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With low voltage (LV) distribution networks increasingly being re-purposed beyond their original design specifications to accommodate low carbon technologies, the ability to accurately calculate their actual spare capacity is critical. Traditionally, within the Great Britain (GB) power system, there has been limited monitoring of LV distribution networks, making this difficult. This paper proposes a method for estimating spare capacity of unmonitored LV networks using demand data from customer Smart Meters. In particular, the proposed method infers existing LV network capacity, as well as losses, across scenarios where only a limited number of customers have Smart Meters installed. Typical daily load profiles across customers with Smart Meters are learned using a Dirichlet sampled Gaussian mixture model (GMM). Learned profiles are then applied to all unmetered customers to estimate network parameters. Method accuracy is assessed by comparing estimations with simulated, fully observed, LV network models. The method is also compared to benchmark models for establishing unobserved demand profiles. Overall, results in the paper show that the proposed method outperforms benchmark models in terms of accurately assessing substation headroom, particularly in scenarios where only 10–50% of customers have Smart Meters installed. [ABSTRACT FROM AUTHOR]

The lack of information regarding the systemic behavior of new software and hardware before introducing them for controlling and operating power networks consists of a major challenge for power distribution utilities towards a smart grid implementation. In this context, this paper describes an innovative and cost-effective test platform for advanced distribution automation (ADA) functionalities. The platform integrates a steady-state power system simulator, intelligent electronic devices, smart meters, communication infrastructure, and typical operation systems, using a cyber-physical system approach. The development of such platform originated a novel smart grid emulator, aiming at the execution of systemic tests and validation of ADA functionalities in a controlled environment, allowing systemic analysis rarely found in hardware-in-the-loop (HIL) testbeds. Through such an approach, it is possible to predict and solve typical issues that would be typically noticed only in field implementation. The study case presents tests of a specific algorithm for downed conductor location, considering the environment that the power distribution utility would have through the deployment of a smart grid infrastructure. The paper also discusses the robustness of the event-driven synchronization as an interface technique for HIL and software-in-the-loop environment, which is responsible for integrating operation systems, field equipment, and the power system simulator. [ABSTRACT FROM AUTHOR]

Operational performance in the low-voltage distribution network (LVDN) can be undermined by its inherent unbalances, which may become worse as the penetration of rooftop solar continuously increases. To address this issue, load balancing via phase-reconfiguration devices (PRDs), which can change phase positions of residential customers as required, provides a cost-efficient option. However, most reported approaches to control PRDs require that demands of all residential customers are available, which are not viable for many LVDNs without smart meters or advanced metering infrastructure (AMI) installed. To bridging the gap in this field, this paper proposes a novel method to control PRDs purely based on measurable data from PRDs, and its controller. Based on limited information, sensitivity analysis in the network with PRDs is studied, followed by the optimization model that comprehensively considers operational requirements in the network. Moreover, slack variables are introduced to the model, and penalized in the objective function to assure either a strategy that is secure or with minimized violations can always be provided. The model is a challenging mixed-integer non-convex programming (MINCP) problem, which is reformulated as an efficient solvable mixed-integer second-order cone programming (MISOCP) based on exact reformulations or accurate linear approximations. Simulations based on two modified IEEE systems, and a real system in Australia demonstrate that an efficient strategy can be provided to mitigate unbalances in the network. [ABSTRACT FROM AUTHOR]

With the advancements of the battery energy storage systems (BESSs), reduction of their manufacturing costs and government subsidies, the BESS uptake is likely to increase rapidly in power distribution networks. This paper investigates the effects of residential BESSs on low-voltage (LV) networks using the actual household load profiles equipped with BESS and solar-photovoltaic (PV) systems. The electricity consumption data collected via smart meters (2200 households with PV/BESS, 1950 households with PV only and 1000 households without a PV or a BESS) at different solar-PV penetration levels and network types are used to simulate real network operating scenarios. A real LV distribution network in Australia is analysed in DIgSILENT PowerFactory under different scenarios, such as, customers with and without a solar-PV/BESS, with a solar-PV but without a BESS, and without a solar-PV, by using both the power flow and quasi-dynamic simulation studies under balanced and the unbalanced network loading conditions. According to the study, customers experience large voltage excursions from solar-PV power exports, which could be resolved by the household BESS, provided that the BESS charging is coordinated with the solar-PV production. Moreover, quasi-dynamic simulation shows that the BESS could reduce the violation of the over-voltage limit during the solar peak hours (midday) by lowering the worst-case feeder voltage by 3%. Finally, extreme-event (high solar PV generation scenario) simulation shows that the implementation of the BESS controller to facilitate charging BESS during afternoon solar-PV export may reduce the negative grid impact and will assist to avoid network upgrades. [ABSTRACT FROM AUTHOR]

The article presents the cross-sectional results of analyzes of authentic conducted disturbances introduced into public power networks by various types of loads installed at end users. The foundation for the research was a set of 260 identified cases of Power Line Communication (PLC) transmission disturbances based on the Open Smart Grid Protocol (OSGP) technology, which were recorded in the urban network of the distribution system operator covering over 400,000 smart meters. The paper presents a quantitative assessment of the share of loads that most often cause problems with transmission efficiency. Moreover, a qualitative assessment of the detected disturbance cases was carried out in terms of conditions relating to the structure of the power grid and real transmission levels and disturbance levels. The authors made a pioneering analysis in the so-called “communication path” where the PLC communication disturbance was identified, i.e., from the installation point of the PLC concentrator to the metering point where the energy meter, with which the connection was lost, was installed. [ABSTRACT FROM AUTHOR]

This paper proposes a new impedance-based technique to locate all fault types in distribution networks with/without distributed generators. A new procedure to form an impedance matrix using only series impedances of the distribution lines is introduced. The impedance matrix along with the prefault and during-fault voltage phasors at few buses is used to estimate the injection fault current via the least-squares technique. Linear least-squares estimator is utilized if microphasor measurement units (\mu \rmPMUs) are installed along the network. However, a nonlinear least-squares problem solved by the trust-region-reflective algorithm is used when only the voltage magnitudes are provided by smart meters. The operation of the standard protective devices in the distribution networks is used to reduce the computational burden of the proposed method. Also, a generalized measurement placement algorithm is studied using the discovered features of the impedance matrix. In addition, the Sobol's sensitivity analysis is conducted to quantify the importance of different input factors on the fault location accuracy. The effectiveness of the proposed method is validated on a real 134-bus, 13.8 kV distribution network under several fault scenarios and noisy measurements. [ABSTRACT FROM AUTHOR]

The smart meters installed at the customer premises are one of the main apparatuses promoting the modernization of the distribution systems. These devices collect a huge amount of data, demanding the development of analytic techniques to transform these data into useful information. In addition, by proposing new applications to data supplied by smart meters, more value is added to this equipment, allowing higher return on the associated investments. Utilities can have a business case by increasing their operational efficiency if smart meters are used, for instance, to identify non-technical losses, which represent an important cause of revenue losses. This paper presents a new data analytic technique for detection and location of non-technical losses caused by illegal connections of loads to distribution systems in the presence of smart meters. The data analytic technique relies on bad data analysis, similar to the ones used in state estimation methods, developed specifically for this application. A real 34-bus low voltage system is used to illustrate the main concepts of the proposed algorithm. Systematic tests are also conducted on a real 1682-bus distribution system to evaluate the method performance considering electricity theft caused by medium and low voltage customers. [ABSTRACT FROM AUTHOR]

This paper presents an automatic faulted line section location method for distribution systems. It can be divided into two parts: faulted line section location method for medium-voltage distribution feeders and faulted area identification technique for low-voltage distribution systems. In the first part, an adjacency list that shows the adjacency relationship and status of fault indicating devices is developed. Then, an iterative search technique is devised to traverse the adjacency list to determine the faulted line section. In the second part, a relational table that shows the association relationship between smart meters, meter boxes, and distribution transformers is developed. Based on the table and the voltage of smart meters received from the customer electric information acquisition system, the faulted area can be located. Several test cases studied in a real distribution system demonstrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel method for fault location in distribution networks using compressive sensing. During fault and prefault voltages are measured by smart meters along the feeders. The voltage sag vector and impedance matrix produce a current vector that is sparse enough with one nonzero element. This element corresponds to the bus at which a fault occurs. Due to the limited number of smart meters installed at primary feeders, our system equation is underdetermined. Therefore, the \ell ^1-norm minimization method is used to calculate the current vector. Primal-dual interior point (PDIP) and the log barrier algorithm (LBA) are utilized to solve the optimization problem with and without measurement noises, respectively. Our proposed method is implemented on a real 13.8-kV, 134-bus distribution network when single-phase, three-phase, double-phase, and double-phase-to-ground short circuits occur. Simulation results show the robustness of the proposed method in noisy environments and satisfactory performance for various faults with different resistances. [ABSTRACT FROM AUTHOR]

Monitoring the operating conditions of residential appliances by collecting the total power consumption data of households has a great significance for smart power utilization. In this study, a graph least squares reconstruction approach is proposed. First, the graph signal is constructed using the collected active power consumption data, providing unique appliance signature information. The signal smoothness of the graph and an iterative least squares reconstruction algorithm are utilized for classification. The proposed graph reconstruction method relies on only low-sampling data from installed smart meters. It aims to address some existing problems of event-based NILM methods like measurement noise, indistinguishable load signatures, and inaccurate power reconstruction. Also, extensive training and associated calculations are not required. Simulation results on the REDD benchmark dataset demonstrate that the proposed method outperforms some of the current state-of-the-art techniques. [ABSTRACT FROM AUTHOR]