Test Feeder Research Articles

Settling Time Estimation in Synchronization of Impulsive Networks With Switching Topologies

This article addresses the problem of synchronization of impulsive networks with switching topologies. A new synchronization framework is established with an emphasis on settling time estimation. The impulsive networks consist of physical nodes and cyber modules. For physical nodes, states are changed impulsively at discrete time instants due to some switching phenomena or unexpected sudden noises. For cyber modules, two cases of switching scenarios are considered for information exchange patterns during specific time intervals. In the first case, cyber modules lose all the communication links with others, resulting in disconnected topologies. Then, a distributed controller is proposed for nodes without intrinsic nonlinear dynamics. A distinguished feature of this controller is its capability to estimate a bound for settling time, beyond which the synchronization with respect to a virtual target is guaranteed. In the second case, cyber modules lose some communication links but build other new ones with the help of a smart communication center to form connected topologies. A distributed controller is further designed for nodes in the presence of intrinsic nonlinear dynamics. Accordingly, a sufficient condition is derived to achieve synchronization with an estimated settling time bound. For both cases, the estimated bounds are able to reveal the relationship between the impulsive strength and the synchronization performance. Finally, numerical examples including a case study on a modified IEEE 34 bus test feeder are provided to demonstrate the effectiveness of the proposed controllers.

Optimal Allocation and Sizing of PV Generation Units in Distribution Networks via the Generalized Normal Distribution Optimization Approach

The problem of optimal siting and dimensioning of photovoltaic (PV) generators in medium-voltage distribution networks is addressed in this research from the perspective of combinatorial optimization. The exact mixed-integer programming (MINLP) model is solved using a master–slave (MS) optimization approach. In the master stage, the generalized normal distribution optimization (GNDO) with a discrete–continuous codification is used to represent the locations and sizes of the PV generators. In the slave stage, the generalization of the backward/forward power method, known as the successive approximation power flow method, is adopted. Numerical simulations in the IEEE 33-bus and 69-bus systems demonstrated that the GNDO approach is the most efficient method for solving the exact MINLP model, as it obtained better results than the genetic algorithm, vortex-search algorithm, Newton-metaheuristic optimizer, and exact solution using the General Algebraic Modeling System (GAMS) software with the BONMIN solver. Simulations showed that, on average, the proposed MS optimizer reduced the total annual operative costs by approximately 27% for both test feeders when compared with the reference case. In addition, variations in renewable generation availability showed that from 30% ahead, positive reductions with respect to the reference case were obtained.

Optimal coordination of unbalanced power distribution systems with integrated photovoltaic systems and semi‐fast electric vehicles charging stations

Increased integration of photovoltaics (PVs) systems and charging stations for electric vehicles (EVs) has led to a substantial increase in the level of voltage unbalance beyond the acceptable limit. Ordinary voltage regulation devices such as on-load tap changers (OLTCs) and distribution static synchronous compensator (DSTATCOM) are sometimes incapable of adequately addressing this issue without proper coordination with PVs and EVs. This paper presents a novel real-time optimal coordination scheme to determine the tap position of OLTC, the amount of reactive power to be exchanged by DSTATCOM and a PV inverter, and the phase connection of EVs. The proposed scheme aims to maintain the voltage magnitude and voltage unbalance within the statutory limit while minimising the power losses in an active unbalanced power distribution system. Advanced and hybrid particle swarm optimisation (AHPSO) algorithm is also developed to solve the optimisation problem, and its robustness in comparison with other techniques is verified. The impact of uncoordinated voltage control and proposed control on voltage unbalance and power losses are investigated. Time-series simulations confirm the significance and scalability of the developed coordination control scheme on IEEE 37-node and IEEE 123-node test feeders with real data and different PV penetration levels.

Sensibility Analysis with Genetic Algorithm to Allocate Distributed Generation and Capacitor Banks in Unbalanced Distribution Systems

In the last years, many authors have dedicated themselves to solving the problem of distributed resources allocation in Distribution Systems (DS). The optimal siting and sizing of such equipment can improve the efficiency of the provided service, promoting technical and economic gains. Several papers use artificial intelligence algorithms and methods based on population evolutions to achieve this goal, due to the increasing complexity of the simulated systems and the highly combinatorial characteristics of this problem. Reducing the search space and/or setting the initial population are critical factors for improving the performance of population-based optimization methods. In the literature, the adjustment of the initial population does not receive the attention it should, especially when dealing with three-phase large-scale unbalanced systems – when computational time becomes primary adversity. The objective of this paper is to propose a novel approach for DS optimization, using Genetic Algorithms (GA) to map which areas are more sensitive to equipment allocation to reduce losses. In this work, capacitor banks and distributed photovoltaic (PV) generation are considered. The method adopted to carry out the allocation considers proper modeling of unbalanced DS through multiphase Optimal Power Flow frameworks. Its development takes advantage of the probabilistic nature of the GA to perform a sensitivity analysis to determine the positions that may reduce the losses in a daily planning horizon. For this, the execution of GA is done sequentially, under different conditions of generation and load, selected at random, setting in optimization a 24-hour load and PV generation curves that take into consideration the uncertainties, representing each hour of the day individually for better detail of the planning horizon. It was seen that the best positions are always concentrated in specific regions, called Optimal Allocation Zones (OAZ), which may favor the planning of Utilities. Furthermore, the OAZ method can be used to define an initial population of evolutionary algorithms, thus achieving a reduction in computational time and a better quality of solutions, which is a major contribution to literature. Through this approach, the computational time can be reduced up to 60% in large-scale systems. Simulations were carried out on four unbalanced distribution test-feeders: IEEE 4, IEEE 13, IEEE 37, and IEEE 123 Node Test Feeders.

Application of the Hurricane-Based Optimization Algorithm to the Phase-Balancing Problem in Three-Phase Asymmetric Networks

This article addresses the problem of optimal phase-swapping in asymmetric distribution grids through the application of hurricane-based optimization algorithm (HOA). The exact mixed-integer nonlinear programming (MINLP) model is solved by using a master–slave optimization procedure. The master stage is entrusted with the definition of load connection at each stage by using an integer codification that ensures that, per node, only one from the possible six-load connections is assigned. In the slave stage, the load connection set provided by the master stage is applied with the backward/forward power flow method in its matricial form to determine the amount of grid power losses. The computational performance of the HOA was tested in three literature test feeders composed of 8, 25, and 37 nodes. Numerical results show the effectiveness of the proposed master–slave optimization approach when compared with the classical Chu and Beasley genetic algorithm (CBGA) and the discrete vortex search algorithm (DVSA). The reductions reached with HOA were 24.34%, 4.16%, and 19.25% for the 8-, 28-, and 37-bus systems; this confirms the literature reports in the first two test feeders and improves the best current solution of the IEEE 37-bus grid. All simulations are carried out in the MATLAB programming environment.

Model-augmented safe reinforcement learning for Volt-VAR control in power distribution networks

Volt-VAR control (VVC) is a critical tool to manage voltage profiles and reactive power flow in power distribution networks by setting voltage regulating and reactive power compensation device status. To facilitate the adoption of VVC, many physical model-based and data-driven algorithms have been proposed. However, most of the physical model-based methods rely on distribution network parameters, whereas the data-driven algorithms lack safety guarantees. In this paper, we propose a data-driven safe reinforcement learning (RL) algorithm for the VVC problem. We introduce three innovations to improve the learning efficiency and the safety. First, we train the RL agent using a learned environment model to improve the sample efficiency. Second, a safety layer is added to the policy neural network to enhance operational constraint satisfactions for both initial exploration phase and convergence phase. Finally, to improve the algorithm’s performance when learning from limited data, we propose a novel mutual information regularization neural network for the safety layer. Simulation results on IEEE distribution test feeders show that the proposed algorithm improves constraint satisfactions compared to existing data-driven RL methods. With a modest amount of historical data, it is able to approximately maintain constraint satisfactions during the entire course of training. Asymptotically, it also yields similar level of performance of an ideal physical model-based benchmark. One possible limitation is that the proposed framework assumes a time-invariant distribution network topology and zero load transfer from other circuits. This is also an opportunity for future research.

An online resilience assessment method for islanded distribution systems considering uncertainty of intermittent RESs and loads

The frequent natural disasters caused by global warming have been threatening the safety of power systems for decades. A resilient power system has the ability to recover quickly following a disaster and has been continuously studied these years. However, with the progress of energy transition, a large quantity of renewable energy sources (RESs) has been integrated into the power distribution systems and the uncertainty has complicated the decision and operation of resilient distribution systems. This paper proposes an online resilience assessment method for islanded distribution systems considering the time-varying uncertainty of RESs and loads, based on only the measured data without any forecasting tools. An offline probabilistic modeling and online conditional probability distribution derivation method is proposed to generate short-term power scenarios of RESs and loads dynamically, and an online resilience assessment framework is established with an optimization-based system response model in different outage scenarios of distribution systems. The case study to a modified IEEE 37-node test feeder validates the advantages of the proposed method in generating accurate short-term scenarios of uncertainties and providing reliable resilience assessment results. The proposed framework can be used as a basic tool for the online operation of resilient distribution systems.

Controlled electric vehicle charging for reverse power flow correction in the distribution network with high photovoltaic penetration: case of an expanded IEEE 13 node test network

Over the past years, the penetration of photovoltaic (PV) systems into the distribution network has experienced significant augmentation as the pressure to reduce greenhouse gas emissions into the atmosphere keeps increasing while the prices of the solar components keep reducing. Despite the benefits PV systems bring to the distribution network, the high penetration of this technology into the distribution network could lead to reverse power flow (RPF) when the PV systems produce more than the local loads require. This RPF could result in the malfunctioning of protective devices and their coordination. This research addresses this problem by utilizing electric vehicles (EVs) that are currently revolutionizing the transport sector. Here, the charging of EVs during the day is intelligently controlled to mitigate RPF as a result of the excess power produced by the PV systems. Resolving RPF is achieved through a control system that measures the power flow on each phase of the main grid substation. If at any instance negative power is detected (reverse power), quantified EVs needing recharge are instantly incorporated into the network for charging through the automatic closure of the power switches of the required number of charging points with EVs whose total power demand equals the amount of reverse power detected. The excess power is hence absorbed and stored by the EVs. The proposed method is tested on an expanded IEEE 13 node test feeder and simulated using ETAP software. Simulation results show the effectiveness of the proposed method in eliminating RPF which occurs from 10:00 am to 12:00 noon by connecting the required number of EVs during that timeframe. The proposed method involves the distribution network operators working in synergy with the transport sector to effectively solve the problem of RPF in the distribution network.

Enhancing Active Distribution Systems Resilience by Fully Distributed Self-Healing Strategy

Distributed restoration can exploit smart grid technologies to enhance the resilience of active distribution networks toward a self-healing smart grid. However, the large number of decision variables, especially the binary ones for reconfiguration, bring challenges to developing scalable distributed distribution service restoration (DDSR) strategies. This paper proposes a fully distributed solution procedure based on the alternating direction method of multipliers (ADMM) for mixed-integer programming problems and applies to develop the DDSR framework. The method consists of relax-drive-polish phases, 1) relaxing binary variables, and applying the convex ADMM as a warm start; 2) driving the solutions toward Boolean values through a proximal operator; 3) fixing the obtained binding binary variables and solving the rest of the problem to polish results and achieve a high-quality suboptimal solution. Then, an autonomous clustering strategy and consensus ADMM are integrated with the proposed method to realize the fully distributed cluster-based framework of DDSR. This framework can first determine DER scheduling and switch status for reconfiguration to energize the out-of-service areas from local faults, and then provide the load restoration solution in a distributed manner for total blackouts in large-scale distribution networks. The effectiveness and scalability of the proposed DDSR framework are demonstrated through testing on the IEEE 123-node, IEEE 8500-node, and synthetic 100k-node test feeders.

An Investigative Analysis of the Protection Performance of Unbalanced Distribution Networks With Higher Concentration of Distributed Energy Resources

Despite the substantial reliability and power quality improvements, the proliferation of distributed energy resources can introduce many protection complexities in distribution networks (DNs). This research article presents a comprehensive investigative analysis of the distributed generation (DG) impacts on the protection performance of the DN. Various protection coordination case studies have been analyzed under different operating conditions by varying the DG penetration level or the fault conditions to highlight the potential risks of the protection networks. The severity of the risks is evaluated by considering the DG locations, protection settings, and fault types/impedance parameters on a standardized IEEE 13 node test feeder in DIgSILENT Power Factory/MATLAB softwares. The subsequent protection hazards are examined for the mutual coordination of the overcurrent relays, auto-reclosers, and series fuses in DNs along with the differential protection issues for dc bus system. Furthermore, the impacts of DGs/PDs types are assessed on the nuisance tripping of the protection networks and results are reported.

Guest Editorial: Enhancing hosting capability for renewable energy generation in active distribution networks

Guest Editorial: Enhancing hosting capability for renewable energy generation in active distribution networks

Detection of Falsified Commands on a DER Management System

Distributed energy resource management systems (DERMS) are increasingly deployed to manage potential adverse impacts of DERs on distribution feeder voltages. However, communication channels used by DERMS controllers, along with the unmanned substations where they are deployed, provide a target for cyber attackers to cause severe voltage disturbances. The voltage problems can damage equipment, trip inverters, and cause undesirable operations of voltage regulating devices. In this paper, using detailed cyber-physical models, the various cyberattack paths an adversary may use to conduct falsified control command cyberattacks are presented. Robust and fast acting centralized cyber layer and de-centralized inverter intrusion detection are also proposed. This two-tiered system can detect and mitigate these cyberattacks before the power system layer is impacted, even if the DERMS controller itself has been compromised. The cyberattack models and proposed intrusion detection techniques have been validated using IEEE test feeders.

국제 표준과 버스 임피던스 매트릭스를 이용한 고조파 해석 알고리즘에 관한 연구

Widespread interests in environmental issues are growing. Following this trend, the proportion of renewable energy resources in the grid is increasing. Renewable energy is connected to the grid as distributed generation (DG) using converters or inverters. Therefore, harmonic analysis in the system is essential. In this paper, we present an algorithm for harmonic analysis. Harmonics are analyzed using bus impedance matrix that does not require inverse matrix operation. And this algorithm includes a tap-change transformer. This allows you to analyze complex power systems and expands the versatility of the algorithm. And by presenting harmonic analysis methods according to international standards (i.e. IEC, IEEE 519-1,2), the ability of the algorithm has been expanded. The case study validates the algorithm by comparing the harmonic calculation results over the bus admittance matrix with the calculation results over the proposed bus impedance matrix in various scenarios (eg IEEE 14, 30 bus test feeder).

Dynamic MGs-based load restoration for resilient urban power distribution systems considering intermittent RESs and droop control

The large amount of small-scale distributed generation can provide emergency power supply to critical loads during blackouts and help build resilient urban power distribution systems. However, the uncertainty from intermittent renewable energy sources (RESs) and loads complicates the modeling and challenges the operation of resilient distribution systems. This paper first proposes a novel offline probabilistic modeling and online updating method to characterize the time-varying uncertainty of multiple RESs power outputs and loads. Then, a scenario-based stochastic optimization model is proposed for load restoration, which incorporates the new concept of dynamic microgrids (MGs) and also takes into account the effects of droop control and frequency regulation. The case study to the modified 37 and 123 node test feeders shows that the restoration with droop control and frequency regulation could simultaneously consider the importance and frequency requirement of loads, and the proposed uncertainty modeling and updating method as well as the dynamic MGs could increase the overall system resilience, where the served ratio of critical loads would be particularly improved. All of these factors should be considered in real applications for a more realistic and feasible decision of load restoration strategy.

A New Algorithm for Harmonic Impacts with Renewable DG and Non-linear Loads in Smart Distribution Networks

With the development of renewable energy sources (RECs), distributed generation (DG), micro-grid, electric vehicles, etc., in traditional distribution network which was earlier supply-side driven, single power supply, one-way service and basic reliance on manual management of business model turns into the direction of user interaction, the trend of two-way flow and highly automated these changes gradually formed the smart distribution networks (SDN). The rapid advancement in power electronic devices increases the fast growth of RECs into SDN and also increases the non-linear portion of loads. Due to this, harmonic currents will be injected into SDN. This paper proposed a new power flow algorithms (PFAs) for fundamental and harmonic impacts in SDN with integration of renewable energy based DG and presence of non-linear loads. The proposed methodology completely exploits the radial feature in distribution networks and developed the matrix of bus numbers (BUS_NUM) and matrix of branch numbers (BRANCH_NUM). These matrices make the implementation of PFAs simple. The accuracy of fundamental and harmonic PFAs is tested on IEEE-13 bus test feeder and they are found to be accurate with the literature. Different case studies are carried on IEEE-13 bus test feeder for analyzing the impacts of integration of synchronous based-renewable DG and power electronic based-renewable DG.

Convolutional autoencoder anomaly detection and classification based on distribution PMU measurements

The huge volume of data that is streamed from distribution phasor measurement units (DPMU) toward distribution management system (DMS) can rarely be used in the raw format. Data-driven analysis to extract information out of massive raw data has revealed promising opportunities to overcome this challenge. This paper utilizes convolutional autoencoders (Conv-AE) for the sake of anomaly detection based on the DPMU measurements in distribution systems. The Conv-AE is unsupervised and independent of the event type. It dispenses preprocessing and feature extraction along with obtaining the essential information directly from the measured data. The anomaly detection is followed by a supervised classifier which is developed to identify the type of anomaly. This classifier is a convolutional neural network that is designed and fine-tuned for the problem at hand. It uses an ensemble learning method to augment the fully-labelled dataset. Performance of the proposed methodology is evaluated on modified IEEE 34-node and IEEE 123-node test feeders in various operating conditions, presence of noise, and different scenarios of DPMU outage. Moreover, a real-world DPMU dataset is utilized to evaluate performance of the Conv-AE model in practical conditions. Results confirmed effectiveness of the proposed technique to be used in future DMS platforms.

Enlarging flexibility region of virtual power plant via dynamic line rating

The virtual power plant (VPP) can aggregate small-scale distributed resources to provide flexibility for the transmission system. The flexibility provision capability of the VPP is restricted by technical operating limits of the distribution system to which distributed resources are connected. To facilitate the flexibility provision of the VPP while securing the operation of the local system, this paper leverages the dynamic line rating (DLR) to enlarge the VPP flexibility region. The flexibility region of the VPP is defined as the allowable range of active and reactive power output that the VPP can execute subject to operating constraints of the internal system. With the DLR, the current carrying capacity of the distribution network is dynamically adjusted based on actual environmental conditions, which helps to remove barriers from transmission limits that hinder the integration of distributed flexibility. A convex hull-based method with an explicit accuracy guarantee is proposed to approximate the flexibility region with the DLR. Case studies based on IEEE-33 and IEEE-123 test feeders with real-world operation data validate the proposed framework.

A hierarchical blockchain-based electricity market framework for energy transactions in a security-constrained cluster of microgrids

In this paper, a Hierarchical Blockchain-based electricity market framework is proposed for peer-to-peer energy transactions within and between Microgrids in a security-constrained cluster of Microgrids. This market enhances the energy transaction mechanism, market clearing process and security of the power market. The existing blockchain based electricity markets usually check the energy availability through a transaction verification process, however, the feasibility of energy transactions between microgrids from the power system point of view is not evaluated before approving a transaction and creating a new block. In this paper, the unique structure of the market makes it possible to consider reliability security constraints of the power grid during the transaction verification process. Furthermore, the blockchain ledger summarization and microgrid’s equivalent model development are other new features of this framework. To develop and verity this market framework, Java programming language is used to create the hierarchical blockchain-based algorithm, and a modified IEEE 37-bus distribution test feeder system is chosen as the case study. Numerical results will show the effectiveness of this market structure, especially, when several transactions are requested to transfer energy over some congested transmission corridors.

신재생에너지 보급률 증가에 따른 지역적 태양광 수용 용량 추정에 관한 연구

Due to eco-friendly policies, the use of distributed generation, which is a renewable energy, in the existing power system is increasing. Such renewable energy has advantages of voltage drop compensation and power loss reduction, but has disadvantages such as reverse current phenomenon, overvoltage, and increased complexity. For this reason, research on the effect and stability of the power system when a distributed power supply is connected is becoming important, and it is necessary to calculate the maximum allowable distributed generation supply capacity. To this end, this study analyzed the effect on the transient state when the distributed generation supply is connected to the power system through simulation, and presented the maximum allowable distributed generation capacity by analyzing frequency, rotor angle, convergence time, voltage magnitude, etc. In this study, the IEEE 37-bus test feeder and IEEE 9-bus test feeder were set as the test bed as the simulation system, and the simulation was conducted including the photovoltaic system installation, fault current, and events such as switch opening and closing.

Active Exploration by Chance-Constrained Optimization for Voltage Regulation with Reinforcement Learning

Voltage regulation in distribution networks encounters a challenge of handling uncertainties caused by the high penetration of photovoltaics (PV). This research proposes an active exploration (AE) method based on reinforcement learning (RL) to respond to the uncertainties by regulating the voltage of a distribution network with battery energy storage systems (BESS). The proposed method integrates engineering knowledge to accelerate the training process of RL. The engineering knowledge is the chance-constrained optimization. We formulate the problem in a chance-constrained optimization with a linear load flow approximation. The optimization results are used to guide the action selection of the exploration for improving training efficiency and reducing the conserveness characteristic. The comparison of methods focuses on how BESSs are used, training efficiency, and robustness under varying uncertainties and BESS sizes. We implement the proposed algorithm, a chance-constrained optimization, and a traditional Q-learning in the IEEE 13 Node Test Feeder. Our evaluation shows that the proposed AE method has a better response to the training efficiency compared to traditional Q-learning. Meanwhile, the proposed method has advantages in BESS usage in conserveness compared to the chance-constrained optimization.