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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.

This research investigated the increases of the voltage profile on the Provincial Electricity Authority (PEA)’s low voltage (LV) network due to the solar photovoltaic (PV) penetration. This study proposed the solution to maintain the voltage profile within the PEA’s standard limitation by using battery energy storage system (BESS) application. The algorithm using bisection method to figure out the optimal size and location of BESS was examined and simulated in different scenarios such as summer/winter and weekend/weekday behaviors. Furthermore, the allocation of a battery in various locations was also considered. DIgSILENT power factory with DPL script and Python are the tools used to cover diverse scenario cases. The results showed that the best practice of how to implement BESS to solve the voltage rise problem was the BESS installation at the distribution transformer and the BESS installation separately at the end of each feeder near the loads. However, the optimal size of BESS installation at the distribution transformer was almost double that of installation at the end of each feeder.

A record amount of renewable energy has been added to global electricity generation in recent years. Among the renewable energy sources, solar photovoltaic (PV) is the most popular energy source integrated into low voltage distribution networks. However, the voltage limits and current-carrying capacity of the conductors become a barrier to maximizing the PV-hosting capacity in low voltage distribution networks. This paper presents an optimization approach to maximize the PV-hosting capacity in order to fully utilize the existing low voltage distribution network assets. To achieve the maximum PV-hosting capacity of the network, a novel method based on the dynamic line rating of the low voltage distribution network, the coordinated operation of voltage control methods and the PV re-phasing technique was introduced and validated using a case study. The results show that the proposed methodology can enhance the PV-hosting capacity by 53.5% when compared to existing practices.

Photovoltaic (PV) system installation has encouraged to be further expedited to minimize climate change and thus, rooftop solar PV systems have been sparkled in every corner of the world. However, due to technological constraints linked to voltage and currents, the PV hosting capacity has been substantially constrained. Therefore, this paper proposes a competent approach to maximize PV hosting capacity in a low voltage distribution network based on voltage control and dynamic line rating of the cables. Coordinated voltage control is applied with an on-load tap changing transformer, and reactive power compensation and active power curtailment of PV inverters. A case study with probabilistic and deterministic assessments is carried out on a real Sri Lankan network to show how the PV hosting capacity is constrained. The findings revealed the capability of integrated voltage control schemes and dynamic line rating in maximizing hosting capacity. The study is expanded by incorporating the PV rephasing approach in conjunction with the aforementioned control techniques, and the effectiveness of PV-rephasing is clearly demonstrated. When compared to voltage control and conductor static rating, the combined rephasing, voltage control, and DLR yielded a 60% increase in PV hosting capacity.

Electric vehicles’ (EVs) technology is currently emerging as an alternative of traditional Internal Combustion Engine (ICE) vehicles. EVs have been treated as an efficient way for decreasing the production of harmful greenhouse gasses and saving the depleting natural oil reserve. The modern power system tends to be more sustainable with the support of electric vehicles (EVs). However, there have been serious concerns about the network’s safe and reliable operation due to the increasing penetration of EVs into the electric grid. Random or uncoordinated charging activities cause performance degradations and overloading of the network asset. This paper proposes an Optimal Charging Starting Time (OCST)-based coordinated charging algorithm for unplanned EVs’ arrival in a low voltage residential distribution network to minimize the network power losses. A time-of-use (ToU) tariff scheme is used to make the charging course more cost effective. The concept of OCST takes the departure time of EVs into account and schedules the overnight charging event in such a way that minimum network losses are obtained, and EV customers take more advantages of cost-effective tariff zones of ToU scheme. An optimal solution is obtained by employing Binary Evolutionary Programming (BEP). The proposed algorithm is tested on IEEE-31 bus distribution system connected to numerous low voltage residential feeders populated with different EVs’ penetration levels. The results obtained from the coordinated EV charging without OCST are compared with those employing the concept of OCST. The results verify that incorporation of OCST can significantly reduce network power losses, improve system voltage profile and can give more benefits to the EV customers by accommodating them into low-tariff zones.

The energy transition poses a challenge for the electricity distribution network design as new energy technologies cause increasing and uncertain network loads. Traditional static load models cannot cope with the stochastic nature of this new technology adoption. Furthermore, traditional nonlinear power methods have difficulty evaluating very large networks with millions of cables, because they are computationally expensive. This paper proposes a method which uses copulas for modeling the uncertainty of technology adoption and load profiles, and combines it with a fast linear load flow model. The copulas are able to accurately model the stochastic behavior of solar irradiance, load measurements, and mobility data, converting them into electricity load profiles. The linear load flow model has better scalability and stability compared to traditional load flow models. The models are applied to a case study which uses a real-world dataset consisting of a realistic technology adoption scenario and a low-voltage network with millions of cables, which considers both voltage and current problems. Results show that risk profiles can be generated for all cables in the network, resulting in a valuable map for the district network operator as to where to focus their efforts.

This paper proposes a local control strategy applied in the soft open point (SOP) to suppress voltage fluctuation when adding a renewable energy source into the system. The mathematic model of the grid connected to SOP is established based on the characteristics of a low-voltage distribution network. Combined with the mathematic model and local voltage information, the local control strategy is proposed to optimize the active and reactive power distribution and consume the minimum apparent power of the converter. The local control strategy can effectively suppress the voltage fluctuation caused by renewable energy access, which was testified by MATLAB/Simulink simulation. In addition, the local control strategy can deduce the communication resource and increase the response speed compared to global optimization. This paper is meaningful for renewable energy source distribution and voltage balance in low-voltage distribution systems.

Line loss rate plays an essential role in evaluating the economic operation of power systems. However, in a low voltage (LV) distribution network, calculating line loss rate has become more cumbersome due to poor configuration of the measuring and detecting device, the difficulty in collecting operational data, and the excessive number of components and nodes. Most previous studies mainly focused on the approaches to calculate or predict line loss rate, but rarely involve the evaluation of the prediction results. In this paper, we propose an approach based on a gradient boosting decision tree (GBDT), to predict line loss rate. GBDT inherits the advantages of both statistical models and AI approaches, and can identify the complex and nonlinear relationship while computing the relative importance among variables. An empirical study on a data set in a city demonstrates that our proposed approach performs well in predicting line loss rate, given a large number of unlabeled examples. Experiments and analysis also confirmed the effectiveness of our proposed approach in anomaly detection and practical project management.

Many researchers in recent years have studied voltage deviation issues in distribution networks. Characterizing the impedance between consuming nodes in a network is the key to controlling the network voltage. Existing impedance estimation methods are faced with three challenges: time synchronized measurement, a generalization of the network model, and convergence of the optimization for objective functions. This paper extends an existing impedance estimation algorithm by introducing an enhanced particle swarm optimization (PSO). To overcome this method’s local optimum problem, we propose adaptive inertia weights. Also, our proposed method is based on a new general model for a low voltage distribution network with non-synchronized measurements. In the case study, the improved impedance estimation algorithm realizes better accuracy than the existing method.

Expansion of photovoltaic (PV) generation is increasing the challenge for network operators to keep voltages within operational limits. Voltage rise occurs in low voltage (LV) networks when distributed generators export, particularly at times of low demand. However, there is little work quantifying the scale of voltage issues and subsequently potential solutions across large numbers of real networks. In this paper, a method is presented to analyse a large quantity of geographically and topographically varying distribution networks. The impact of PV on voltages in 9163 real LV distribution networks is then quantified. One potential mitigation measure is increased network demand to reduce voltages. In this work, location algorithms are used to identify where increased demand, through energy storage, has the greatest effect on overvoltage. The study explores the impact on overvoltage of two modes of storage installation reflecting differing routes to adoption: purchase of storage by homeowners and purchase by network operators. These scenarios are compared with traditional re-conductoring in the 9163 networks. It is shown that to avoid violation of absolute voltage limits, storage should be installed at strategically important locations. Storage in homes reduces overvoltage, offering clear benefits to the network operator, but very wide deployment is required to completely remove the need for reinforcement.