Feeder Model Research Articles

Prediction of amplitude of fault generated travelling wave in medium-voltage grids for fault type classification

This paper proposes a new solution for the classification of short-circuits in medium-voltage distribution grids. The solution requires the measurements of the phase voltages at the time of the short-circuit and the voltage amplitudes of the short-circuit wave resulting from the fault. The values of the voltage before the short-circuit are used to calculate the expected values of the short-circuit wave amplitudes for the different types of short circuits. These calculations are based on an analysis of the charge flow between the capacitances of the power line due to the disturbance. The short-time matrix pencil method was used to detect the incoming short-circuit waves. Tests of the algorithm were carried out on the IEEE 34-bus Feeder model, taking into account the influence of voltage sensors on the measured signal values. The classification algorithm uses only non-zero components due to their relatively low attenuation. Despite that, it achieves high performance in the classification of single- and two-phase short-circuits.

Smart meter-based outage detection method for power distribution systems

This paper proposes a new data-driven method for power outage detection. By capturing the changes in data distribution of smart meters (SM), it can detect power outages in partially visible distributed systems. First, a mechanism based on breadth-first search (BFS) is proposed, which decomposes the network into a set of regions to find the location information where power outages are most likely to occur. Then, the SM data for each region, generating a generative adversarial network (GAN), is used in order to extract unsupervised manner implicit temporal behavior under normal conditions. After network training, anomaly scoring technology is used to determine whether the real-time measurement data is the data of a power outage event. Finally, in order to infer the location of a power outage in a multi-area network, a regional coordination process with interdependence be-tween cross-regions is used. At the same time, the concept of entropy is used to provide performance analysis for the algorithm in this paper. This method has been verified on the distribution feeder model with actual SM data. Experimental results show that the algorithm is effective and feasible.

Impacts of wireless charging system for electric vehicles on power grid

Electric Vehicles (EVs) have become a viable choice for cutting carbon emissions in the field of road transportation. EVs will be incorporated into the electrical grid at an increasing rate in the near future. EVs themselves are becoming immense power consuming load on the power grid system as a result of the growing number of EVs, mostly light-duty EVs. In addition to posing major technical risks such power quality issues, voltage swings, harmonic injection, battery deterioration, and grid instability, the unplanned installation and rapid adoption of charging stations might impede the smooth functioning of the power distribution system. Therefore, this paper analyzes and summarizes the potential issues and solutions for Fast Wireless Charging (FWC) that allows for the wireless transmission of very high-power levels (>20 kW) from the power grid to explore the impact of EV charging on the power system network. This need can have far-reaching effects on the load profile, operation, and design of the distribution network. In order to avoid exceeding the grid's technical restrictions, it is crucial to find the maximum feasible deployment level before rolling out an FWC network. This study provides a method for assessing how FWC technologies influence distribution networks. Using a realistic model of an Indian MV distribution feeder, the proposed methodology is used, providing both quantitative and qualitative results that serve as potential directions for future research studies.

A Decentralized Voltage Regulation Scheme Using Improved Volt‐Var Function of PV Smart Inverter

With the growing distributed PV installation rate in distribution systems, voltage regulation difficulties such as local voltage violations and fluctuations have become common. To solve the voltage regulation problems, the local voltage regulation method using volt‐var (VV) function is effective for its high regulation speed, high accuracy, and flexibility. However, there are still hurdles on real application, such as parameter setting difficulties, insufficient voltage fluctuation mitigation effect, and unfairness of reactive power generation between PV customers. To further improve the VV function, this paper proposes a PID closed‐loop based VV function and mode‐switching function of PV smart inverter (SI). To validate the proposed methods, numerical simulations were conducted using a 6‐feeder distribution system model based on the JST‐CREST 126 distribution feeder model. According to the simulation result, the proposed method can better mitigate the local voltage violation compared to basic VV function based on the VV curve, also improve the fairness between PV customers, while the total reactive power generation and tap operations increases for the aim of more adequate voltage regulation. © 2024 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Allocation and smart inverter setting of ground-mounted photovoltaic power plants for the maximization of hosting capacity in distribution networks

As the integration of solar photovoltaic (PV) power plants into distribution networks grows, quantifying the amount of PV power that distribution networks can host without harmfully impacting power quality becomes critical. This work aims to determine the best number, location, and size of PV systems to be installed on a distribution feeder, as well as the best control set-points of the PV inverters, to maximize the PV hosting capacity (HC). Therefore, a simulation-optimization framework is proposed for siting and sizing ground-mounted PV power plants equipped with smart inverters (SIs). Single (decentralized) and multiple (distributed) allocations are analyzed by considering the connection of one, two, and three PV systems. Genetic algorithm (GA) and particle swarm optimization (PSO) metaheuristics are employed to solve the optimization problem. The simulation-optimization framework is tested on a real-world feeder model from an Ecuadorian utility. Installing two PV systems with their SIs operating with the Volt-VAr control function yields maximum PV HC, which is increased by 32.1 % compared to a single PV power plant operating at a unity power factor. Moreover, a comparative analysis of the two metaheuristic algorithms reveals that the PSO method provides better results than GA.

Adaptive primal–dual control for distributed energy resource management

With the increased adoption of distributed energy resources (DERs) in distribution networks, their coordinated control with a DER management system (DERMS) that provides grid services (e.g., voltage regulation, virtual power plant) is becoming more necessary. One particular type of DERMS using primal–dual control has recently been found to be very effective at providing multiple grid services among an aggregation of DERs; however, the main parameter, the primal–dual step size, must be manually tuned for the DERMS to be effective, which can take a considerable amount of engineering time and labor. To this end, we design a simple method that self-tunes the step size(s) and adapts it to changing system conditions. Additionally, it gives the DER management operator the ability to prioritize among possibly competing grid services. We evaluate the automatic tuning method on a simulation model of a real-world feeder in Colorado with data obtained from an electric utility. Through a variety of scenarios, we demonstrate that the DERMS with automatically and adaptively tuned step sizes provides higher-quality grid services than a DERMS with a manually tuned step size.

Adaptive power system frequency support from distributed photovoltaic systems

The integration of converter-based renewable energy resources in a power system leads to reduced inertia, creating a multitude of challenges relating to stability and reliability. On the positive side, the faster response of converter-based renewable energy resources to frequency disturbances, compared to the conventional synchronous-based generators, reduces the required minimum level of inertia in future low-inertia power systems. Accordingly, grid support from distributed photovoltaic (DPV) systems is one of the emerging solutions to overcome the challenges of these systems. This paper demonstrates how adaptive power system frequency support, which modifies the dynamic of frequency support in DPV systems according to the available level of power system inertia, improves overall system operation. In this way, for lower values of power system inertia, a higher amount of power reserve is maintained across DPV systems. Additionally, a faster frequency–Watt response is implemented to provide a larger amount of grid support under frequency disturbances. Simulation results on a composite load model of distribution feeder show that applying for adaptive frequency support from DPV systems improves the frequency nadir during under-frequency events. Experimental results show the effectiveness of the proposed approach in providing a faster dynamic response and higher amount of power support from DPV systems under low inertia conditions.

Flowsheet modelling of a powder continuous feeder-mixer system

In this study, an integrated flowsheet model of the continuous feeder-mixer system was calibrated, simulated and compared against experimental data. The feeding process was first investigated using two major components (ibuprofen and microcrystalline cellulose (MCC)), in a formulation comprised of: 30 wt% of ibuprofen, 67.5 wt% MCC, 2 wt% of sodium starch glycolate and 0.5 wt% of magnesium stearate. The impact of a refill on feeder performance was experimentally evaluated for different operating conditions. Results showed that it had no influence on feeder performance. While simulations with the feeder model fairly reproduced the material behaviour observed in the feeder, unintended disturbances were underpredicted due to the model’s low complexity. Experimentally, mixer’s efficiency was assessed based on ibuprofen residence time distribution. Mean residence time pointed to a higher mixer’s efficiency at lower flow rates. Blend homogeneity results showed that for the entire set of experiments, ibuprofen RSD < 5%, irrespective of process variables. A feeder-mixer flowsheet model was calibrated, after regressing the axial model coefficients. The regression curves exhibited a R2 above 0.96, whereas the RMSE varied from 1.58x10-4 to 1.06x10-3 s−1 across all fitted curves. Simulations confirmed that flowsheet model captured the powder dynamics inside the mixer and qualitatively predicted the mixer’s filtering ability against feeding composition fluctuations, as well as ibuprofen RSD in blend, in line with real experiments.

Estimating and Calibrating DER Model Parameters Using Levenberg–Marquardt Algorithm in Renewable Rich Power Grid

The proliferation of inverter-based distributed energy resources (IBDERs) has increased the number of control variables and dynamic interactions, leading to new grid control challenges. For stability analysis and designing appropriate protection controls, it is important that IBDER models are accurate. This paper focuses on the accurate estimation and parameter calibration of DER_A, a recently proposed aggregated IBDER model. In particular, we focus on the parameters of the reactive power–voltage regulation module. We formulate the problem of parameter tuning as a non-linear least square minimization problem and solve it using the Levenberg–Marquardt (LM) method. The LM method is primarily chosen due to its flexibility in adaptively selecting between the steepest descent and Gauss–Newton methods through a damping parameter. The LM approach is used to minimize the error between the actual measurements and the estimated response of the model. Further, the computational challenges posed by the numerical calculation of the Jacobian are tackled using a quasi-Newton root-finding approach. The proposed method is validated on a real feeder model in the northeastern part of the United States. The feeder is modeled in OpenDSS and the measurements thus obtained are fed to the DER_A model for calibration. The simulation results indicate that our approach is able to successfully calibrate the relevant model parameters quickly and with high accuracy, with a total sum of square error of 3.57×10−7.

Distributed Economic Control Method for Energy Storage Unit with Random Time Delay

The operation cost waste in the charge and discharge process cannot be ignored for islanded microgrids with energy storage units. Different from the economic dispatch methods focused on the power pricing and bid coefficient in the tertiary control layer, this paper designed a power management algorithm for the economic operation of energy storage units in the secondary control layer. The strategy is devoted to minimizing the total charge power cost of energy storage units while satisfying the constraint of power balance. Meanwhile, the maximized allowable delay bounds were derived to guarantee the communication network reliability when against random time delays. The system stability was analyzed by the Lyapunov functions. Finally, the numerical simulations on the modified IEEE 30 feeder model verify the superiority of the provided algorithm.

Metasurface Antennas Design: Full-Wave Feeder Modeling and Far-Field Optimization

This article addresses the optimization of the radiation pattern of surface-wave (SW)-based metasurface (MTS) antennas. Those antennas are considered a promising alternative to parabolic reflectors and phased arrays due to their extremely low profile and their ability to provide high gain, shaped beams and multibeams. However, pattern synthesis with MTS antennas is very challenging because of the single active control point and the need to control surface and leaky waves through the MTS. An accurate optimization of the radiation pattern, along with the sidelobe level requires full-wave modeling of the feeding structure, including its coupling with the MTS. MTS synthesis methods existing in the literature usually approximate the feeder model, and neglect its coupling with the MTS. Such approximation may lead to more than 1 dB error in the predicted antenna directivity. This article presents a technique for optimization of the far-field pattern, built on a Method of Moments (MoM) analysis tool in which the MTS coupling with the feeder, a coax probe, is fully considered. The MTS is modeled as an arbitrarily shaped, spatially modulated electric sheet impedance in a layered medium. At each optimization iteration, the complexity of the underlying analysis is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(\text {N}\log \text {N})$ </tex-math></inline-formula> owing to the use of a fast Fourier transforms (FFT)-based acceleration. The effectiveness of the method is demonstrated through the optimization of MTSs radiating a pencil beam and a conical beam with orbital angular momentum (OAM).

A Joint Distribution System State Estimation Framework via Deep Actor-Critic Learning Method

Due to the increasing penetration of volatile distributed photovoltaic (PV) resources, real-time monitoring of customers at the grid-edge has become a critical task. However, this requires solving the distribution system state estimation (DSSE) jointly for both primary and secondary levels of distribution grids, which is computationally complex and lacks scalability to large-scale systems. To achieve real-time solutions for DSSE, we present a novel hierarchical reinforcement learning-aided framework: at the first layer, a weighted least squares (WLS) algorithm solves the DSSE over primary medium-voltage feeders; at the second layer, deep actor-critic (A-C) modules are trained for each secondary transformer using measurement residuals to estimate the states of low-voltage circuits and capture the impact of PVs at the grid-edge. While the A-C parameter learning process takes place offline, the trained A-C modules are deployed online for fast secondary grid state estimation; this is the key factor in the scalability and computational efficiency of the framework. To maintain monitoring accuracy, the two levels exchange boundary information with each other at the secondary nodes, including transformer voltages (first layer to second layer) and active/reactive total power injection (second layer to first layer). This interactive information passing strategy results in a closed-loop structure that is able to track optimal solutions at both layers in a few iterations. We have performed numerical experiments using real utility data and feeder models to verify the performance of the proposed framework.

Determining the relationship between the simulation duration by the discrete element method and the computer system technical characteristics

The object of this study is the relationship between the technical characteristics of a computing system and the duration of modeling the motion of particles of granular materials by the discrete element method. The scheme of the calculation algorithm is presented; its main stages are analyzed. A 3D model of a belt feeder and a mathematical model of particle motion were developed for calculations by the discrete element method in the EDEM 2017 environment. The physical and mechanical properties of bulk material are defined; the structural and technological parameters of equipment are determined. The parameters of the algorithm and computing system are analyzed. Those parameters are defined, the change of which does not affect the accuracy of calculations but can change the volume of computing resources used. These include the number of particles of loose material, the «grid» step, and the number of processor cores. The influence of these parameters on the duration of simulation was determined using a complete factor experiment. Experimental studies have shown that for the duration of the simulation, the determining parameters are the number of particles and the number of processor cores. It was established that there is a linear relationship between the duration of the simulation and the number of particles. The regression equation is built, which makes it possible to predict the simulation time. It was also established that the software does not fully use all available computing resources; the maximum load on the processor when utilizing all available cores is 57 %. The use of RAM and disk subsystem almost did not change during simulation. The results reported here make it possible to plan the use of computing resources for research using the discrete element method and to predict the simulation time

The impact of energy-efficiency upgrades and other distributed energy resources on a residential neighborhood-scale electrification retrofit

Ambitious targets for carbon emissions reductions are highlighting new challenges for electrification strategies, leading to an increased focus on building load flexibility and energy management to complement the variability inherent in renewable energy generation. Over the next decade millions of existing homes could undergo electrification retrofits, and there is an urgent need to understand the potential impacts of electrifying major residential loads such as water and space heating on community load characteristics, resident energy bills, and the utility’s distribution system. Behind-the-meter distributed energy resources (DERs), including efficiency measures, photovoltaics (PV), battery storage, managed electric vehicle (EV) charging, and controls such as home energy management systems (HEMS), can significantly alter a neighborhood’s load profile and provide benefits to both the residents and the grid. We present a novel approach to characterizing the impact of a hypothetical neighborhood-scale residential retrofit program on individual homes’ energy use profiles, associated utility bills, and the local distribution system. We modeled a mixed-fuel community of 30 single-family homes in Denver, Colorado, and compared the effects of retrofit scenarios ranging from conventional energy-efficiency upgrades to full electrification with and without more advanced DER technologies. We analyzed which packages of DERs most reliably enable demand flexibility in response to a time-of-use (TOU) rate for this and similar neighborhoods. Our buildings-to-grid co-simulation framework includes a generic secondary distribution feeder model to capture voltage profiles, transformer loading, and other grid impacts in each case. We also calculated the carbon emissions associated with energy use in the community. The methodology developed here can be broadly applied to community-scale beneficial electrification studies in other regions, climates, utility infrastructures, and building typologies to make specific, targeted recommendations based on quantified projections of energy demand in any given community. Our findings indicate that residential electrification can be achieved without negatively impacting the monthly utility bill, and that a combination of conventional energy-efficiency measures, PV, battery, controls, and managed EV charging to maximize a community’s demand flexibility is a promising strategy. Adding DERs (especially PV) as part of efficient electrification produces much bigger savings than efficient electrification without DERs. A key barrier is that upgrades require upfront costs, and modest utility bill savings result in long payback periods.

Efficient Phasor-Based Dynamic Volt/VAr and Volt/Watt Analysis of Large Distribution Grid With High Penetration of Smart Inverters

As the penetration of power-electronics based smart inverters (SIs) is increasing in distribution grids, it adds computational challenges in solving dynamic models of large-scale distribution feeders. Voltage and reactive power (Volt/VAr), and voltage and active power (Volt/Watt) dynamics have been analyzed at slower time scales akin to the control of legacy grid devices. However, smart inverters, being power-electronics based devices, can provide dynamic active/reactive power support at a faster time scale, which necessitates Volt/VAr and Volt/Watt dynamics to be analyzed at a faster time scale. The existing dynamic models are overly detailed and computationally intractable for distribution feeders with a large number of inverters. In this context, this proposed work aims towards developing a computationally tractable, scalable, and accurate phasor-based model for dynamic Volt/VAr and Volt/Watt analyses of large distribution systems with high penetration of smart inverters. Case studies demonstrate that the proposed phasor-based model sufficiently captures the Volt/VAr and Volt/Watt dynamics, and is computationally faster by one order of magnitude compared to the average model and by two orders of magnitude compared to the detailed switching model. Case studies also demonstrate the efficacy and scalability of the proposed model in analyzing Volt/VAr and Volt/Watt dynamics of large-scale power networks with hundreds of SIs.

Control of Distributed Photovoltaic Inverters for Frequency Support and System Recovery

Replacing conventional synchronous generator-based power plants with inverter-based renewable energy resources results in a reduction of the inertia in power systems. To sustain the security and reliability of these low-inertia power systems, frequency support is increasingly required in new standards for grid-connected renewable energy resources, especially distributed photovoltaic (DPV) systems. This article proposes a frequency droop-based control in DPV inverters to improve frequency response in power grids with high penetration of renewable energy resources. A predefined power reserve is kept in the DPV inverter, using flexible power point tracking. The proposed algorithm uses this available power reserve to support the grid frequency. Furthermore, a recovery process is proposed to continue injecting the maximum power after the disturbance, until frequency steady-state conditions are met. The positive inertia using the proposed recovery mode, leads to recovery of the frequency in a shorter period. A composite load model of a distribution feeder, including DPV, is developed to assess the effectiveness of the proposed frequency support algorithm in power systems with high penetration of DPV inverters. The dynamic performance of the proposed frequency support algorithm with over frequency and under frequency events is also evaluated experimentally.

Controller Hardware-in-the-Loop Validation of Coordinated Voltage Control Scheme for Distribution Systems Containing Inverter-Based Distributed Generation

This article presents the controller hardware-in-the-loop (CHIL) validation of a novel coordinated voltage control solution for distribution systems with high penetration of distributed generation (DG). The control algorithm coordinates traditional voltage regulation devices and inverter-based DGs capable of supplying reactive power to mitigate voltage violations. Voltage sensitivity analysis and electric distances are used to identify the contributing nodes and the amount of VAR to be injected so that voltage violations can be mitigated. The proposed algorithm is validated using offline simulation and a real-time CHIL setup that considers a real-world communication scenario. In both the offline and real-time validation, a feeder model based on an actual distribution feeder with real field data is used. The results show that the proposed algorithm is successful in coordinating the available system resources to keep the system voltages within the desired limit.

A Network-Aware Distributed Energy Resource Aggregation Framework for Flexible, Cost-Optimal, and Resilient Operation

To efficiently use the ubiquitous behind-the-meter distributed energy resources (DERs) in distribution systems for providing grid services, this paper presents a hierarchical control framework for DER optimal aggregation and control. We first develop a convex optimization model to evaluate the DER flexibility, and then use a convex model-predictive-control based approach to dispatch those DERs. The hierarchical control framework consists of a utility controller, community aggregators and multiple home energy management systems. The flexibility of the DERs is evaluated by each controller in the hierarchy such that the resultant flexibility is feasible given its operational domain. Based on the determined flexibility, the hierarchical controllers then compute optimal setpoints for the DERs to help the distribution system regulate node voltages and provide other distribution grid services. Numerical simulations performed on a model of a real distribution feeder in Colorado, using actual DER data in a residential community demonstrate that the proposed approach can effectively alleviate voltage issues and support resilient operation.

Voltage Unbalance Compensation in Distribution Feeders Using Soft Open Points

Soft open points (SOPs) are power electronic devices that may replace conventional normally-open points in distribution networks. They can be used for active power flow control, reactive power compensation, fault isolation, and service restoration through network reconfiguration with enhanced operation flexibility and grid resiliency. Due to unbalanced loading conditions, the voltage unbalance issue, as a common problem in distribution networks, has negative impacts on distribution network operation. In this paper, a control strategy of voltage unbalance compensation for feeders using SOPs is proposed. With the power flow control, three-phase current is regulated simultaneously to mitigate the unbalanced voltage between neighboring feeders where SOPs are installed. Feeder voltage unbalance and current unbalance among three phases are compensated with the injection of negative-sequence and zero-sequence current from SOPs. Especially in response to power outages, three-phase voltage of isolated loads is regulated to be balanced by the control of SOPs connected to the feeders under faults, even if the loads are unbalanced. A MATLAB/Simulink model of the IEEE 13-bus test feeder with an SOP across feeder ends is implemented, and experimental tests on a hardware-in-the-loop platform are implemented to validate the effectiveness of the proposed control strategy.

Detailed Primary and Secondary Distribution System Model Enhancement Using AMI Data

Reliable and accurate distribution system modeling, including the secondary network, is essential in examining distribution system performance with high penetration of distributed energy resources (DERs). This paper presents a highly automated, novel method to enhance the accuracy of utility distribution feeder models to capture their performance by matching simulation results with corresponding field measurements. The method is demonstrated using an actual feeder from an electrical utility with high penetration of DERs. The method proposed uses advanced metering infrastructure (AMI) voltage and derived active power measurements at the customer level, and data acquisition systems (DAS) measurements at the feeder-head, in conjunction with an AC optimal power flow (ACOPF) to estimate customer active and reactive power consumption over a time horizon, while accounting for unmetered loads. The ACOPF uses the measured voltage magnitudes, derived active power measurements, and the feeder head measurements to obtain a complete active power and reactive power capture of the feeder loads. Additionally, the method proposed estimates both voltage magnitude and angle for each phase at the unbalanced distribution substation. The accuracy of the method developed is verified in two stages: by comparing the time-series power flow results obtained from the enhancement algorithm with OpenDSS results and with the field measurements available. The proposed approach seamlessly manages the data available from the optimization procedure through the final model verification automatically.