Distribution Network Losses Research Articles

Hierarchical Optimal Dispatching of Electric Vehicles Based on Photovoltaic-Storage Charging Stations

Electric vehicles, known for their eco-friendliness and rechargeable–dischargeable capabilities, can serve as energy storage batteries to support the operation of the microgrid in certain scenarios. Therefore, photovoltaic-storage electric vehicle charging stations have emerged as an important solution to address the challenges posed by energy interconnection networks. However, electric vehicle charging loads exhibit notable randomness, potentially altering load characteristics during certain periods and posing challenges to the stable operation of microgrids. To address this challenge, this paper proposes a hierarchical optimal dispatching strategy based on photovoltaic-storage charging stations. The strategy utilizes a dynamic electricity pricing model and the adaptive particle swarm optimization algorithm to effectively manage electric vehicle charging loads. By decomposing the dispatching task into multiple layers, the strategy effectively solves the problems of the “curse of dimensionality” and slow convergence associated with large numbers of electric vehicles. Simulation results demonstrate that the strategy can effectively achieve peak shaving and valley filling, reducing the load variance of the microgrid by 24.93%, and significantly reduce electric vehicle charging costs and distribution network losses, with a reduction of 92.29% in electric vehicle charging costs and 32.28% in microgrid losses compared to unorganized charging. Additionally, this strategy can meet the travel demands of electric vehicle owners while providing convenient charging services.

Power flow analysis and volt/var control strategy of the active distribution network based on data-driven method

In order to solve the problems of low power flow calculation accuracy and voltage overcrossing of the distribution network caused by large-scale distributed power supply, a data-driven power flow analysis and volt/var optimization control strategy for distribution network are proposed in this paper. Firstly, a data-driven power flow analysis model for the distribution network is proposed, and the nonlinear mapping relationship between the distribution network state and power flow results is described from the data-driven perspective. Secondly, the paper analyzes the influence of photovoltaic power supply on the voltage of the distribution network, and establishes the volt/var optimization model based on photovoltaic power supply. Then, on the basis of data-driven power flow analysis of the distribution network, the distribution network reactive voltage optimization strategy based on data-driven power flow analysis is proposed to ensure the safe and stable operation of distribution network voltage, reduce the operating network loss of distribution network, and realize the distribution network reactive voltage optimization is not affected by factors such as distribution network line parameters. Finally, IEEE 33 node power distribution system is used to verify the effectiveness of the proposed strategy.

Design of ant colony planning algorithms for multi-stage grids of distribution networks considering network resilience

Network Destruction Resilience is the ability of a system to maintain good operation in the event of an external attack or internal failure. The resilience of distribution network is crucial to guarantee the reliability of power supply. In this paper, we design a planning algorithm considering network destructibility for the multi-stage ant colony planning problem of distribution networks. The method establishes the multi-stage network planning objective function of distribution network from the perspectives of total investment cost and annual operation cost of multi-node network frame, destruction resistance, active power and reactive network loss of distribution network, etc. Then based on the constraints of the model, the improved ant colony algorithm is used to solve the multi-stage network planning objective function of distribution network, and the results of the anti-termite colony planning of multi-stage network frame of distribution network are obtained. In order to verify the effectiveness of the algorithm, simulation experiments are carried out on real distribution network data. The results show that the proposed ant colony planning algorithm for multi-stage grid frames can effectively improve the destruction resistance of distribution networks, and reduce the total investment cost and annual operation cost of multi-stage grid frames, and reduce the network loss rate data of multi-stage grid frames of distribution networks after application. It provides an effective method for planning the distribution network.

Optimizing peak-shaving cooperation among electric vehicle charging stations: A two-tier optimal dispatch strategy considering load demand response potential

The increase in the grid connection of electric vehicles (EVs) provides great potential for peak load regulation and valley filling of the grid. In order to solve the challenges brought by the integration of new energy vehicles into the power grid and give full play to the potential of EV demand response, this paper proposes a two-layer optimal dispatch strategy for the “distribution network-charging station” system. First, assess EV load demand response potential. The research area is divided into different functional areas by using the data of urban interest points, and the travel habits of users are analyzed by using EV driving data. On this basis, considering the influencing factors such as EV battery capacity (SOC), charging electricity price and spatial characteristics, the EV load response potential evaluation model is constructed by integrating user electricity price response and charging power response. Secondly, taking the evaluation value of EV response potential as the range of load adjustment, in order to optimizing peak-shaving cooperation among EV charging stations and obtaining the peak-shaving measures of each charging station, a “distribution network-charging station” double-layer optimal dispatch is carried out. The upper-level distribution network scheduling aims at minimizing the unfinished peak shaving rate and distribution network loss, and optimizes the scheduling power allocation of the peak tasks of each charging station. The lower-level charging station scheduling is based on the principle of maximizing user charging satisfaction and charging station economic benefits, and completes the peak-shaving scheduling determined by the superior. Finally, the strategy is applied in a specific area of Shaanxi Province, and the scheduling results show that the strategy is more economically feasible.

Reducing non-technical losses in electricity distribution networks: Leveraging explainable AI and three lines of defence model to manage operational staff-related factors

This study presents a multidisciplinary approach involving Explainable Artificial Intelligence (ExAI) and operational risk management to reduce Non-Technical Losses (NTL) in electricity distribution. It empirically explores how the activities of employees of utility companies contribute to NTL, a phenomenon often overlooked in existing empirical research. An ensemble classification algorithm is used to analyse utility operations data, and the SHAP explainability technique establishes the predictive significance of staff activities for NTL. Subsequently, these staff activities are mapped into risk cells using the BASEL II and III operational risk definitions, and the Three Lines of Defence (3LoD) model is developed for optimizing electricity distribution. The paper makes three original contributions to the literature: first, it empirically links staff operations to NTL; second, it maps NTL causes to Basel II/III operational risk categories; and finally, to the best of the authors’ knowledge, it is the first study to use the 3LoD model for electricity distribution optimization.

Distribution network line loss analysis method based on improved clustering algorithm and isolated forest algorithm

The long-term loss of distribution network in the process of distribution network development is caused by the backward management mode of distribution network. The traditional analysis and calculation methods of distribution network loss can not adapt to the current development environment of distribution network. To improve the accuracy of filling missing values in power load data, particle swarm optimization algorithm is proposed to optimize the clustering center of the clustering algorithm. Furthermore, the original isolated forest anomaly recognition algorithm can be used to detect outliers in the load data, and the coefficient of variation of the load data is used to improve the recognition accuracy of the algorithm. Finally, this paper introduces a breadth-first-based method for calculating line loss in the context of big data. An example is provided using the distribution network system of Yuxi City in Yunnan Province, and a simulation experiment is carried out. And the findings revealed that the error of the enhanced fuzzy C-mean clustering algorithm was on average − 6.35, with a standard deviation of 4.015 in the situation of partially missing data. The area under the characteristic curve of the improved isolated forest algorithm subjects in the case of the abnormal sample fuzzy situation was 0.8586, with the smallest decrease, based on the coefficient of variation, and through the refinement of the analysis, it was discovered that the feeder line loss rate is 7.62%. It is confirmed that the suggested technique can carry out distribution network line loss analysis fast and accurately and can serve as a guide for managing distribution network line loss.

Estimating and Reducing Leakages in the Water Distribution Networks of Small Settlements: The Case of Agios Germanos in the Prespes Municipality

Pressure management is a fundamental and highly effective method for the management of real losses in water distribution networks and therefore reducing non-revenue water. In this work, a methodology is developed to assess leakages in the water distribution networks of small settlements. The settlement of Agios Germanos in the Municipality of Prespes is selected as a representative case study. The hydraulic modeling of the water distribution network in the study area is used to assess the hydraulic behavior of the existing infrastructure in its current state of operation and to find critical locations to install the necessary measuring equipment (pressure sensors, flow meters, water level sensors, and pressure reducing valves). This equipment is used to calibrate the hydraulic model, estimate leakages, and manage them effectively. Minimum night flow analysis is utilized to assess leakages in the studied network based on measurements of the hydraulic parameters from the equipment installed. The effects of pressure management on leakages are then examined by assessing the relationship between the pressure and leak flow rate in the selected settlement.

Calculation of electrical energy losses in distribution networks for power supply using oil and vacuum circuit breakers

A method for approximate estimation of load losses in distribution networks from 6 to 10 kV, as well as for lower voltage networks, has been proposed. The main advantage of this method is that the root mean square current is calculated only once for a series of calculations. This approach significantly reduces the time required for calculations and improves their accuracy, as it eliminates the need for multiple determinations of the root mean square current for each individual calculation. The proposed approach contributes to a better understanding of the influence of various factors on load losses, which allows for more effective planning and management of distribution networks. This is particularly important in the context of increasing loads on electrical networks and higher demands for their reliability and efficiency. In the calculation of electrical energy losses in distribution networks from 6 to 10 kV, methods that use generalized coefficients of electrical circuits and operating modes in the form of regression equations are widely applied. The most significant and independent factors, such as the input of active energy, total line length, number of line sections, total number of transformers, and their total installed capacity, must be taken into account and are included in the calculation equation. Additionally, the components used and their regular maintenance play an important role in electrical energy losses in electrical networks. The main direction of research is the optimization of distribution network parameters, taking into account the latest technologies and materials, as well as the implementation of automated monitoring and control systems that will allow timely detection and elimination of problems associated with electrical energy losses. The proposed method for estimating load losses in distribution networks is a promising direction for the development of the energy sector, which will contribute to improving the efficiency and reliability of electrical networks, as well as reducing the costs of their operation and maintenance.

Analysis of power quality and additional loss in distribution network with distributed generation

As the penetration of distributed generation in the power grid, the power quality issues in the active distribution network are becoming increasingly diversified, hence followed with complex changes of the additional loss. The article investigates the additional loss caused by disorder power flow and power quality disturbances in the active distribution network. Firstly, the active distribution network model is established based on typical parameters. The impact of the access of distributed generation on power quality issues, including three-phase imbalance, harmonic distortion and voltage deviation and rise, is analyzed. Then, the influence of different power quality disturbances on additional loss with distributed generation is quantitatively explored. By introducing the concept of harmonic three-phase imbalance, the article analyzes the variation characteristics of network loss under the disturbances of composite power quality issues, and introduces the concept of a loss reduction interval, whose size is related to the average current. Finally, the validity of the analysis is verified through simulation. This article provides a new approach for loss analysis in distribution network containing distributed generation, which is beneficial for identifying and minimizing network loss in distribution networks.

Research on the minimum network loss optimization control strategy of the active distribution network based on the distributed power flow controller

With large-scale access to distributed new energy, the power flow form of the distribution network tends to be complex. The distribution loop network rate increases, resulting in serious circulation of the distribution network, which places significant pressure on the loss of the distribution network. The problem of network loss change caused by distributed new energy grid connection, coupled with the inconsistency between load fluctuation and output power change of distributed new energy, will have a significant impact on the safety, reliability, and economic operation of the distribution network. Therefore, it is urgent to have good economic and technical means of power flow control. This paper proposes a minimum network loss optimization control strategy for active distribution networks based on a distributed power flow controller. First, according to the characteristics of the distribution network load and the new energy power supply along the distribution line, the multiple sub-modules of the distributed power flow controller are arranged in sections in the distribution loop network line. The regulation effect of the distributed power flow controller on the voltage and power flow of the distribution loop network is analyzed. The loop network current equation and power loss equation after the distributed power flow controller are constructed, and the power equation with the minimum active loss of the active distribution loop network is derived. Second, the control strategy for minimum network loss of the active distribution network based on the distributed power flow controller is proposed. Finally, the simulation model of the active distribution loop network is constructed by PSCAD/EMTDC. The loss of the loop network before and after the distributed power flow controller is simulated and analyzed, and the effectiveness of the proposed strategy is verified.

Dual-layer loss reduction strategy for virtual distribution transformer integrating energy storage converter

To solve the problem that power quality disturbance aggravates the loss of distribution network in new power systems, this paper proposes a loss reduction strategy for virtual distribution transformer with integrated energy storage converter. Firstly, the concept of the virtual distribution transformer is defined through the analysis of the impact of complex power quality disturbances on distribution network losses. Secondly, a dual-layer control strategy for loss reduction based on the virtual distribution transformer is proposed. In the outer control layer, power quality control considering the load rate of the distribution transformer is achieved by extracting the integrated command signal that incorporates load fluctuations and using the improved quasi-proportional resonance controller. In the inner control layer, a grid-connected power stabilization strategy based on an adaptive double-window moving average filter is proposed, which adjusts the sliding window length in real-time according to the amplitude of fluctuation and state of charge, ensuring power meets the grid-connected standard while improving energy storage charge and discharge margins. Finally, simulation results validate that the proposed strategy enables the rapid realization of functions such as virtual capacity enhancement for distribution transformers, power flow control, and integration of new energy sources within short time scales while effectively reducing operating losses in low-voltage distribution networks.

Measurement and Assessment of Reactive, Unbalanced and Harmonic Line Losses

This study investigates the feasibility of utilizing the line loss power factor to assess the reactive, unbalanced, and harmonic line losses in low-voltage distribution networks and explores the method of calculating decoupled line loss values based on this factor. To achieve this objective, we establish preliminary definitions of single-phase and three-phase reactive, unbalanced, and harmonic line loss power factors, drawing upon the principles of electrical theory outlined in IEEE Standard 1459. These power factors serve as crucial indicators for evaluating the severity of line losses caused by reactive power, unbalance, and harmonic problems. Subsequently, the values of line loss attributed to reactive, unbalanced, and harmonic components are decoupled and quantified using the line loss power factor as a fundamental parameter. The effectiveness and accuracy of the proposed method were verified in Matlab simulation and physical experiments.

Optimal and distributed energy management in interconnected energy hubs

Recently, multi-carrier energy systems (MCESs) have been rapidly developed as flexible multi-generation systems aiming to satisfy load demands by purchasing, converting, and storing different energy carriers. This study specifically focuses on the optimal and robust large-scale coordination of interconnected energy hubs (IEHs) in an iterative consensus-based procedure considering distribution network losses. Furthermore, a new robust-based hybrid IGDT/consensus algorithm is introduced to achieve risk-averse optimal energy management in IEHs under uncertainty. The fast convergence, needless to collect the total information from all hubs, minimal computational burden, and more robust communication system are the most important features of the proposed distributed consensus algorithm in this study. The effectiveness of the proposed consensus algorithm is verified by simulation results considering various energy trading structures in IEHs at different scales. The obtained results highlight the scalability capability of the proposed method. Regarding an IEHS of 30 energy hubs, the computation burden is lightened by 0.53 (s) and 0.1917 (s), respectively with and without uncertainty. Considering distribution network losses, the total purchasing costs can be increased by 8%. The simulation results also reveal an increase of 11% in the total power trading under the uncertainty.

Enhancing Distribution Network Efficiency with Andean Condor Algorithm-Driven Optimal Placement of Distributed Generation and Network Reconfiguration

This research introduces an innovative methodology for optimizing placement of Distributed Generations (DGs) within distribution networks, with primary focus on reducing power losses and enhancing energy efficiency. The surge in electric power demand has necessitated integration of DGs into distribution networks, bolstering system security. However, this integration brings about structural changes and alters system parameters, such as power fluctuations, voltage profiles and fault current levels. To address these complexities, this research contributes comprehensive approach that integrates Andean Condor Algorithm (ACA) and Network Reconfiguration (NR) to optimize DG placement. The proposed methodology aims to minimize both active and reactive power losses in distribution networks. MATLAB simulations using the 69 and 33 bus distribution networks are used to thoroughly validate the system’s efficacy. The suggested ACA achieves an active power loss of 120 KW and a reactive power loss of 28.66 KVAr for 33 bus system. In the same way, 69 bus system manages to achieve a 16 KVAr reactive power loss and a 92.3 KW active power loss. The results show that suggested methodology is both effective and better than popular CPSO method, which makes it valuable addition to the field of distributed generation optimization in power distribution networks.

Power Losses Reduction in Three-Phase Unbalanced Distribution Networks Using a Convex Optimization Model in the Complex Domain

This article presents a solution methodology to minimize power losses in three-phase unbalanced distribution networks. This approach involved an efficient complex-domain model that is categorized under mixed-integer convex optimization. The methodology employed consisted of efficient load rotation at each constant power node via a three-phase rotation matrix that allows defining each load connection to minimize the expected power imbalance at the terminals of the substation, as well as the total grid power losses, and improve voltage profile performance at each system phase. The load imbalance, expressed as a percentage, can be defined as a function of the active, reactive, or apparent power. In addition, considering the complex-domain representation of three-phase electrical networks under steady-state conditions, a mixed-integer convex model was formulated to reduce the power imbalances. With the purpose of determining the initial and final power losses of these distribution systems, the successive approximations method was employed to address the three-phase power flow problem. As a result, numerical validations in the IEEE 25-bus system and a 35-node three-phase feeder showed that the final active power losses vary depending on the objective function analyzed. Therefore, for the test feeders studied, it is necessary to evaluate each objective function, with the aim of finding the one that yields the best numerical results. Power losses reductions of about 3.8056 % and 6.8652 % were obtained for both test feeders via the proposed optimization methodology. All numerical validations were performed in the Julia programming environment, using the JuMP optimization tool and the HiGHS solver.

Study of distribution network loss allocation with high photovoltaic penetration ratio

With the rapid development of renewable energy, the penetration ratio of photovoltaic (PV) in the distribution network is getting higher and higher. The fair and reasonable allocation of high loss caused by high PV penetration in the distribution network is of great significance to the healthy development of distribution systems. In order to study the loss allocation in distribution networks with high PV penetration, based on the cooperative game theory, combined with the power flow algorithm, the power flow and loss of the 72-node active distribution network in agricultural and pastoral areas were analyzed. Three scenarios were included: no PV, low PV penetration, and high PV penetration. The allocated loss of the distribution network with high PV penetration and multiple distributed generators was computed. The results show that the loss allocation method, which takes into account the power as well as the network topology, is applicable to distribution networks with multiple distributed generators. In distribution networks with high PV penetration, distributed generators are the main cause of high loss. If the network contains the extreme reverse power flow concerned in this paper, the loss allocated to DGs accounts for more than 95% of the total network power loss.

ANALYSIS REDUCTION OF ENERGY LOSSES IN DISTRIBUTION NETWORKS

The article analyzes the reduction of energy losses in distribution networks, which is a serious problem requiring an integrated approach, including technological, regulatory, and behavioral interventions. Some measures that can be developed and implemented to solve this problem are given, such as infrastructure modernization, taking into account investments in the modernization of distribution infrastructure, affecting a significant reduction in energy loss, and the introduction of load management strategies that optimize energy distribution and reduce losses. This includes measures such as load balancing, voltage regulation, and demand management programs to shift peak loads to off-peak hours. The integration of smart grid technologies allows monitoring and management of the distribution network in real-time. This includes the deployment of advanced measurement infrastructure, sensors, and automation systems to more effectively detect and reduce energy losses. Technological losses are associated with the technology of the process of transmission of electricity through networks. Commercial losses are measured by the difference between actual estimated losses and technological losses, taking into account commercial losses, electric energy entering the electric grid, electric energy supplied to consumers, and electricity costs for own needs of substations. Circuit engineering methods for accounting for energy losses in electrical networks are used in various combinations. At the same time, it should be borne in mind that the sequence of reporting operations using the results of specified specific parameters should give an accurate result. To determine the loss of electricity, data on the minimum and maximum load of the network and the amount of electricity consumed over the same period are required. The calculation is given by the method of average load. Information about the minimum and maximum network load is a key element for effective design, management and maintenance of electrical networks. This information allows you to optimize the use of resources and ensure reliable network operation in various conditions. Minimum network load - the minimum load is the minimum amount of energy that is consumed by the network during a certain period of time, for example, during night hours or periods of low activity. Information about the minimum load allows you to optimize network operation, for example, by turning off part of the etquipment or managing energy consumption during off-peak periods. Maximum network load - is the maximum amount of energy consumed by the network at a given time. This may be during periods of peak demand, for example, during a heat wave when a lot of air conditioners are running, or during an increase in production in industrial areas. The maximum load information allows you to determine the required power and capacity of the network equipment to ensure reliable operation under maximum load conditions. Keywords: distribution networks, network load, average load, power, period, power factor, electrical energy, duty cycle, consumers, practical research, voltage.

Decomposition of unbalanced operation incremented active power loss in distribution network

Decomposition of unbalanced operation incremented active power loss in distribution network

Coordinated optimization of source‐grid‐load‐storage for wind power grid‐connected and mobile energy storage characteristics of electric vehicles

AbstractThe rapid growth in the number of electric vehicles (EVs), driven by the ‘double‐carbon’ target, and the impact of uncontrolled charging and discharging behaviour and discharged battery losses severely limit electric vehicles’ low carbon characteristics. Existing research on systemic low‐carbon emissions and electric vehicle charging and discharging issues is usually determined by considering only carbon trading markets or charging and discharging management on the source side. In this regard, a coordinated and optimized operation model that considers the participation of electric vehicle clusters in deep peaking and the source network load and storage adjustable resources is proposed. The upper layer establishes a real‐time price‐based demand response mechanism for the load side with the minimum net load fluctuation as the objective function; the middle layer establishes a comprehensive operation mechanism for the source and storage side that includes an orderly charging and discharging peaking compensation mechanism for electric vehicles, and a deep peaking mechanism that takes into account clean emission, and constructs an optimal operation model with the minimum comprehensive operating cost as the objective function; the lower layer establishes a distribution network loss minimization model for the network side that takes into account the orderly charging and discharging of electric vehicle as the objective function. The optimal load model with the objective function of minimizing the distribution network loss is established at the lower level. Finally, the original problem is transformed into a mixed integer linear programming problem, and the model's effectiveness is verified by setting different scenarios. The model reduces the total cost by 22.22%, improves the wind power consumption rate by 19.55%, reduces the actual carbon emission by 16.66%, and reduces the distribution network loss by 13.91% compared to the basic model.

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.