Distributed Network Applications Research Articles

PMU-based voltage estimation and distributed generation effects in active distribution networks

The integration of distributed generation (DG) and phasor measuring units (PMUs) has significantly impacted electrical distribution networks. This study focuses on real-time control of renewable energy intermittency and strategic PMU deployment. By optimizing the placement of PMUs and DGs, the study aims to achieve several goals: maximize power loss reduction, increase DG penetration, and maintain acceptable voltage profiles. The recommended PMU-based approach incorporates the optimal number of DGs into the distribution network for load flow analysis. Testing this technique on 12-bus, 33-bus, and 69-bus networks yielded substantial gains. For example, power loss was 81.044 % lower in the 33-bus system and 79.256 % lower in the 69-bus test system compared to the base-case scenario. We also compared these results with other methods found in the literature. The developed algorithm is recommended for application in a real electrical power distribution network for more efficient integration of PMUs and new distributed generation units. Integrating PMUs with DG benefits active distribution network management, enabling proactive grid management and stability.

Ranking-based multi/many-objective evolutionary optimization with hierarchical evaluation rules and its application in water distribution network

To solve multi/many-objective optimization problems characterized by complex Pareto fronts, we propose a two-phase ranking-based multi-objective evolutionary algorithm with hybrid convergence rules. Firstly, we introduce a novel hybrid convergence criterion by integrating Pareto dominance, localized pruning power (LPP), and distance-based measure. Secondly, we design a two-phase ranking-based selection strategy. Specifically, we employ the acute angle between objective vector and reference vector to divide the current population into multiple sub-populations. In the first-phase ranking, the solutions within each subpopulation are sorted according to the hybrid convergence rule. The best solutions are chosen based on the angle-based diversity criterion into separate fronts. In the second-phase selection, a specific number of solutions with best diversity are further selected to the next generation based upon their front-level assignments. To obtain better diversity of selected solutions, we employ the maximum angle between solutions and the sum of normalized objectives of solutions to assist the selecting process. The experimental results, which encompass benchmark problems involving up to 10 objectives as well as two real-world application instances, validating our proposed algorithm’s competitiveness and effectiveness.

Optimal integration and planning of PV and wind renewable energy sources into distribution networks using the hybrid model of analytical techniques and metaheuristic algorithms: A deep learning-based approach

The optimal integration of Renewable Energy Sources (RESs) into distribution networks is crucial for sustainable energy generation. However, existing approaches utilized for optimal RES integration, including metaheuristic and analytical methods, exhibit limitations. Analytical methods are simple to implement, computationally efficient, and able to model power system mechanisms, yet they face scalability issues, making them inefficient for large-scale distribution systems. On the other hand, metaheuristic approaches, while flexible for complex problems, suffer from premature convergence problems, limiting their application in real distribution networks. To address these issues, this paper introduces a novel hybrid model that combines the strengths of analytical and metaheuristic techniques. The proposed model employs a deep learning-based method called bi-directional long short-term memory (B-LSTM) network is utilized to handle the uncertainties associated with the Photovoltaic (PV) and Wind Turbine (WT) generation and load demand. Subsequently, a distinctive analytical multi-objective index-based procedure is developed to minimize the real and reactive energy loss and voltage deviation by analyzing the branch current change caused by the RES penetration in distribution networks. The optimization problem is then solved using the binary particle swarm optimization (BPSO) technique. Importantly, the presented method considers the long-term nature of optimal RES integration and reflects the seasonal impacts of the load and RES output powers. The proposed approach is applied to the IEEE 33-bus test system. The simulation results show that the introduced hybrid model, by addressing the limitations of existing approaches, offers a promising solution for efficient and effective RES integration in distribution systems while requiring low computational effort, thereby advancing the transition towards sustainable energy infrastructure.

Economic evaluation for 5G planning of distribution network considering transmission loss and vulnerability

Aiming at the difficulty of existing 4G networks to meet distribution network services, and the unclear economics of 5G in distribution network applications, an evaluation method of 5G communication planning economics for distribution networks considering the path loss model and comprehensive node vulnerability is proposed. Firstly, the exact coverage radius of 5G base stations is solved according to the signal transmission path loss model, and the objective function for solving the number and location of communication base stations in the distribution network is established. Secondly, a comprehensive communication node vulnerability evaluation model is established to judge whether each communication node meets the node vulnerability requirements under different communication modes, and to improve the communication reliability by installing additional fiber optic or micro base stations for nodes with too much vulnerability. Meanwhile, a comprehensive cost model for communication planning is established. Finally, the distribution network communication planning economic evaluation model is established. To verify the effectiveness of the method in this paper, the IEEE-123 topology model is used as an example to compare the economic evaluation of 5G and 4G in three scenarios: main urban areas, county town and township, respectively. According to the simulation results, using 5G communication in the distribution network reduces the total investment, operation and maintenance costs of the main urban area by 1.64 million yuan compared to 4G within 10 years. The total investment and maintenance cost of 5G in the county town for 10 years decreased by 2.16 million yuan compared to 4G. The cost of 5G in the township scenario is reduced by 860,000 yuan compared to 4G. According to the results of the comprehensive economic comparison, the comprehensive economic performance of 5G in the three scenarios is lower than that of 4G in the first few years due to the investment cost. In comparison, the comprehensive economic performance of 5G is better than that of 4G in the subsequent time due to the unique obtainable benefits of 5G, which concludes that 5G is better than that of 4G communication in all cases.

An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks

Distribution grids must be regularly updated to meet the global electricity demand. Some of these updates result in fundamental changes to the structure of the grid network. Some recent changes include two-way communication infrastructure, the rapid development of distributed generations (DGs) in different forms, and the installation of smart measurement tools. In addition to other changes, these lead to distribution grid modifications, allowing more advanced features. Even though these advanced technologies enhance distribution grid performance, the operation, management, and control of active distribution networks (ADNs) have become more complicated. For example, distribution system state estimation (DSSE) calculations have been introduced as a tool to estimate the performance of distribution grids. These DSSE computations are highly dependent on data obtained from measurement devices in distribution grids. However, sufficient measurement devices are not available in ADNs due to economic constraints and various configurations of distribution grids. Thus, the modeling of pseudo-measurements using conventional and machine learning techniques from historical information in distribution grids is applied to address the lack of real measurements in ADNs. Different types of measurements (real, pseudo, and virtual measurements), alongside network parameters, are fed into model-based or data-based DSSE approaches to estimate the state variables of the distribution grid. The results obtained through DSSE should be sufficiently accurate for the appropriate management and overall performance evaluation of a distribution grid in a control center. However, distribution grids are prone to different cyberattacks, which can endanger their safe operation. One particular type of cyberattack is known as a false data injection attack (FDIA) on measurement data. Attackers try to inject false data into the measurements of nodes to falsify DSSE results. The FDIA can sometimes bypass poor traditional data-detection processes. If FDIAs cannot be identified successfully, the distribution grid’s performance is degraded significantly. Currently, different machine learning applications are applied widely to model pseudo-measurements, calculate DSSE variables, and identify FDIAs on measurement data to achieve the desired distribution grid operation and performance. In this study, we present a comprehensive review investigating the use of supervised machine learning (SML) in distribution grids to enhance and improve the operation and performance of advanced distribution grids according to three perspectives: (1) pseudo-measurement generation (via short-term load forecasting); (2) DSSE calculation; and (3) FDIA detection on measurement data. This review demonstrates the importance of SML in the management of ADN operation.

Smart Water Grids and Digital Twin for the Management of System Efficiency in Water Distribution Networks

One of the main factors contributing to water scarcity is water loss in water distribution systems, which mainly arises from a lack of adequate knowledge in the design process, optimization of water availability, and poor maintenance/management of the system. Thus, from the perspective of sustainable and integrated management of water resources, it is essential to enhance system efficiency by monitoring existing system elements and enhancing network maintenance/management practices. The current study establishes a smart water grid (SWG) with a digital twin (DT) for a water infrastructure to improve monitoring, management, and system efficiency. Such a tool allows live monitoring of system components, which can analyze different scenarios and variables, such as pressures, operating devices, regulation of different valves, and head-loss factors. The current study explores a case study in which local constraints amplify significant water losses. It develops and examines the DT model’s application in the Gaula water distribution network (WDN) in Madeira Island, Portugal. The developed methodology resulted in a significant potential reduction in real water losses, which presented a huge value of 434,273 m3 (~80%) and significantly improved system efficiency. The result shows a meaningful economic benefit, with savings of about EUR 165k in water loss volume with limiting pressures above the regulatory maximum of 60 m w.c. after the district metered area (DMA) sectorization and the requalification of the network. Hence, only 40% of the total annual volume, concerning the status quo situation, is necessary to supply the demand. The infrastructure leakage index measures the existing real losses and the reduction potential, reaching a value of 21.15, much higher than the recommended value of 4, revealing the great potential for improving the system efficiency using the proposed methodology.

Research on leakage area detection method in water distribution network based on gray wolf optimization

Abstract Leakage in a water distribution network (WDN) leads to a large amount of water loss and water pipe pollution and affects residents’ domestic water supply. Therefore, network leakage detection significantly saves water resources. The traditional model approach has ample search space in solving large distribution network applications, a challenging and complex leakage detection process and a low detection accuracy. For the above problems, this study proposes a new method of leakage area detection based on gray wolf optimization (GWO). First, the extensive WDN is divided into several virtual areas. Then, the leakage is simulated by the additional water demand of nodes, and the node demand of the distribution network is calibrated based on the GWO algorithm. Finally, the leakage area is identified, and the size of the leakage in that area is estimated. The method was experimented on in two cases, simulating single-point leakage and multi-point simultaneous leakage, respectively. The results show that the method estimates the size of leakage in the corresponding area based on accurate identification of leakage areas, and the detection error of leakage is within 17.14%. The method provides water workers with guidance on leak detection, significantly reducing staff time to repair pipes.

Optimal Placement and Sizing of Distributed Generation (DG) Units in Electrical Power Distribution Networks

Researchers' attention has recently been on the best ways to integrate Distributed Generation (DG) into the conventional centralized electrical power distribution systems, particularly in the context of the smart grid idea due to its reputation as a viable remedy for the lack of electric power supply. To optimize the environmental, financial, and technological advantages of DG units’ integration for distribution network operators, it is crucial to determine their ideal position and size. The main objective of this study was to develop and simulate an optimization system for the placement and sizing of distributed generation units in electrical power distribution networks for power losses reduction and voltage profile improvement. The specific objectives were to model and develop the load flow algorithm and codes; develop a meta-heuristic optimization algorithm and codes that selects the best location and size of the DG unit; simulate the nested load flow and optimization algorithms and codes on MATLAB and analyze the effectiveness of the developed algorithm via testing on the standard IEEE 33-bus radial electrical power distribution benchmark network. The Backward-Forward Sweep (BFS) technique was employed in the load flow modelling owing to its maximization of the radial structure of distribution systems. The optimization algorithm was developed based on the Multi-objective Particle swamp optimization (PSO) meta-heuristic technique due to its effective global searching characteristic. The line and load data for the IEEE 33-bus test network being a cutting-edge benchmark for contemporary power distribution networks; were obtained from the Power Systems Test Case Archive- a secondary data source. For this network fed by a synchronous generator, the chosen base MVA (Mega Volt Amp) was 10MVA and the base voltage was 12.66kV. The total active and reactive power demand were 3.715MW and 2.3Mvar respectively. The simulation was done on R2021a version of MATLAB/Simulink. The total real and reactive power losses obtained from base case simulation without the placement of any DG unit in the network were obtained as 201.8925kW and 134.6413kvar respectively while the per unit (p.u) average bus voltage was 0.948594 p.u. After the optimal allocation of one, two, three and four DG units, the total real power loss (in kW) in the network reduced by 140.89, 173.89, 189.89 and 195.89 respectively while the total reactive power loss (in kvar) reduced by 86.64, 114.64, 124,64 and 128.64 respectively. Likewise, the per unit average bus voltage improved by 0.0376p. u, 0.0458p.u, 0.0480p.u and 0.0498p.u respectively. Also, the decrease in the total real and reactive power losses and the improvement in bus voltage profiles varies proportionally with the number of DG units optimally placed. In conclusion, the results shows that the total real power loss and the total reactive power loss of the network, were significantly decreased; and the voltage profile of the system was drastically enhanced by incorporating DG units at predetermined buses. The developed algorithm is recommended for application in a real electrical power distribution network for more efficient integration of new distributed generation units in the current electrical power distribution networks.

Terminal Node of Active Distribution Network Correlation Compactness Model and Application Based on Complex Network Topology Graph

Multiple nodes (such as distributed generation (DG), electric vehicles (EV), energy storage (ES), flexible loads (FL), etc.) are connected to the active distribution network (ADN), which changes its original operational mode. According to the bidirectional current and low-voltage transmission mode, this study proposed a multi voltage and multi electricity flat loop network, AC/DC (Alternating Current/ Direct Current) hybrid network, unified interface and flexible self-organizing network based on Complex network theory. First, the ADN complex network topology of various nodes is established based on the actual grid connected terminal nodes and power flow sensitivity algorithm. Second, using the TOPSIS model, the influence factor matrix of weighted directed network is established. The matrix can be used to guide the formulation of the distribution network operation mode, and the robustness and reliability of this paper are verified by using the standard multi voltage level main distribution hybrid model provided by the Panda Power website as the verification method. Finally, using the influence maximization calculation model of the New Creedy algorithm, the node correlation matrix is expanded to form a super family region set of active distribution network. The results show that the seven nodes in this paper have high correlation, while the other nodes have low correlation. In addition, the change of reactive power has little impact on other nodes, for a node with a change rate of 0, it is obviously not in the same power supply family as node 1, and theoretically it may not have a topological relationship, be a power generation node, or be completely independent. Analyzing the relationship between nodes has a guiding significance for power supply recovery and interaction in distribution network reconfiguration.

Under voltage load shedding and penetration of renewable energy sources in distribution systems: a review

ABSTRACT The growing energy demand and the emerging environmental concerns across the globe caused increasing penetration of renewable energy sources (RESs) in distribution systems. One of the major issues faced by modern distribution systems with high RESs penetration is voltage instability. Generally, voltage instability can lead to voltage collapse, which is one of the key drivers for blackout incidents around the world. Under voltage load shedding (UVLS) schemes are usually executed as the final option to prevent a large-scale blackout or voltage collapse. But ever-increasing integration of RESs in distribution systems creates an additional threat to the existing UVLS schemes. Therefore, new UVLS methods are necessary to provide the appropriate corrective scheme for distribution systems, particularly when connected with RESs. This paper reviews and updates the recent UVLS methods emphasizing on distribution network application. In addition, the contributions and limitations of the conventional, adaptive, and computational intelligence techniques of UVLS are highlighted. Moreover, this paper provides an overview of the voltage stability indices applied for UVLS methods. Also, at the end of this review article, the further research directions to improve the safety of modern distribution systems during emergencies using UVLS are drawn as the conclusion.

Implementation of EDGE Computing Platform in Feeder Terminal Unit for Smart Applications in Distribution Networks with Distributed Renewable Energies

Under the plan of net-zero carbon emissions in 2050, the high penetration of distributed renewable energies in distribution networks will cause the operation of more complicated distribution networks. The development of edge computing platforms will help the operator to monitor and compute the system status timely and locally, and it can ensure the security operation of the system. In this paper, a novel EDGE computing platform that is implemented by a graphics processing unit in the existing feeder terminal unit (FTU) is proposed for smart applications in distribution networks with distributed renewable energies and loads. This platform makes timely forecasts of the feeder status for the next seven days in accordance with historical weather, sun, and loading data. The forecast solver uses the machine learning long short-term memory (LSTM) method. Thereafter, the power calculation analyzers transform feeder topology into the circuit model for transient-state, steady-state, and symmetrical component analyses. An important-factor explainer parses the LSTM model into the concise value of each historical datum. All information transports to remote devices via the internet for the real-time monitor feature. The software stack of the EDGE platform consists of the database archive file system, time-series forecast solver, power flow analyzers, important-factor explainer, and message queuing telemetry transport (MQTT) protocol communication. All open-source software packages, such as SQLite, LSTM, ngspyce, Shapley Additive Explanations, and Paho-MQTT, form the aforementioned function. The developed EDGE forecast and power flow computing platform are helpful for achieving FTU becoming an Internet of Things component for smart operation in active distribution networks.

Risk assessment in distribution networks considering cyber coupling

With the continuous development of information and communication technologies and their wide application in distribution networks, which have evolved into cyber-physical distribution systems (CPDS). Yet, interactions between cyber and physical systems mean power systems face a greater potential risk. Since feeder automation (FA) systems in a CPDS can quickly isolate faults and restore services, to reduce customer outage times, the potential effect of cyberattacks on FA systems is massive. This paper proposes a risk assessment method for cyberattacks in distribution networks considering cyber coupling. The proposed method takes into account the impacts of cyber systems on power systems, and vice versa. Regarding the cyberattack, an attack method to mislead the judgment of FA systems by tampering with monitoring data of remote terminal units (RTUs) is analyzed, and a Bayesian attack graph (BAG) model is adopted to quantify the successful probability of RTUs. To calculate the attack consequences, a power supply restoration model considering the interdependence of cyber and physical systems is developed, which is measured in scale and in duration to full restoration. The proposed methodology is tested and validated using the modified IEEE 33-node and CPS 62-node test systems. This study highlights the importance of assessing distribution network security from the cyber-physical point of view.

On the Development of Overcurrent Relay Optimization Problem for Active Distribution Networks

The goal of this review paper will be to address complex optimization functions in the area of overcurrent relay optimization, to inspect and valorize their objectives and to critically examine their application in real distribution networks. Special emphasis will be put on observing the optimization of discrimination time between primary and backup relay pairs, and methods which prevent the latter becoming penalty functions will be shown. Furthermore, the impact of distributed generation units on the overcurrent relay optimization problem will be elaborated in detail. Protection settings of all the existing and new relays must be thoroughly inspected before the connection of new production units, because contributing fault currents may have a significant effect on the solution of the overcurrent optimization problem. This is because the relays being used are of an inverse-type. Next, meshed network operation will be critically assessed in comparison with the actual practice of radial operation, and some topologies will be highlighted for future research, while others will be discarded. The distribution network in mesh operation implicitly suggests using numerical relays on both sides of protected elements (usually lines), so their total number will be much higher than in radial operation. Finally, a relatively new concept of adaptive distribution network protection will be reviewed, and its potential application to the overcurrent relay optimization problem will be examined. The purpose of this review paper will be to lay a groundwork for future research papers by inspecting the overall development of the overcurrent relay optimization problem for contemporary and future active distribution networks.

Optimum allocation of multiple type and number of DG units based on IEEE 123-bus unbalanced multi-phase power distribution system

The increasing demand for clean energy has resulted in the growth of renewable energy-based distributed generation (DG) penetration in the power distribution networks (PDN). The crucial point here is to find the optimum allocation of DGs to minimize the power loss and enhance the system performance overall. In this study, the Equilibrium Optimizer (EO) algorithm-based method is proposed to determine the location, size, connection type, and power factor of three different types of multiple DGs based on a three-phase 123-bus unbalanced PDN. The complex 123-bus UPDN has been very little studied to date, and most existing research works on the DG allocation problem have been conducted using a co-simulation platform. In this work, an unbalanced three-phase backward forward load flow (UBFLF) algorithm is written and executed under the MATLAB environment. This approach has decreased the complexity of co-simulation and provided advantages like optimizing the power factor value and the connection type (Delta/Wye) of DGs easily. Six more recent and well-established optimization algorithms such as PSO, JFO, BO, SMA, FDA, and GBO are also applied under the same test conditions to solve the Type-III DGs allocation problem. The results obtained are compared with the proposed method to show the effectiveness of the method in terms of elapsed time, statistical variables (such as standard deviation, median, variance and so on), converge speed, and best/worst case active power loss values. The efficacy of the presented method is also validated by comparing the results obtained using three distinct platforms as MATLAB/Simulink, IEEE-PES data, and OpenDSS. The detailed and comprehensive analyses show that the proposed approach demonstrates the lowest power loss, more enhancement in voltage profile, and the superiority of the EO algorithm in the application of a 123-bus unbalanced power distribution network.

Day-ahead local flexibility market for active and reactive power with linearized network constraints

This paper presents a Day-Ahead Local Flexibility Market in the Distribution Network cleared using a novel Successive Linear Programming algorithm for Optimal Power Flow in the Distribution Network. The proposed algorithm is shown to be robust and produce high quality, AC feasible solutions with satisfactory solution speed. The efficiency of the proposed algorithm is validated via its application in various distribution networks and under different loading scenarios. The proposed Flexibility Market encompasses flexibility products for upward and downward active energy as well as service products for the provision of reactive power. The efficiency of the proposed Flexibility Market in solving operational issues of the distribution network is demonstrated via a case-study.

Feasibility of Solar Grid-Based Industrial Virtual Power Plant for Optimal Energy Scheduling: A Case of Indian Power Sector

The increased popularity of small-scale DER has replaced the well-established concept of conventional generating plants around the world. In the present energy scenario, a significant share of energy production now comes from the grid integrated DERs installed at various consumer premises. These DERs are being renewable-based generates only intermittent power, which in turn makes the scheduling of electrical dispatch a tough task. The Virtual Power Plant (VPP) is a potential solution to this challenge, which coordinates and aggregates the DERs generation into a single controllable profile. In this paper, a modified PSO-based multi-objective optimization is proposed for the VPP scheduling in distribution network applications such as energy cost minimization, peak shaving, and reliability improvement. For feasibility analysis of the VPP, a case study of state power utility is taken, which includes a 90 bus industrial feeder with grid integrated PVs as DER. The optimized results are computed in both grid-connected and autonomous mode reveal that the operating cost, peak demand, and EENS are declined by 31.70%, 23.59%, and 62.30% respectively. The overall results obtained are compared by the results obtained from other well-established optimization techniques and it is found that the proposed technique is comparatively more cost-effective than others.

Distributed Destination Search Routing for 5G and beyond Networks.

Fifth-generation and beyond networks target multiple distributed network application such as Internet of Things (IoT), connected robotics, and massive Machine Type Communication (mMTC). In the absence of a central management unit, the device need to search and establish a route towards the destination before initializing data transmission. In this paper, we proposes a destination search and routing method for distributed 5G and beyond networks. In the proposed method, the source node makes multiple attempts to search for a route towards the destination by expanding disk-like patterns originating from the source node. The source node increases the search area in each attempt, accommodating more nodes in the search process. As a result, the probability of finding the destination increases, which reduces energy consumption and time delay of routing. We propose three variants of routing for high, medium, and low-density network scenarios and analyze their performance for various network configurations. The results demonstrate that the performance of the proposed solution is better than previously proposed techniques in terms of time latency and reduced energy consumption, making it applicable for 5G and beyond networks.

A Fourier-Based Phasor Estimator With a Modified Moving Average Filter and Its Application in Distribution Networks

Phasor estimation is fundamental for most real-time analysis, monitoring, and control tasks in power systems. New applications on microgrids and active distribution networks make such estimation increasingly important, leading to many research efforts focusing on known approaches, such as the Fourier and cosine filters, to abate the estimation errors; others rely on newly applied algorithms like the Taylor–Kalman–Fourier filter (TKF). This variety has led to discussions about the application cases of each technique, normally demoting the Fourier filter (FF). In this spirit, the FF was reworked under the Hilbert space power theory, showing its conceptual correctness and then deriving an accurate and fast alternative relying on a discrete, lead-compensated filter. Such FF is tested over an active distribution network system, showing advantages in relevant scenarios, namely impedance estimation, grid-monitoring, and fault location. The proposal exhibits better dynamic performance and overshoot than the conventional techniques, with no increased complexity, and “cleaner” results than the TKF, which is, however, faster to estimate the system's impedances.

A Mixed-Integer Linear Programming Model for Simultaneous Optimal Reconfiguration and Optimal Placement of Capacitor Banks in Distribution Networks

Optimal capacitor placement and network reconfiguration are well-known methods to minimize losses, enhance reliability, and improve the voltage profile of electric distribution networks (EDNs). Distribution network reconfiguration (DNR) consists of altering the system topology by changing the states of ties and sectionalizing switches, while the optimal placement of capacitors (OPCAs) involves sizing and finding the optimal location of capacitor banks within the distribution network for reactive power control. DNR and OPCAs are challenging optimization problems involving both integer and continuous decision variables. Due to the nature of these problems (combinatorial optimization problems), most approaches that deal with DNR and OPCAs resort to metaheuristic techniques, and they are limited to applications in small-size distribution networks. Although these techniques have proven to be effective when dealing with non-convex optimization problems, their main drawbacks lie in the fact that they require the fine-tuning of several parameters and do not guarantee the finding of a globally optimal solution. This paper presents a mixed-integer linear programming (MILP) model to solve simultaneous DNR and OPCAs in radial distribution networks. The proposed model can be solved by commercially available software; it guarantees to obtain globally optimal solutions and requires low computational effort when compared with metaheuristic techniques employed for the same purpose. Several tests were carried out on seven benchmark EDNs ranging from 33 to 417 buses. In all cases, the proposed methodology was able to replicate the results reported in the specialized literature. Regarding the DNR alone, a novel solution was found for the 119-bus test system which is 1.86 % better than that previously reported in the specialized literature. Furthermore, new solutions are reported for the simultaneous optimal DNR and OPCAs for medium and large-size EDNs. The power loss reduction in the test system ranged from 21.12 % to 68.93 % evidencing the positive impact of the proposed approach in EDNs.

A Remote Estimation Method of Smart Meter Errors Based on Neural Network Filter and Generalized Damping Recursive Least Square

To solve large-scale smart meter verification and periodic replacement problems, a remote estimation method based on neural network filter (NNF) and generalized damping recursive least square (GDRLS) is proposed. In this article, a smart meter error estimation model with a loss noise filter is built. A typical loss noise filter is designed with a neural network, so that the filtered loss noise meets the Gauss–Markov condition, which paves the way for the best linear unbiased estimation (BLUE). GDRLS algorithm is applied to solve the novel estimation model, which can effectively address the problem that large loss estimation errors merge small smart meter errors. Then, a complete process of the proposed method is constructed, which can estimate both the user smart meter errors, and the loss noises accurately. Finally, the effectiveness, superiority, and applicability of the proposed method are verified through simulation analysis and practical distribution network application.