated power systems, is caused by the failure of transmission networks to satisfy load power demands. This failure is primarily caused due to an increase in loads or loss of transmission lines or generators in modern restructured power networks. This work introduces a CM approach using Deep Convolution Neural Network (DCNN) for minimizing congestion and supporting Independent System Operators (ISOs). The purpose of the work is to generate enhanced prediction outputs for congestion management with reduced error values. These objectives were achieved through the actual power rescheduling of generators. The proposed work adopts DCNN which is optimized using an Improved Lion Algorithm (LA) and aids in providing significant outcomes for congestion management with reduced error. By implementing customized IEEE 57-bus, IEEE 30-bus, and IEEE 118-bus test systems, the suggested approach has been successfully verified for its performance on test systems of varied sizes. This analysis incorporates restrictions such as line loads, bus voltage influence, generator, line limits, etc. The most important results for the test system indicating convergence profile, congestion cost, and change in real-power and voltage magnitude are obtained by the simulation in MATLAB, and on the basis of the obtained simulation outcomes, it is evident that the proposed Improved Lion Algorithm optimized Deep Convolution Neural Network displays phenomenal computation performance in minimizing congestion losses at minimum congestion costs. When compared to several contemporary optimization techniques, the suggested technique performs better in terms of congestion cost and losses by generating improved prediction outputs with reduced errors.

Voltage Management of Distributed Photovoltaic Cluster Connected to a Distribution Network

The proportion of distributed photovoltaic grid connection in the distribution network is gradually increasing, and its characteristics of high volatility and poor stability have brought new challenges to the overvoltage problem faced by the distribution network structure and safe and stable optimization operation. This paper mainly studies clustering and reactive power optimization control of distributed Photovoltaic. The system architecture of the cloud edge is constructed, and the voltage autonomy strategy within the region after partition is proposed. The model is established with the total voltage offset and the minimum line loss as targets. Adopting adaptive PSO-BP algorithm model for optimization solution. Finally, conduct simulation analysis on the MATLAB platform. We can see that the proposed method can not only reduce the uneven voltage distribution, network loss, but also reduce the calculation burden of the controller has a positive effect.

Achieving an Optimal Decision for the Joint Planning of Renewable Power Supply and Energy Storage for Offshore Oil–Gas Platforms

To address the complexity of siting and sizing for the renewable energy and energy storage (ES) of offshore oil–gas platforms, as well as to enhance the utilization of renewable energy and to ensure the power-flow stability of offshore oil–gas platforms, this paper proposes a hierarchical clustering-and-planning method for wind turbine (WT)/photovoltaic (PV) ES. The proposed strategy consists of three stages. First, the WT/PV power generation is forecast by a LightGBM model. The WT/PV siting and sizing at each node of the distribution network is optimized with a particle swarm optimization (PSO) algorithm, with the objectives of economy and stability. In the second stage, the distribution network is partitioned into sub-clusters, based on a voltage and loss-sensitivity index. Finally, the ES siting and sizing is optimized with PSO to minimize the line loss and the voltage fluctuation for each sub-cluster. The relationship between the economic and stability indicators is conducted quantitatively in the joint-planning approach. Considering the 10 kV distribution network of an oil–gas platform in the Bohai Sea of China as an example, our experiments demonstrated that by adjusting the WT/PV ES capacity for different gas-turbine power outputs, line losses can be reduced by 55–66% and voltage fluctuations can be reduced by 30.4–47.5%.

Carbon Market Trading Mechanisms Considering Multi-Layer Reactive Power Compensation

In the context of achieving carbon peaking and carbon neutrality goals, focusing on coordinated efficiency in loss and carbon reduction, and promoting comprehensive green transformation of economic and social development are critical strategies. Line loss is an economic and technical indicator for measuring losses in a power system, and loss reduction is one of the important ways to achieve the carbon peaking and carbon neutrality goals in the power system. However, with the continuous increase in the power grid scale and the increasingly complex operation mode of the system, it is difficult to clearly quantify the carbon reduction benefits brought by system loss reduction. In order to synergize grid loss reduction and system carbon reduction, and generate economic and environmental benefits at the same time, this paper proposes a carbon market trading model that considers multi-layer reactive power compensation strategies. Based on the carbon emission flow model, a node carbon cost pricing is formed, and multi-layer reactive power compensation measures are set in the distribution network nodes to obtain an optimal loss reduction strategy, with the carbon market trading cost minimization as the objective. The effectiveness of the model is verified by simulating and analyzing four scenarios. Compared with the original system that does not consider carbon trading and reactive compensation, the model proposed in this paper can reduce losses by 20% and reduce carbon emissions by 5.7%. This paper is of great value for reactive power loss reduction management in distribution networks of a low-carbon background.