Search

Local Electricity Market (LEM) appears as a promising consumer-centric market-based approach that extends the self-consumption method, widely implemented in residential households, to collective self-consumption in the local energy communities, enabled through peer-to-peer (P2P) transactions. To facilitate the integration of LEM in the wholesale electricity market (WEM), it is paramount to comprehend the synergy of retail electricity pricing on the LEM operation hosted in the low-voltage distribution network (LVDN). The paper presents a co-simulation framework consisting of a local electricity market model coupled with a three-phase distribution network simulator to perform a holistic case study for a smart energy community in Ireland. The novel contribution of the work is to explore the potential of local electricity trading in the presence of residential energy storage (ES), under different retail pricing schemes existent in Ireland, by evaluating economic benefits to the energy community and network performance of three-phase LVDN. Extensive simulation studies indicate that the presence of residential ES significantly boosts P2P transactions under static time-of-use (SToU) pricing. These P2P transactions are primarily contributed by energy arbitrage (among customers in LEM) in the winter and surplus PV-generated electricity in the summer. On the other hand, the scheduling of ES under SToU pricing deteriorates the network performance of LVDN in winter, showing the highest active power loss and under-voltage scenario among all the cases. Another unique aspect of LVDN is the voltage unbalance studied and found to be highly correlated with ES operation under SToU pricing. Recommendations have been made to the relevant stakeholders and market actors, identifying key aspects necessary to roll out the LEM under retail electricity pricing schemes.

With the large number of renewable energy power sources such as wind power and photovoltaic power connected to the grid, the power system is becoming more electronic. The fault characteristics of renewable energy power sources and synchronous generators are obviously different, mainly manifested as the characteristics of short circuit current amplitude limitation and frequency offset, etc. The traditional protection based on the fault characteristics of synchronous generators may have adaptability problems. For a high proportion of renewable energy system, the short circuit current in the low voltage distribution network is greatly affected by the operation state of the renewable energy power sources after the short circuit fault occurs, which leads to the difficulty of the traditional three-stage overcurrent protection setting and coordination. In order to ensure the safe and stable operation of the system, this paper proposes a new backup protection method for low voltage distribution network. This method uses the information of the electric parameters on the high voltage side and low voltage side of the transformer. Firstly, the modulus sum of the voltage sampling value, the equivalent distance between the fault point and the protection installation place, and the modulus sum of the current sampling value are calculated. Secondly, the direction is judged by the criterion of the voltage direction element and the criterion of the distance protection element based on the multi-interval information. Finally, the maximum current branch of the low voltage distribution network is selected as the fault branch by the overcurrent element based on multi-interval information, so as to trigger the circuit breaker trip and isolate the fault. The low voltage distribution network model of the high proportion of renewable energy system is built on the PSCAD/EMTDC electromagnetic simulation platform. The simulation results show that the proposed protection method can reliably achieve fault removal by using multi-interval information, and has strong adaptability to the high proportion of renewable energy system.

The increased penetration of smart meters in the low voltage distribution network provides valuable insights that distribution network operators can leverage to produce detailed analytics for decision making. This includes enhanced network observability that can support network planning, phase balancing and planned outage management. In this paper, we propose three unsupervised learning algorithms to model the problem of identifying the transformer–customer connectivity relation at the low voltage network using smart meter voltage measurements of residential units as a classification problem. The algorithms are tested on real-world smart meter datasets and can detect the correct transformer–customer connectivity with 95% to 100% accuracy. This allows distribution network operators to effectively detect inconsistencies between customer data reports and actual physical connection of residential units to distribution transformers to comply with legal regulations.

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.

The use of photovoltaic reactive power and energy storage active power can solve the problems of voltage violation, network loss, and three-phase unbalance caused by photovoltaic connection to low-voltage distribution networks. However, the three-phase four-wire structure of the low-voltage distribution network brings difficulties to power flow calculation. In order to achieve photovoltaic utilization through optimal power flow, a photovoltaic-energy storage collaborative control method for low-voltage distribution networks based on the optimal power flow of a three-phase four-wire system is proposed. Considering the amplitude and phase angle of voltage and current, a three-phase four-wire node admittance matrix was used to establish the network topology of the low-voltage distribution network. Also, to minimize the network loss, the three-phase unbalance and voltage deviation. a multi-objective optimization model based on three-phase four-wire network topology was established considering the voltage constraints, reverse power flow constraints and neutral line current constraints. Through improving the node admittance matrix and model convexity, the complexity of solving the problem is reduced. The CPLEX algorithm package was used to solve the problem. Based on a 21-bus three-phase four-wire low-voltage distribution network, a 24-h multi-period simulation was undertaken to verify the feasibility and effectiveness of the proposed scheme.

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.