Real Low Voltage Distribution Network Research Articles

Analysis of Voltage Profiles, Unbalance Voltages and Phase Balance Optimization of Single-Phase Customer and Photovoltaic Connection at Real Low Voltage Feeder

Abstract Voltage unbalance is common issue encountered in low voltage distribution networks, caused by uneven allocation of single-phase customers among phases. This paper analyses part of real low voltage distribution network in Bosnia and Herzegovina. The impact of single-phase customers and single-phase connected micro photovoltaic power plants (MPPP-s) were analysed. To reduce unbalance and improve voltage profiles, Phase Balance Optimization toolbox in DIgSILENT PowerFactory software was performed in six scenarios with different distribution of customers and different percentage of penetration of MPPS-s. The aim was to find method with least number of changes in customer and photovoltaic phase connection that fits within defined limits of voltage variations and voltage unbalance of European standard for power quality (EN 50160). Conclusion is that MPPP-s cause voltage increases in the network, as well as an increase in voltage unbalance, but these effects can be mitigated by proper distribution of customer loads and MPPS-s among phases.

Topology identification method of low-voltage distribution network based on measurement data of IoT devices

Accurate network topology information serves as the foundation for the operation of active control and active management, which have become indispensable requirements for the development of distribution networks with the growth of intelligence and information technology. The access of user-side Internet of Things (IoT) devices makes the topology of distribution network increasingly complex. IoT devices provide a considerable amount of information, which serves as the data basis for identifying the distribution network’s structure. The cyber–physical distribution system (CPDS) and the entire IoT distribution architecture are initially introduced in this study. Next, we enumerate the communication requirements of certain IoT services to illustrate the feasibility of IoT devices acquiring measurement data. Then, based on the measurement data that can be collected in real time by the user-side IoT devices, a data-driven low-voltage (LV) distribution network topology identification method is proposed through the similarity analysis of the measurement data. Finally, a real LV distribution network is used to validate the effectiveness and practicability of the proposed method, and the case study proves that the topology identification scheme is accurate and efficient. The method can provide correct physical connection information of LV distribution network, solving the problems of inflexibility and inefficiency of existing LV distribution network topology identification methods.

A Mixed-integer Linear Programming Model for Defining Customer Export Limit in PV-rich Low-voltage Distribution Networks

A photovoltaic (PV)-rich low-voltage (LV) distribution network poses a limit on the export power of PVs due to the voltage magnitude constraints. By defining a customer export limit, switching off the PV inverters can be avoided, and thus reducing power curtailment. Based on this, this paper proposes a mixed-integer nonlinear programming (MINLP) model to define such optimal customer export. The MINLP model aims to minimize the total PV power curtailment while considering the technical operation of the distribution network. First, a nonlinear mathematical formulation is presented. Then, a new set of linearizations approximating the Euclidean norm is introduced to turn the MINLP model into an MILP formulation that can be solved with reasonable computational effort. An extension to consider multiple stochastic scenarios is also presented. The proposed model has been tested in a real LV distribution network using smart meter measurements and irradiance profiles from a case study in the Netherlands. To assess the quality of the solution provided by the proposed MILP model, Monte Carlo simulations are executed in OpenDSS, while an error assessment between the original MINLP and the approximated MILP model has been conducted.

Effects of Household Battery Systems on LV Residential Feeder Voltage Management

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.

A Development PMU Device for Living Lab Applications

This paper is focused on the hardware and software development of a compact phasor measurement unit (PMU) device. This project seeks to provide a flexible, compact and development environment of hardware and software platform for advanced monitoring of a living lab Campus network. The development PMU ( $$D_{ev}-PMU$$ ) allows the exploration of the phenomena involved in the living lab Campus networks and can be very useful for the development of control and protection advanced applications in Low-Voltage (LV) power systems. The $$D_{ev}-PMU$$ is built on a general hardware platform and is able to process single as well as three-phase voltages. The performance of the proposed device is evaluated using the PMUCal Fluke set up as the benchmark and under real low-voltage distribution networks. Results confirm the accuracy of the proposed device for providing voltage phasors complying with the Standard limits for class P and M.

Smart coordination of battery energy storage systems for voltage control in distribution networks with high penetration of photovoltaics

The use of battery energy storage systems (BESS) is one of the methods employed in solving the major challenge of overvoltage, experienced on low voltage (LV) distribution networks with high penetration of photovoltaics (PV). The overvoltage problem limits the penetration levels of PV into the LV network, and the benefits that could be gained. This paper presents a smart scheme for the coordination of multiple battery energy storage systems (BESS) in such networks. An approximate method was adopted for the evaluation of network voltage sensitivity, and the coordination algorithm was developed based upon this. Through the efficient selection, coordination and timing of charge and discharge operations of the BESS, the scheme maintains bus voltages within statutory ranges during both periods of high PV power generation and high network load demand. The scheme also prevents sudden voltage rise, which usually occurs in such networks immediately a BESS gets fully charged. Simulations were carried out on a real LV distribution network and results demonstrate the effectiveness of this approach.