Low Voltage Grid Research Articles

Development of PV hosting-capacity prediction method based on Markov Chain for high PV penetration with utility-scale battery storage on low-voltage grid

ABSTRACT The previous stochastic hosting capacity prediction method using the Monte Carlo method for high photovoltaic (PV) penetration with a battery energy storage system (BESS) required a large number of computations to achieve the expected accuracy. The problem of high computational load must be addressed so that the electrical distribution planner can practically use the PV hosting capacity prediction in actual situations. Therefore, this study developed a Markov-chain-based PV hosting capacity prediction method for high PV penetration using BESS. The proposed method is described in detail, followed by case and validation studies. The obtained hosting capacity was 123.58 kW, which increased to 3676.4 kW after the utility-scale BESS implementation. The results demonstrate that the proposed Markov-chain-based PV hosting capacity prediction method outperforms the Monte Carlo method, which is the most popular stochastic hosting capacity method, in terms of accuracy and computational cost.

Integrating thermal properties and degradation modelling of batteries into the scheduling of hybrid power plants with photovoltaics

Renewable energy penetration in the energy mix has risen sharply in the last decade, and brought with it an increased deployment of large-capacity storage systems. The result is a need for optimised management strategies that integrate and safely operate the whole energy production chain, including precise models for each element that they control. This paper presents the results of a study on detailed state-of-charge, thermal and degradation modelling of Battery Energy Storage Systems for hybrid photovoltaic-battery power plants. The objective is to quantify the error associated with not taking into account a precise model for these parameters in industrial applications. The use case of optimal battery scheduling in a capacity firming framework and a low-voltage grid is considered to quantify the impact of these models. The proposed models for these parameters were developed and then validated against experimental measures from five hybrid power plants. The models were then applied to calculate optimal battery schedules, and showed lower than expected state-of-health loss values. The impact of including the thermal modelling in the scheduling process was also observed to be weaker than expected.

Technico and Economic Optimization of the Injection of a Photovoltaic System on a Low Voltage Network

AbstractThis paper investigates an experimental study of photovoltaic PV modules behavior (SM110-24) connected to the low-voltage grid (230 V / 50 Hz) with a three-phase inverter. In order to ensure maximum power point tracking (MPPT), The input voltage is delivered via a DC/DC converter that is controlled by the Fuzzy Logic Technical FLC to ensure maximum power MPPT. The control system of the setup is designed for the extraction of maximum power. The primary aim of this investigation is to synchronize the two sources namely PV-grid, using the Phase Locked Loop PLL command with the injection of an alternating current into the network whose aim is stabilizing the system and minimizing the cost of the energy consumed by the load. In this study, the authors have taken into account the real kWh price of the Algerian society of distribution of electricity (Sonelgaz) as the basis for their research. The simulation results obtained by utilizing the MATLAB software show that the proposed system exhibits high performance and flexibility, indicating its potential usefulness for practical applications in the future.

The importance of battery storage systems in reducing grid issues in sector-coupled and renewable low-voltage grids

Transforming the energy system towards renewable energies and the electrification of the transport and heating sectors is necessary. A substantial part of this transformation occurs in the low-voltage grids. The increasing number of photovoltaic systems, electric vehicles and heat pumps poses new challenges for low-voltage grids. The load on lines and transformers is increasing due to higher electricity demand. Battery storage systems and the flexible operation of consumers can increase photovoltaic self-consumption and relieve low-voltage grids by using a grid-serving mode of operation and thus supplement grid expansion.We conducted time series-based load flow calculations using five representative low-voltage grids for four weeks of the year. We determined the loads and the need for curtailment caused by a high expansion of roof-mounted photovoltaic systems and new sector-coupled consumers and how these loads and curtailment can be reduced with the help of battery storage systems and flexible consumers.Our analyses show that consumption-related overloads can be completely avoided, and the share of locally consumed photovoltaic energy can be doubled. Digitalising the low-voltage grids is required to detect overloads and allow battery storage systems and consumers to react specifically to these grid overloads. Rural low-voltage grids are particularly affected by overloads, as they have greater rooftop photovoltaic potential and tend to be weaker. Battery storage systems and smart consumers are thus becoming essential cornerstones for the transformation of the energy system.

Computer-aided Optical Plasma Postprocessing Applied on Model Spark Gaps

Spark gaps are used as surge protective devices (SPD class 1) for low voltage grids protection against surge currents and overvoltages. For practical research of the narrow gap plasma of spark gaps, high-speed camera recordings are used in modified transparent test models. In this test setup, current densities of 1010 A/m2 are generated. In order to optimize and automate the evaluation process of camera recordings, an image analysis tool is developed further in this contribution. After basic image improvement and segmentation, this research optimizes a detection algorithm for plasma location and distribution. As a result, the known plasma distribution gives access to significantly more information about the plasma behaviour and the spatial distribution of radiation.

Fault Ride Through approach for Grid-Connected Photovoltaic System

This paper presents a fault ride-through approach for grid-connected photovoltaic (PV) systems, aimed at improving the system's response during voltage sags and limiting the maximum inverter current during symmetrical faults. A constant active current reactive power injection approach was developed for low-voltage ride-through (LVRT) operation of grid-connected solar PV inverters in low voltage grids. The method manages the active and reactive power references and satisfies grid code requirements while also addressing tripping problems caused by overcurrent. The approach improves the speed response of the power converters during voltage sags, resulting in better dynamic grid support. The authors evaluated the approach using a 2-kW single-phase grid-connected PV system and demonstrated its ability to inject the required reactive power during a fault to improve the voltage profile and achieve dynamic grid support requirements. The accuracy of the proposed methodology is identified to be 98.3%, and the detection time is reduced by a reasonable margin in accordance with the grid standards. Overall, the fault ride-through approach can enhance the performance and reliability of grid-connected PV systems, particularly in low voltage grids.

A comprehensive methodological workflow to maximize solar energy in low-voltage grids: A case study of vertical bifacial panels in Nordic conditions

The large-scale deployment of solar photovoltaic (PV) panels in residential and commercial buildings affects the local distribution grid. Voltage rises during times when PV production exceeds consumption can limit the hosting capacity of the distribution grid. This study presents a comprehensive methodological workflow that moves from the solar analysis of an ideal district to the identification of the PV hosting capacity of a distribution grid. The workflow aims to be highly flexible: the input parameters (i.e., PV technology, PV orientation, global horizontal solar irradiation, and grid properties) can be varied to simulate different cases and compare the corresponding hosting capacities. This workflow enables the analysis of the influences of PV systems’ properties and orientation, electricity consumption, and grid characteristics on a grid’s hosting capacity. A case study was conducted in which the IEEE European Low Voltage Test Grid was used, and conventionally mounted monofacial photovoltaic (MPV) panels were progressively replaced with east–west oriented, vertically mounted bifacial photovoltaic (VBPV) panels that can provide a better match with the electricity load and a higher daily total to daily peak electricity production ratio. The benefits of VBPV are highlighted at high-latitude locations, such as Nordic countries. Therefore, Turku, Finland, which is at a representative Nordic latitude considering the locations of the most important population centers, was chosen as the location for the case study. The results showed that applying VBPV increased a grid’s PV hosting capacity significantly, up to 46% with the optimal system (30% MPV and 70% VBPV).

Maximum Tolerated Number of Simultaneous BEV Charging Events in a Typical Low-Voltage Grid for Urban Residential Area

In this paper, we determine the maximum number of battery electric vehicles (BEVs) that can be charged simultaneously at full power during peak load hour without overloading transformer and lines or causing an unacceptable voltage drop in the low-voltage (LV) grid. In order to predict the BEVs charging demand, an application that takes into account the random user’s arrival time and the initial battery state of charge (SOC) was developed using the C++ programming language and the Qt toolkit. The network analysis was then carried out using the Quasi-Dynamic Simulation (QDS) toolbox in DIgSILENT PowerFactory on a typical German LV grid for a metropolitan urban area. The simulation findings indicate that the value of simultaneity factor (SF) plays an important role in identifying the most robust and weakest grid’s bottlenecks. There is currently no immediate threat of electromobility pushing the parameters of the grid to their unacceptable limits; however, it is essential to examine the LV grid’s bottlenecks and gradually prepare them for the ramp-up of BEVs. In the short term, the bottlenecks can be removed using conservative planning and operating principles; however, employing novel approaches will be crucial in the longer term.

Control method of an AC-AC MMC for open-end stator winding wind turbine generation system

With the increasing of power rating and voltage level of wind turbine, the traditaional low voltage converter is being replaced by multilevel converter. This paper proposes a new ac-ac modular multi-level converter (MMC) connecting the open-end stator winding permanent magnet synchronous generator (OW-PMSG) to AC power grid. The working principle of the proposed topology is analyzed. Under different grid conditions, a unified capacitor voltage balance control method and a current control method are proposed. Aiming at the low grid voltage ride through problem, a distributed braking chopper circuit and the corresponding control strategy is given. Simulation is conducted for verification. Compared with the back to back (BTB) MMC for OW-PMSG, the number of capacitors and arm inductors of the proposed topology are reduced by 43% and 33%. Moreover, the number of the state variables of the average capacitor voltages and currents of the proposed topology are reduced by 33% and 47%, reducing the control complexity. The proposed topology also has higher efficiency than the BTB MMC for OW-PMSG. Compared with the other ac-ac MMC topologies for OW-PMSG, the combined power rating of the power devices of the proposed topology is much lower and thus the proposed topology has higher power density.

Assessment of a single-phase single-stage grid-connected photovoltaic system

A grid-connected photovoltaic (GCPV) system plays an important role in the development of green technology. However, generated photovoltaic (PV) power that is injected into the grid causes instability in the grid system. Previous studies have shown that power quality for a three-phase system is thoroughly being discussed, but not for a single-phase system. Single-phase system also contribute to instability in low-voltage grid networks during high power penetration as majority of the single-phase GCPV nowadays are installed on rooftops. Thus, in this study, the power quality of a single-phase single-stage GCPV system is investigated based on the total harmonic distortion (THD), power factor (PF), and the generated active (P) and reactive (Q) power during different solar irradiance and varying loads conditions. A model of the GCPV system was developed in MATLAB/Simulink, so that certain variables can be varied to observe their effect towards the power quality. The results show that the current and voltage of generated PV power are synchronized and having low current THD which is less than 1% for all cases. PF values are also in the acceptable range in compliance to IEC 61727 standard which is 0.9971, and the generated PQ is in accordance to the connected load.

Intensive Data-Driven Model for Real-Time Observability in Low-Voltage Radial DSO Grids

Increasing levels of distributed generation (DG), as well as changes in electricity consumption behavior, are reshaping power distribution systems. These changes might place particular stress on the secondary low-voltage (LV) distribution systems not originally designed for bi-directional power flows. Voltage violations, reverse power flow, and congestion are the main arising concerns for distribution system operators (DSOs), while observability in these grids is typically nonexistent or very low. The present paper addresses this issue by developing a method for nodal voltage estimation in unbalanced radial LV grids (at 0.4 kV). The workflow of the proposed method combines a data-driven grey-box modeling approach with generalized additive models (GAMs). Furthermore, the proposed method relies on experimental data from a real-world LV grid in Denmark and uses data input from only one measuring device per feeder. Predictions are evaluated by using a test data set of 31 days, which is more than twice the size of the training data set of 13 days. The prediction results show high accuracy at root mean squared errors (RMSEs) of 0.002–0.0004 p.u. The method also requires a short computation time (14 s for the first stage and 2 s for the second stage) that meets requirements for the practical, real-time monitoring of DSO grids.

Optimization-based framework for low-voltage grid reinforcement assessment under various levels of flexibility and coordination

The rapid electrification of residential heating and mobility sectors is expected to drive the existing distribution grid assets beyond their planned operating conditions This change will also reveal new potentials through sector coupling, flexibilities, and the local exchange of decentralized generation. This paper thus presents an optimization framework for multi-modal energy systems at the low voltage (LV) distribution grid level. In this, we focus on the reinforcement requirements of the grid and the techno-economic assessment of flexibility components and coordination between agents. By employing a multi-level approach, computational complexity is reduced, and various levels of coordination and flexibilities are implemented. We conclude the work with a case study for a representative rural grid in Germany, in which we observe high economic potential in the flexible operation of buildings, majorly thanks to better integration of photovoltaics. Across all paradigms barring a best-case benchmark, grid reinforcement based on a mix of passive and active measures was necessary. A synergy effect is observed between flexibilities and coordination, as their combination reduces the peaks to the extent of completely avoiding grid reinforcement. The presented framework can be applied with a wide range of grid and component types to outline the broad landscape of future LV distribution grids.

A Practical Fault Location Algorithm With Increased Adaptability for Active Low Voltage Grids

Quick power restoration has always been one of the grid operators' major goals and could be facilitated by the application of automatized fault location methods. The need for such methods is particularly visible in the low voltage (LV) grids which have been overlooked until now. Hence, this paper presents a novel turnkey solution to the fault location problem in LV grids. The proposed algorithm takes advantage of the growing data availability and employs a machine learning (ML) model for the prediction of the faulted point. In order to ensure the applicability of the method all the steps of its implementation are analyzed. The study begins by addressing the algorithm's data collection requirements and proposing a smart storage strategy. It continues with the implementation of advanced data processing and minimization techniques, such as the feature selection, and addresses the generalizability limitations of ML–based algorithms by proposing a retraining scheme. Finally, the algorithm's performance during load changes and data contamination is studied. For the validation of the algorithm a modified version of the CIGRE European LV benchmark was simulated. The results verify the algorithm's robustness against the main influencing parameters and reveal low mean absolute errors throughout the tests; the default study case error was 0.5 m.

Design and control of LCL-type grid-tied PV power conditioning system based on inverter and grid side currents double feedback

ABSTRACT Solar energy as a limitless, clean, and cost-effective energy source could be converted to electrical power by photovoltaic cells. In this paper, a photovoltaic power conditioner, which is connected to the low voltage grid via an LCL filter, is presented. This power conditioning system supplies high-quality current and has high stability against disturbances. Since the generated PV voltage is lower than the grid voltage, a DC-DC converter is utilized. Also, DC voltage is also converted to AC by an LCL-type inverter. To ensure the resonance elimination yielded from the LCL filter, inverter and grid side currents, a double feedback strategy is employed. By considering resonance damping, stability margin, and the grid-connected requirements, a systematic design procedure for the suggested strategy is investigated to ensure the system stability in the low-voltage network. In comparison with the conventional strategies, the suggested strategy gives suitable resonance damping capability, higher marginal stability, and better grid-connected properties. Simulation results of the PV power conditioning system illustrate the accurate performance of the control system, suitable tracking of the PV array maximum power point, and high-quality current injection into the grid.

Grid‐aware provision and activation of fast and slow power flexibilities from distributed resources in low and medium voltage grids

AbstractThe intermittencies of renewable generations give rise to situations, which require both slow‐ and fast‐ramping flexibility capability from a variety of resources connected at the medium voltage (MV) andlow voltage (LV) distribution grids. Moreover, this may increase the grid reinforcement costs. To defer this reinforcement, the grid needs to be operated optimally. This paper proposes—(a) such an optimal operational methodology forthe MV and LV grids; and (b) an aggregated flexibility estimation methodology separately for fast and slow services at the primary substation (transmission interface). The methodologies based on model‐based MV grids and sensitivity coefficients‐based LV grids are suitable for LV grids where an up‐to‐date, accurate model and topology are not always available. Besides, the costs and resources associated to create full LV grid model and visibility should be prevented. The approaches, here, use the synchronised and accurate measurements from grid monitoring devices located at the LV distribution grids. They have been validated on a real MV and LV networks of a Swiss distribution grid operator equipped with such devices. The results, in terms of reducing technical losses, reducing grid violation costs,and estimating flexibility capability show the efficacy of the proposed methodologies and therefore, can be easily deployed.

Geographic-information-based stochastic optimization model for multi-microgrid planning

This paper presents a model for the realistic planning of multi-microgrids in the context of Active Distribution Networks with the assistance of Geographic Information Systems. The model considers the distribution system grid as well as the geographic features of the Region of Interest. It also includes long-term purchase decisions and short-term operational constraints, and considers uncertainties associated with electricity demand and Renewable Energy Resources using an existing Two-Stage Stochastic Programming approach. Geographic Information Systems along with Deep Learning are used to estimate the areas of rooftops within the Region of Interest and model the Low Voltage grid. The planning model is used to study the feasibility of implementing a multi-microgrid system consisting of 4 individual microgrids at an Active Distribution Network in a municipality in the state of São Paulo, Brazil. The results of the model presented in this paper are compared with the results obtained using Monte Carlo Simulations and an existing, less detailed, Two Stage Stochastic model. It is demonstrated that the stochastic solutions are close to those obtained with Monte Carlo at a lower computational cost, and that the use of Geographic Information allows to determine both the capacity and location of the PV panels, batteries, and distribution transformers on the microgrids grid, thus providing more precise and useful planning results.

Assessment of Conservation Voltage Reduction in Distribution Networks with Voltage Regulating Distribution Transformers

The application of voltage reduction in medium and low voltage grids to reduce peak power demand or energy consumption has been implemented since the 1980s using several approaches. Conservation Voltage Reduction (CVR), as one such approach, uses a voltage control device to reduce or increase the voltage setpoint on a busbar, thereby reducing or increasing the amount of active and reactive power supply in the network. Voltage regulation for CVR is always implemented according to established network planning standards in each country. Research in this field has proven that a CVR factor (CVRf) of 0.7–1.5 for peak demand reduction can be achieved. This is an evaluation metric of CVR. The aim of this research is to determine and validate CVRf for peak demand reduction by comparing actual results obtained during regular tap changes with other randomly distributed periods outside tap change operations, using a set of measurement data. It is important to understand CVR deployment capability by evaluating CVR potentials from historical random tap operations before a robust network-wide deployment is introduced. This research provides such guidance. It also provides a novel approach to determining tap changes from voltage measurements using a time-based algorithm. A CVRf ranging from 0.95 to 1.61 was estimated using a measurement dataset from a test field. The result of the entire evaluation shows that the CVRf are smaller during peak PV production and greater during peak demand periods. Further evaluation using statistical hypotheses testing and a control chart was used to validate the evaluation.

Calculation Method of Theoretical Line Loss in Low-Voltage Grids Based on Improved Random Forest Algorithm

Theoretical line loss rate is the basic reference value of the line loss management of low-voltage grids, but it is difficult to calculate accurately because of the incomplete or abnormal line impedance and measurement parameters. The traditional algorithm will greatly reduce the number of samples that can be used for model training by discarding problematic samples, which will restrict the accuracy of model training. Therefore, an improved random forest method is proposed to calculate and analyze the theoretical line loss of low-voltage grids. According to the Influence mechanism and data samples analysis, the electrical characteristic indicator system of the theoretical line loss can be constructed, and the concept of power supply torque was proposed for the first time. Based on this, the attribute division process of decision tree model is optimized, which can improve the limitation of the high requirement of random forest on the integrity of feature data. Finally, the improved effect of the proposed method is verified by 23,754 low-voltage grids, and it has a better accuracy under the condition of missing a large number of samples.

GridShield—Optimizing the Use of Grid Capacity during Increased EV Adoption

With the increasing adoption rate of electric vehicles, power peaks caused by many cars simultaneously charging on the same low-voltage grid can cause local overloading and power outages. Smart charging solutions should spread this load, but there is a residual risk of incidental peaks. A decentralized and autonomous technology called GridShield is being developed to reduce the likelihood of a transformer’s fuse blowing when other congestion solutions have failed. It serves as a measure of last resort to protect the grid against local power failures from unpredicted congestion by temporarily limiting the virtual capacity of charging stations. This paper describes the technical development and demonstrates how GridShield can keep a transformer load below a critical limit using simulations and real-world tests. It optimizes grid capacity while ensuring grid reliability.

A Three-Stage Strategy With Settlement for an Energy Community Management Under Grid Constraints

This paper focuses on a local energy market where a group of households in a low voltage (LV) grid is organized as an energy community. A three-stage management strategy is proposed under the French collective self-consumption framework. In the first day-ahead stage, households coordinate with the community manager to minimize the overall energy bills for the next day. Then, real-time operation (second stage) focuses on the mitigation of forecast uncertainties and voltage violation, by utilizing the reserve of the local production/storage assets. Finally, the third stage contractually allocates the community energy among the households on 30 min basis (based on the French regulation), in order to ensure fair individual cost reduction and possibly create economic surplus in the community. The proposed community management strategy is evaluated on a LV grid with 55 households, and the results show the proposed strategy achieve an average of 30% individual cost reduction among the users compared to the base scenarios. Furthermore, different pricing scenarios are compared through sensitivity studies and scalability tests are run on larger systems.