Secondary Substations Research Articles

Analysis of Underground Distribution System Models for Secondary Substations

Analysis of Underground Distribution System Models for Secondary Substations

Disaggregation Model: A Novel Methodology to Estimate Customers’ Profiles in a Low-Voltage Distribution Grid Equipped with Smart Meters

In the context of increasingly necessary energy transition, the precise modeling of profiles for low-voltage (LV) network consumers is crucial to enhance hosting capacity. Typically, load curves for these consumers are estimated through measurement campaigns conducted by Distribution System Operators (DSOs) for a representative subset of customers or through the aggregation of load curves from household appliances within a residence. With the instrumentation of smart meters becoming more common, a new approach to modeling profiles for residential customers is proposed to make the most of the measurements from these meters. The disaggregation model estimates the load profile of customers on a low-voltage network by disaggregating the load curve measured at the secondary substation level. By utilizing only the maximum power measured by Linky smart meters, along with the load curve of the secondary substation, this model can estimate the daily profile of customers. For 48 secondary substations in our dataset, the model obtained an average symmetric mean average percentage error (SMAPE) error of 4.91% in reconstructing the load curve of the secondary substation from the curves disaggregated by the model. This methodology can allow for an estimation of the daily consumption behaviors of the low-voltage customers. In this way, we can safely envision solutions that enhance the grid hosting capacity.

Analysis of PRIME PLC Smart Metering Networks Performance

This paper describes the process followed to assess the performance of Smart Metering PLC networks based on PRIME specification. This process is composed of a monitoring phase dedicated to obtain data from the network (Communication level and Application level), and an analysis phase which produces a standardized report for each Secondary Substation with the relevant performance data correlated with electricity grid information. The paper includes also a section identifying different specific abnormal behaviours in PLC Smart Meters, working in the grid.

Impact of OLTC equipped transformer operation on PV installation in urban distribution network

Presented paper addresses the problem of proper large-scale integration of photovoltaic (PV) systems in urban area distribution network (DN). A methodology for minimization of annual energy losses in DN by optimal placement of PV systems, which simultaneously considers rooftop PV potential and time dependent network operation, is presented. Optimal reactive power generation of PV systems and optimal setting of on-load tap changer (OLTC) equipped transformer in secondary substation are determined as well. Furthermore, proposed methodology enables evaluation of the impact of OLTC equipped transformer on the share of PV installation that urban DN can accommodate. Proposed methodology is based on the optimization tool called differential evolution, with the objective function set to minimize network’s annual energy losses, while preventing the voltage violations and thermal overloading of lines. Results of the case study performed on a real urban low voltage distribution network, with consideration of different scenarios of OLTC operation are presented. The results indicate that simultaneous consideration of chosen variables yields greater benefits to DN operation in terms of reduction of annual energy losses and greater PV penetration.

Deep Neural Network-Based Autonomous Voltage Control for Power Distribution Networks with DGs and EVs

This paper makes use of machine learning as a tool for voltage regulation in distribution networks that contain electric vehicles and a large production from distributed generation. The methods of voltage regulation considered in this study are electronic on-load tap changers and line voltage regulators. The analyzed study-case represents a real-life feeder which operates at 10 kV. It has 9 photovoltaic systems with various peak installed powers, 2 electric vehicle charging stations, and 41 secondary substations, each with an equivalent load. Measurement data of loads and irradiation data of photovoltaic systems were collected hourly for two years. Those data are used as inputs in the feeder’s model in DigSilent PowerFactory where Quasi-Dynamic simulations are run. That will provide the correct tap positions as outputs. These inputs and outputs will then serve to train a Deep Neural Network which later will be used to predict the correct tap positions on input data it has not seen before. Results show that ML in general and DNN specifically show usefulness and robustness in predicting correct tap positions with very small computational requirements.

An enhanced CNN-LSTM based multi-stage framework for PV and load short-term forecasting: DSO scenarios

The importance of accurate forecasting in the electric sector has grown due to the increasing demand and adoption of high volume of Renewable Energy Sources (RES). Short-term forecasting (STF) using deep learning methods has shown potential for improving forecasting accuracy. However, the accuracy of these methods can be further enhanced by combining them to generate a hybrid model, selecting appropriate input features, generating new features, and optimizing model parameters. This paper proposes a novel multi-stage framework for PV and load STF that employs feature generation, feature selection, and optimal hyperparameter tuning preprocessing techniques. An enhanced hybrid CNN-LSTM deep learning model architecture is developed in the final stage of the proposed framework. The framework is assessed and compared to other leading-edge approaches across different DSO scenarios, including multiple single-phase residential loads, three-phase feeders, and secondary substation, demonstrating a significant reduction in forecasting errors.

Developing a public EV charging infrastructure in a DSO's perspective: Hosting capacity assessment and grid reinforcements on a real case study

The widespread increase of electric vehicles sales forces Distribution System Operators to evaluate and improve the capability of the distribution system to host distributed charging infrastructures. This paper suggests a procedure to identify grid reinforcements to increase the capability of the system as well as to evaluate the current hosting capacity. The developed procedure is based on a micro-genetic algorithm and its solution identifies the optimal grid reinforcements to be planned on a Medium Voltage network in order to increase the number of charging stations that can be connected. The procedure requires the model of the network, the yearly load profiles of the secondary substations and the algorithm output takes into account an envisioned budget to be spent, avoiding network violations and an iniquitous distribution of the charging stations. The procedure was firstly validated considering a 70 nodes test network. Furthermore, the real case study of the distribution system of the city of Terni was assessed focusing on one of its main subnetwork with 471 nodes; the procedure was exploited to assess the current hosting capacity and to estimate that on average 0.01 M€ has to be spent on grid reinforcements in order to further increase hosting capacity.

EV smart charging: How tariff selection influences grid stress and carbon reduction

With the rapid increase in ownership of Electric Vehicles (EVs), widespread concern has been raised regarding the potential for EV charging demand to overload electricity grids. Smart control of charging is advocated as a solution, gaining attention from business and support from policymakers. However, the ultimate grid benefits (or disbenefits) of smart charging will follow from a combination of user behaviour and pricing arrangements / tariffs. Local clustering of vehicle uptake can lead to unintended consequences as national incentives fail to align with local pressures. In this paper, we describe a simulation of the dynamic electricity demand pattern arising from a fleet of grid connected EVs. The model developed for this study combines stochastic sampling of data from a UK-based smart charging trial (Western Power Distribution’s Electric Nation project) with a set of plausible tariffs, including a strategy which specifically seeks to minimize grid carbon emissions. This provides insights into the potential impacts of EV charging by encompassing a wider range of tariffs than previously assessed, while also separating the control actions of optimising cost and managing capacity. We examine the carbon implications of tariff choice and introduce a range of grid overload metrics that reveal nuances in the tariff implications and evolution of impacts as EV penetration increases. The results show that smart charging is not necessarily a better solution for the grid compared to on-demand charging. Stepwise tariffs, currently favoured by UK energy suppliers, present a particular risk. Such tariffs can tend to increase load synchronization by shifting load towards periods where more cars are connected and awaiting charge. This can lead to an increased peak load even at moderate EV uptake levels. Dynamic tariffs proved preferable but still increase peak demand at higher vehicle uptakes. All smart tariffs offer a strong carbon benefit, but, again, current stepwise tariffs are failing to realise the full potential that could be realized by targeting low carbon time periods. Separate local capacity management was able to eliminate overload at the secondary substation, even with very high EV uptake, with only rare, very small levels of unserved demand.

Cost efficient management of digital secondary substations, on the example of the process interface and detection unit (PIDU)

Cost efficient management of digital secondary substations, on the example of the process interface and detection unit (PIDU)

Asset management prepared smart secondary substation

Asset management prepared smart secondary substation

Smart secondary substation development and demonstration under FLEXIGRID project

Smart secondary substation development and demonstration under FLEXIGRID project

“End-To-End Testing” of Enedis' Smart Equipment for Secondary Substations

“End-To-End Testing” of Enedis' Smart Equipment for Secondary Substations

A study on the application of power electronics technology in secondary substation to improve power quality

A study on the application of power electronics technology in secondary substation to improve power quality

Evaluation of the autonomous and controllable degree of secondary substation equipment based on fuzzy comprehensive evaluation method

The demand for autonomy and controllability of substations is increasing, and there is still a lack of specific methods to evaluate the degree of autonomy and controllability of secondary substation equipment. Given the above situation, firstly, the principle of an autonomous and controllable evaluation system is established. On this basis, the factors affecting the autonomous and controllable level of equipment are analyzed in depth, and the specific evaluation indicators of the autonomous and controllable degree of secondary equipment are determined. Then the fuzzy comprehensive evaluation method is used to establish the evaluation model of the degree of autonomy and controllability of secondary substation equipment. Finally, the specific examples are evaluated. The results show that the autonomous and controllable degree of the equipment has indeed increased after the autonomous and controllable improvement, which verifies the accuracy of the evaluation model. The evaluation model can also provide direction and guidance for the autonomous and controllable improvement of secondary equipment in substations.

Wind Forecast at Medium Voltage Distribution Networks

Due to the intermittent and variable nature of wind, Wind Power Generation Forecast (WPGF) has become an essential task for power system operators who are looking for reliable wind penetration into the electric grid. Since there is a need to forecast wind power generation accurately, the main contribution of this paper is the development, implementation, and comparison of WPGF methods in a framework to be used by distribution system operators (DSOs). The methodology applied comprised five stages: pre-processing, feature selection, forecasting models, post-processing, and validation, using the historical wind power generation data (measured at secondary substations) of 20 wind farms connected to the medium voltage (MV) distribution network in Portugal. After comparing the accuracy of eight different models in terms of their relative root mean square error (RRMSE), extreme gradient boosting (XGBOOST) appeared as the best-suited forecasting method for wind power generation. The best average RRMSE achieved by the proposed XGBOOST model for 1-year training (January–December of 2020) and 6 months forecast (January–June of 2021) corresponds to 13.48%, outperforming the predictions of the Portuguese DSO by 20%.

Protection study for SST-integrated LVDC networks with multiple feeders

To enhance the adaptability of low voltage (LV) networks and release additional network capacity towards enabling the wider uptake of low carbon technologies, SP Energy Networks’ (SPEN) LV Engine project aims to design and trial the first UK solid state transformer (SST) for deployment within secondary substations (11/0.4kV). This project aims to form the first LV direct current (LVDC) trial at a utility scale in the UK. The SST and the introduction of low voltage DC (LVDC) supplies will present fundamental changes in the operation of existing secondary substations and will introduce new LV fault profiles for the associated LVDC distribution networks. This paper presents the results of joint work between the University of Strathclyde, SPEN and WSP with the primary objectives of identifying suitable DC protection and fault discrimination approaches for LVDC networks with multiple feeders and DC loads. This work intends to reflect the challenges and potential solutions for LVDC protection.

Evaluation of residential demand response trials with smart heat pumps and batteries and their effect at the substation feeder

Residential demand response using low carbon technologies can potentially offer energy flexibility to the electricity network along with integration of renewable generation. This paper seeks to empirically evaluate the effectiveness of residential demand response trials on a low voltage feeder of a secondary substation in Barnsley (UK). The study used a sample of 14 well-insulated dwellings equipped with home batteries, heat pumps and solar photovoltaic systems coordinated using automated control. Statistical analysis was undertaken using time-series monitoring data obtained at the individual dwelling level, dwelling sample and at the feeder level of the local low voltage network. Resident experience of the trials was assessed through qualitative data obtained from household telephone surveys. Over three weeks of trials, daily demand response interventions of 2 h duration were applied to the sample of 14 dwellings. For evening peak times, the mean reduction in grid electricity import was found to be 1.3 kWh (67%) per dwelling for turn-down interventions which aimed to minimise import. For turn-up interventions between 1 and 3 pm, the mean increase in grid electricity import was found to be 5.8 kWh (645%) per dwelling. The effect of interventions was measured at the low voltage network level for two single-phase feeders, where penetration of trial homes was approximately one-third. A reduction in mean real power up to 21% was observed for turn-down interventions as well as an increase in real mean power up to 307% for turn-up interventions. In general, the trials had little effect on residents in terms of thermal comfort, hot water availability, noise disturbance or disruption to routines, and where such effects were noticed, they were broadly acceptable. The widespread implementation of residential demand response schemes will require increased roll-out of time-of-use tariffs, enhanced resident support and extensive monitoring of low voltage feeders in electricity substations.

False Data Injection Impact on High RES Power Systems with Centralized Voltage Regulation Architecture

The increasing penetration of distributed generation (DG) across power distribution networks (DNs) is forcing distribution system operators (DSOs) to improve the voltage regulation capabilities of the system. The increase in power flows due to the installation of renewable plants in unexpected zones of the distribution grid can affect the voltage profile, even causing interruptions at the secondary substations (SSs) with the voltage limit violation. At the same time, widespread cyberattacks across critical infrastructure raise new challenges in security and reliability for DSOs. This paper analyzes the impact of false data injection related to residential and non-residential customers on a centralized voltage regulation system, in which the DG is required to adapt the reactive power exchange with the grid according to the voltage profile. The centralized system estimates the distribution grid state according to the field data and provides the DG plants with a reactive power request to avoid voltage violations. A preliminary false data analysis in the context of the energy sector is carried out to build up a false data generator algorithm. Afterward, a configurable false data generator is developed and exploited. The false data injection is tested in the IEEE 118-bus system with an increasing DG penetration. The false data injection impact analysis highlights the need to increase the security framework of DSOs to avoid facing a relevant number of electricity interruptions.

Sensitivity assessment of a novel earth fault location method with optimally placed distributed measurements for MV networks

AbstractThis paper extends the study of a zero‐sequence impedance‐based fault location method (FLM) designed for medium voltage (MV) distribution feeders whose principle has been presented in a previous work Bach et al. This method is able to locate any type of earth fault with any type of neutral grounding inside a convex set of nodes forming the solution area. The total length of the lines inside this area is statistically computable a priori and depends only on the number and location of additional voltage measurements deployed on some remote end secondary substations. To be efficiently applied to the most challenging rural feeders presenting a lot of ramifications, simulations have shown a need for synchronized measurements, such as the ones obtained using phasor measurement units. Besides, this paper addresses the coupling potential of this method with more traditional ones such as Takagi‐based FLMs to enhance the performances of both, solving the multiple estimation problem while potentially locating the fault within one node. Finally, an extensive sensitivity analysis has been carried out, showing a good robustness with respect to impedance estimation errors or fault impedance values while showing the need for synchronization, especially on some remote measurement locations.

Countermeasures for reduction of screen currents due to cross country faults in MV cable distribution networks

• Cross-country faults can involve large currents liable to damage MV cable sheaths. • Methodologies to reduce cross-country fault currents in cable screens are presented. • Methods are compared by means of ATP-EMTP simulations on a 20 kV test network. • Additional ground wires are effective but viable only for newly built cable lines. This paper investigates the reduction of Cross-Country-Faults currents in medium voltage (MV) cable networks in order to decrease thermal stress of cable screens, especially inside joints. Four methods are analyzed: 1) disconnection of cable screens from the earth electrode of primary substation (PS); 2) buried ground wire laid near the cable lines and connected to each earth electrode of secondary substations (SSs) and PS; 3) buried ground wire and disconnection of the bonded cable screens from the earth electrode only at sending/receiving end of each cable line between two consecutive SSs; 4) cross bonding of cable screens between two consecutive SSs. Methods are analysed by ATP-EMTP simulations of a 150/20 kV radial test system formed by eight 5 km long cable lines equipped with 50 mm 2 copper conductors per phase and 16 mm 2 copper screen per cable. Compared to the limiting reactors countermeasure presented in a previous paper by the authors, which seems the solution less burdensome both technically and economically for existing lines, method 3 is more effective but its implementation is limited only to newly built lines, whereas the main drawback of method 1 is the increase of ground potential rise at fault locations.