Low Voltage Distribution Networks Research Articles

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.

Signal Injection-Based Topology Identification for Low-Voltage Distribution Networks Considering Missing Data

With the widespread use of new equipment such as distributed photovoltaics, distributed energy storage, electric vehicles, and distributed wind power, the control of low-voltage distribution networks (LVDNs) has become increasingly complex. Acquiring the most recent topological structure is essential for conducting accurate analysis and real-time control of LVDNs. The signal injection-based topology identification algorithm is favored for its speed and efficiency. This research introduces an innovative topology identification algorithm based on signal injection, specifically designed to address the challenges of incomplete and inaccurate identifications caused by the missing data in feature signal records (FSRs). Based on the correlations among FSRs at various devices, the algorithm introduces a dual-axis completion strategy—both vertical and horizontal—to effectively address missing data. Subsequently, an inclusion detection process is devised to process the completed FSRs, culminating in an accurate topology of LVDNs. Based on the study of actual LVDN data, the results indicate that the proposed algorithm markedly enhances the completeness and accuracy of topology identification. This advancement offers a robust solution tailored to accommodate the dynamic and swiftly changing topological configurations of LVDNs.

A Model-Free Deep Reinforcement Learning-Based Approach for Assessment of Real-Time PV Hosting Capacity

Assessments of the hosting capacity of electricity distribution networks are of paramount importance, as they facilitate the seamless integration of rooftop photovoltaic systems into the grid, accelerating the transition towards a more carbon neutral and sustainable system. This paper employs a deep reinforcement learning-based approach to evaluate the real-time hosting capacity of low voltage distribution networks in a model-free manner. The proposed approach only requires real-time customer voltage data and solar irradiation data to provide a fast and accurate estimate of real-time hosting capacity at each customer connection point. This study addresses the imperative for accurate electrical models, which are frequently unavailable, in evaluating the hosting capacity of electricity distribution networks. To meet this challenge, the proposed approach utilizes a deep neural network-based, data-driven model of a low-voltage electricity distribution network. This proposed methodology incorporates model-free elements, enhancing its adaptability and robustness. In addition, a comparative analysis between model-based and model-free hosting capacity assessment methods is presented, highlighting their respective strengths and weaknesses. The utilization of the proposed hosting capacity estimation model enables distribution network service providers to make well-informed decisions regarding grid planning, leading to cost minimization.

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.

Impact of Prosumers’ Operation on Selected Parameters of Low-Voltage Distribution Network Operation

This article presents the issue of the impact of the operation of prosumer installations on the operation of the distribution network. The emergence of a very large number of distributed generation units in electric distribution networks (in particular, photovoltaic installations in low-voltage grids) can be associated with both positive and negative consequences. Positive consequences can include, among others, the environmental aspect of electricity generation. Negative consequences, on the other hand, can include technical problems of power grid operation, manifested, for example, in changes that can be observed in steady states, in the values of such parameters of grid operation as voltage level, values of branch currents, or power and energy losses. The issue discussed will be illustrated by a calculation example considering, among other things, the size and location of the prosumer installation.

Enhancing Low-Voltage Distribution Network Safety through Advanced Residual Current Protection Techniques

Residual current protection can detect and isolate the grounding (leakage) fault of low-voltage distribution networks in time, which is an essential technical measure to reduce electric shocks and fire accidents and improve power supply safety. This paper systematically analyzes the operating characteristics of low-voltage distribution networks and proposes a distributed residual current protection method based on closed sections. It utilizes the capabilities of a distribution IoT platform to provide comprehensive measurement information for the entire substation area. A method was introduced to divide the low-voltage distribution substation into different protection closed surfaces, defining the current phasor at the external contact point of the closed surface and the remaining current of the closed surface. A calculation method for the critical current based on the remaining current of the closed surface for fault detection was proposed. Case studies have shown that this method is less affected by the inherent current and can significantly improve the sensitivity of protection. For TN-C and TN-C-S systems, in the selection of closed surfaces, the repeated grounding point of the neutral line is excluded from the closed surface. This method can also overcome the impact of residual current changes when load switching, demonstrating the flexibility of the new principle.

Leveraging Transformer-Based Non-Parametric Probabilistic Prediction Model for Distributed Energy Storage System Dispatch

In low-voltage distribution networks, distributed energy storage systems (DESSs) are widely used to manage load uncertainty and voltage stability. Accurate modeling and estimation of voltage fluctuations are crucial to informed DESS dispatch decisions. However, existing parametric probabilistic approaches have limitations in handling complex uncertainties, since they always rely on predefined distributions and complex inference processes. To address this, we integrate the patch time series Transformer model with the non-parametric Huberized composite quantile regression method to reliably predict voltage fluctuation without distribution assumptions. Comparative simulations on the IEEE 33-bus distribution network show that the proposed model reduces the DESS dispatch cost by 6.23% compared to state-of-the-art parametric models.

Topology Identification of Active Low-Voltage Distribution Network Based on Regression Analysis and Knowledge Reasoning

Due to the access of distributed energy and a new flexible load, the electrical characteristics of a new distribution network are significantly different from those of a traditional distribution network, which poses a new challenge to the original topology identification methods. To address this challenge, a hierarchical topology identification method based on regression analysis and knowledge reasoning is proposed for an active low-voltage distribution network (ALVDN). Firstly, according to the new electrical characteristics of bidirectional power flow and voltage jump caused by the ALVDN, active power is selected as the electric volume for hierarchical topology identification. Secondly, considering the abnormal fluctuation of active power caused by bidirectional power flow characteristics of distributed energy users, a user attribution model based on the Elastic-Net regression algorithm is proposed. Subsequently, based on the user identification results, the logic knowledge reflecting the hierarchical topology of the ALVDN is extracted by the AMIE algorithm, and the “transformer-phase-line-user” hierarchical topology of the ALVDN is deduced by a knowledge reasoning model. Finally, the effectiveness of the proposed method is verified by an IEEE example.

Voltage Support With PV Inverters in Low-Voltage Distribution Networks: An Overview

Large solar photovoltaic (PV) penetration using inverters in low voltage (LV) distribution networks may pose several challenges, such as reverse power flow and voltage rise situations. These challenges will eventually force grid operators to carry out grid reinforcement to ensure continued safe and reliable operations. However, smart inverters with reactive power control capability enable PV systems to support voltage quality in the distribution network better. This paper gives an overview of the current state-of-the-art control strategies for handling voltage problems through PV inverters and other devices. In addition, the (control) technical issues of PV systems integrated into the LV distribution network are considered from a control point of view. By comparing the control issues of PV integration into the grid, the paper aims to help distribution system operators to expand the volume of PV generation in the distribution system in an efficient and safe manner. Additionally, it will help control engineers and researchers select proper control strategies for PV systems as well as other distributed renewable sources.

Direct load control-based optimal scheduling strategy for demand response of air-conditioning systems in rural building complex

In recent years, the abrupt surge in electricity consumption for air-conditioning (AC) in rural areas has exacerbated the rural power supply and demand imbalance, presenting a significant challenge to the reliability and stability of power provision in low-voltage distribution networks. Implementing power demand response (DR) in buildings is recognized as an effective solution to address the grid's power imbalance and reliability issues. Among various building DR strategies, direct load control (DLC) stands out as a proficient approach to rapidly curtail building AC loads. However, applying this method solely to individual rural buildings proves less effective in peak regulation (PR). To fully exploit the DR potential of AC systems in rural building complex, it is essential to scale up the application of DLC. Consequently, a DLC-based DR optimal scheduling strategy is proposed for rural building complex AC systems. This strategy aims to meet PR demand and maximize the benefits for load aggregator. It establishes four DLC methods and differential compensation mechanisms, taking into account the varying thermal comfort requirements of AC users. The DR optimization model is solved using a genetic algorithm (GA), with PR demand derived from long and short-term memory neural network predictions. To validate the proposed strategy, a simulation study is conducted on a 200-unit farmhouse AC system, utilizing a typical rural soil source heat pump system in Xi'an, Shaanxi Province. The results demonstrate that the strategy surpasses the PR target by approximately 6.65% during the DR period, while ensuring a minimum level of thermal comfort for all user categories. Additionally, it yields a PR benefit of 88.73 yuan per day for all users, thereby promoting the adoption of clean energy in rural areas.

Feasibility Study of Three-Phase Modular Converter for Dual-Purpose Application in DC and AC Microgrids

The modern concept of a universal converter is intended as a power converter suitable for application in both dc or ac grids using the same external connectors. This novel family was recently proposed to allow an easier integration of renewable energy sources and energy storage systems, interfacing with dc/ac grids and/or loads with a minimum redundancy of power switches and passive elements. This kind of solution and applications are expected to be a reality in the nearest decade, as ac and dc low voltage distribution networks will coexist. Nevertheless, the initial solutions proposed as universal converter were focused on ac single-phase power conversion systems. In this sense, this article proposes and describes a new member of the universal converter family suitable for dc to dc and dc to ac power conversion (both in three-phase three-wire and in three-phase four-wire). The proposed power topology is derived as a modular extension from the single buck-boost bidirectional cell. Its main operation modes (buck and boost) are discussed, and a pulse-width-modulation technique is developed to generate the corresponding switching patterns. The proposed solution is successfully validated in open-loop mode both in simulation and experimentally with a laboratory prototype. The measured efficiency of the power converter was above 97% in the dc to ac mode and around 99% in the dc to dc mode.

Control strategy for flexible interconnection device considering transformer power losses

This paper proposes a flexible interconnection device control strategy. It takes transformer power loss into account to solve the problems of voltage overrun, station load imbalance, and unbalanced transformer capacity allocation that occur after the large-scale connection of distributed power sources into the low-voltage distribution network to realize the mutual aid of active power and load balancing between interconnected stations. Firstly, the VSC control strategy with capacitor current feedback active damping link is introduced to accurately execute the power commands and maintain the DC bus voltage stability. Then, the load regulation strategy with transformer power loss is proposed to generate the power commands of the flexible interconnection device. Finally, the simulation results show that the control strategy of the flexible interconnection device with transformer power loss mentioned in this paper can flexibly control the power transmitted by each port and maintain the bus voltage stability. The simulation results show that the flexible interconnection device control strategy proposed in this paper, which takes into account the transformer power loss, can flexibly control the transmission power at each port to keep the bus voltage stable. Moreover, it can effectively reduce system loss compared with the traditional load regulation strategy.

Three-phase voltage sensitivity estimation and its application to topology identification in low-voltage distribution networks

This paper aims to estimate the three-phase voltage sensitivity matrix and the network topology of a low-voltage distribution network from smart meter data, which measures voltage magnitude, current magnitude, and power factor with a lead/lag indicator. The targeted networks are three-phase networks with single-phase loads. Understanding network sensitivity and topology is crucial for fault detection, unmetered load identification, and addressing Dynamic Operating Envelope problems. The problem is formulated as a constrained optimization problem to estimate both the three-phase voltage sensitivity and the low-voltage transformer voltage. The estimated voltage sensitivity is used to further identify the network topology, by implementing an enhanced Recursive-Grouping and Backtracking algorithm, as well as a candidate topology selection technique. The proposed method is tested on the 55-node European feeder and several synthetic networks. Compared to the state-of-the-art, the results show a four-fold improvement in the accuracy of voltage sensitivity estimation and substantially fewer mistakes in the topology estimates. The result underscores the efficacy of a three-phase network model and voltage angle approximation in enhancing estimation accuracy.

Establishing a hierarchical local market structure using multi-cut Benders decomposition

Local electricity markets (LEMs) such as peer-to-peer (P2P) and community-based markets allow prosumers and consumers to exchange electricity products and services locally. In order to coordinate electricity trading and flexibility services, this paper proposes a hierarchical prosumer-centric market framework with a hybrid LEM and a local flexibility market (LFM). Multi-cut Benders decomposition (MCBD) is employed to decompose the integrated hybrid LEM into a centralized P2P market and multiple community-based markets. The aggregators coordinate energy sources and demands of households in low voltage (LV) distribution networks (DN) as virtual power plants (VPPs) and engage in trading through a P2P market over the medium voltage (MV) DN. In addition, a modified MCBD (M-MCBD) approach is proposed to accelerate the convergence process. The LFM is operated by the distribution system operator (DSO) and is formulated as a mixed-integer nonlinear programming (MINLP) problem which is further relaxed to a mixed-integer second-order cone programming (MI-SOCP) problem. The case study demonstrates that aggregators were able to collaborate on trading within the hybrid LEM to minimize the costs incurred by prosumers within the network. Furthermore, the proposed M-MCBD method improves the scalability of the MCBD by enhancing its convergence speed and accuracy, as demonstrated by testing on problems of varying scales.

Influence of the Supply Voltage Variation on the Conducted Emission in the Frequency Range up to 150 kHz Injected into the Power Grid by CFL and LED Lamps—Case Study

In normal operating conditions, the mains voltage level provided by low-voltage distribution networks follows time-varying patterns within permissible limits. The statistical characterisation of disturbances inserted into the power grid by nonlinear electrical devices is useful since operators are able to establish power quality limits and assess the residual capacity of networks for new smart installations, which may include a multitude of power electronic devices. Existing standards related to emission tests recommend using a power supply source in the measurement circuit with a nominal voltage parameter. However, the range of permissible variations of relevant supply voltage parameters may have an impact on conducted emissions. It can also be considered that the symptoms of supply voltage variations may manifest themselves differently depending on the equipment’s architecture and also the range of frequency observation with reference to harmonics and supraharmonics. The purpose of this study is to measure and analyze non-intentional conducted emissions injected into the power grid by lighting devices, methodically, by numbers of studies under different supply voltage conditions within two frequency bands. The emission is evaluated separately in two ranges: up to 2 kHz and 9-150 kHz. Obtained results show that the level of conducted emission introduced into the low voltage network by modern lighting sources like fluorescent and LED lamps varies with the supply voltage level compared to the emission generated in the nominal voltage level condition. Additionally, in the case of a spectrum of higher frequencies, some trends of shifting of the characteristic frequency bands are recorded. The potential of the obtained results is to complement the knowledge of the emission of modern lighting sources, which can be further used for digital models of these devices and to estimate the impact on the grid under real working conditions.

Optimization of low voltage distribution network configuration using forecasts based on Advanced Metering Infrastructure data

Optimization of low voltage distribution network configuration using forecasts based on Advanced Metering Infrastructure data

Improved topology identification for distribution network with relatively balanced power supplies

AbstractHaving correct distribution network topology information is essential for system state estimation, line loss analysis, electricity theft detection and fault location. At present, with continuous deployment of smart sensors, a large amount of monitoring data is collected, which enables refined management for distribution network. A data‐driven low voltage (LV) distribution network topology identification method is proposed, which realises transformer‐customer pairing and customer phase identification for distribution network with relatively balanced power supplies. Firstly, an integrated similarity coefficient of voltage curve is proposed, which can reflect the neighbourhood relationship within stations while increase the distinction between stations; the K‐Nearest Neighbour (KNN) algorithm is used to propagate the service transformer labels to complete transformer‐customer association. Then, the influence of power fluctuation on voltage curve is analysed and a dynamic sliding window model is adopted to search for voltage segments with significantly difference among three phase feeders to formulate a voltage time series to identify customer phase. Finally, the results are corrected and verified based on the principle of network power balance. The proposed algorithm is tested in two different real substations in China and Europe and shows high accuracy and robustness especially in distribution network with relatively balanced power supplies.

Design and Implementation of Single-Phase Grid-Connected Low-Voltage Battery Inverter for Residential Applications

Integrating residential energy storage and solar photovoltaic power generation into low-voltage distribution networks is a pathway to energy self-sufficiency. This paper elaborates on designing and implementing a 3 kW single-phase grid-connected battery inverter to integrate a 51.2-V lithium iron phosphate battery pack with a 220 V 50 Hz grid. The prototyped inverter consists of an LCL-filtered voltage source converter (VSC) and a dual active bridge (DAB) DC-DC converter, both operated at a switching frequency of 20 kHz. The VSC adopted a fast DC bus voltage control strategy with a unified current harmonic mitigation. Meanwhile, the DAB DC-DC converter employed a proportional-integral regulator to control the average battery current with a dynamic DC offset mitigation of the medium-frequency transformer’s currents embedded in the single-phase shift modulation scheme. The control schemes of the two converters were implemented on a 32-bit TMS320F280049C microcontroller in the same interrupt service routine. This work presents a synchronization technique between the switching signal generation of the two converters and the sampling of analog signals for the control system. The prototyped inverter had an efficiency better than 90% and a total harmonic distortion in the grid current smaller than 1.5% at the battery power of ±1.5 kW.

A framework for prosumer-centric peer-to-peer energy trading using network-secure export–import limits

This paper presents a new prosumer-centric peer-to-peer (P2P) energy trading framework incorporating the network-secure export–import limits, known as dynamic operating envelope (DOE), for prosumers considering their preferences to trade in the local market. In the proposed framework, a DOE-integrated energy management algorithm is developed for prosumers considering electric vehicles (EVs) charge–discharge and photovoltaic (PV) systems. The framework allows prosumers to operate within defined DOE, aligning with the preferences of EV-owning prosumers by managing the desired EV battery charge levels at their departure. An auction-based P2P energy trading mechanism for prosumers is proposed incorporating the allocated DOE to ensure secure network operation and valuing prosumers’ preferences to benefit them. The proposed framework is implemented on a 78-node low-voltage (LV) residential distribution network to evaluate its performance. The results and comparative analyses verify the effectiveness of the proposed framework in addressing network issues such as voltage and line congestion problems, meeting prosumers’ preferences in asset management, and providing benefits to all participants in the energy trading process.

EV Integration-Oriented DC Conversion of AC Low-Voltage Distribution Networks and the Associated Adaptive Control Strategy

Driven by carbon-neutral targets and transportation electrification, the widespread use of electric vehicles (EVs) has become an irreversible trend. However, low-voltage distribution networks (LVDNs) can face several challenges under the high-penetration EV integration, including overloads, voltage violations and unbalance issues. In this paper, an EV integration-oriented DC conversion scheme and the associated adaptive control method for LVDNs is proposed, aiming at releasing more capacity for EVs and collaborative improvement of the above issues caused by EV charging. Based on analyzing the influence of different charging piles connected to the AC and DC LVDNs, a DC conversion scheme for three-phase four-wire LVDNs under high-penetration EVs is proposed. The voltage source converter (VSC) control strategies aimed at alleviating the overload of the DTs, voltage violations, and three-phase unbalance are designed separately based on the modified three-phase four-wire voltage sensitivity. Then, a coordinated adaptive control strategy of on-load tap changer and VSCs is proposed considering the simultaneous occurrence of multiple power quality issues in hybrid AC/DC LVDNs. Case study verifies the effectiveness of the proposed DC conversion and adaptive control methods, by which the maximum EV penetration of the hybrid AC/DC LVDN is increased from 85% to 215% compared with the AC LVDN.