IEEE 13-node Test Feeder Research Articles

Bad data identification method considering the on-load tap changer model

With the connection and integration of renewable energy, the on-load tap-changer (OLTC) has become an important device for regulating voltage in distribution networks. However, due to non-smooth and non-linear characteristics of OLTC, traditional bad data identification and state estimation methods for transmission network are limited when applied to the distribution network. Therefore, the nonlinearity and droop control constraints of the OLTC model are considered in this paper. At the same time, the quadratic penalty function is introduced to realize the fast normalization of the tap position. It proposes a two-stage bad data identification method based on mixed-integer linear programming. In the first stage, suspicious measurements are identified using projection statistics and maximum normalized residual methods for preprocessing the measurement data. In the second stage, a linearization approach utilizing hyhrid data-physical driven is applied to handle nonlinear constraints, leading to the development of a bad data identification model based on mixed-integer linear programming. Finally, the proposed methodology is validated using a modified IEEE-33 node test feeder example, demonstrating the accuracy and efficiency of bad data identification.

Data-driven evaluation for quantifying energy resilience in distribution systems with microgrids and P2P energy trading

Electrical distribution systems (EDS) face the major issue of complete interruption of power when the supply end of the electrical grid encounters extreme events. The integration of distributed energy resources (DERs) at the residential level has opened the path to building energy resilience within these systems, enabling them to withstand such incidents at the load end. To further enhance energy resilience, peer-to-peer (P2P) energy trading networks could be established at the community level. While several researchers have developed models to implement energy trading in the P2P network with DERs, enabling prosumers to benefit from cost savings, the impact of both DERs and P2P energy trading on the system’s resilience has not been evaluated with a quantifiable measure. In this work, we propose a methodology introducing a quantifiable measure for evaluating energy resilience in electrical distribution systems with DERs and P2P energy trading by calculating the percolation threshold (PT) using complex networks for both univariate and multivariate data. We employ cooperative game theory to model P2P energy trading, ensuring that all prosumers participate rationally. We consider the formation of microgrids in a standard IEEE-123 node test feeder system integrated with renewable energy sources. The improvement in energy resilience with trading of energy in the P2P network is analyzed for the most resilient microgrid in the distribution system. The results demonstrate a 67.91% total cost benefit and a 25.07% improvement in the resilience of the microgrid due to the integration of DERs and P2P energy trading for a period of one year.

Fault indicators allocation to maximize the performance of a fault locator based on artificial intelligence

Fault location (FL) is one of the main challenges in Advanced Distribution Automation (ADA) of Active Distribution Networks (ADN). One of the commonly used strategies by utilities to deal with this challenge is the use of Fault Indicators (FIs), which indicate to the operator the path taken by the fault current. However, a good performance of this scheme depends on the number of installed devices, a high number of them could cause a high cost for the utility investment planning. In this context, this paper presents an artificial intelligence-based fault location strategy that determines the number and location of FI into ADN to maximize performance in fault section estimation. To achieve this objective, the ADN is divided into sections, and the FL problem is modeled as a classification problem to train an Artificial Neural Network (ANN). To determine the number of FIs to be installed and their location, the strategy uses the three-phase current magnitudes measured by the FI as features for an ANN model. Also, the strategy uses a feature selection and tuning scheme based on a multiverse optimization algorithm (MOA) to identify the features that maximize the accuracy of the ANN model. The strategy was validated on the IEEE123-node test feeder. The results showed accuracy close to 99.4 % with a reduction of 40 % of the number of FIs when compared with other method. The strategy shows its simplicity and promising prospects to apply it in the utility's investment planning.

Power Quality Study in Distribution Networks with High Penetration of Renewable Energies using Real-Time Simulation

Introduction: The power supplied to the load through the distribution network comes mainly from conventional energy sources. However, a transformation is being made towards sustainable and efficient electricity networks, which incorporate distributed renewable energy sources and closer to consumption. This process has led to the investigation of the effects that the inclusion of distributed energy resources brings, which have shown power quality issues. However, more studies are required to show more detailed effects and the use of new tools such as real-time simulation. Objective: This paper uses real-time simulation to evaluate the impact on the power quality produced by integrating distributed energy resources (DER) into a distribution network. Method: The IEEE 13-node test feeder system was used to evaluate voltage harmonics, current harmonics, flicker, and DC injection based on the IEEE 1547-2018, IEEE 519-2014, and the NTC 5001-2008 standards. The feeder test system was implemented in the Hypersim software and used to run real-time simulations. Results: The results show that integrating distributed energy resources into the network produces a high impact on the current harmonics of the network. Conclusions: The DC injection phenomenon presents a medium impact, and flickers and voltage harmonics present a lower impact.

A Risk Assessment Framework for Cyber-Physical Security in Distribution Grids with Grid-Edge DERs

Integration of inverter-based distributed energy resources (DERs) is reshaping the landscape of distribution grids to fulfill the socioeconomic, environmental, and sustainability goals. Addressing the technological challenges of DER grid integration requires an adaptive communication layer for efficient DER management and control. This transition has given rise to a cyberphysical system (CPS) architecture within the distribution system, causing new vulnerabilities for cyberphysical attacks. To better address potential threats, this paper presents a comprehensive risk assessment framework for cyberphysical security in distribution grids with grid-edge DERs. The framework incorporates a detailed CPS model accounting for dynamic DER characteristics within the distribution grid. It identifies vulnerabilities in DER communication systems, models attack scenarios, and addresses communication latency crucial for inverter control timescales. Subsequently, the quantification of attack impacts employs an attack probability model including both the vulnerability and criticality of cyber components. The proposed risk assessment framework was validated through testing on the modified IEEE 13-node and 123-node test feeders.

Dynamic microgrid formation for resilient distribution systems considering large-scale deployment of mobile energy resources

Microgrids (MGs) are promising solutions to improve power distribution system (PDS) resilience against natural disasters. However, the existing MG formation approaches based on the linearized Distflow (LinDistflow) model always demand MG roots and their corresponding topologies. This can result in an increased number of variables and constraints in the optimization problem, and deteriorate their computational performance. To this end, an adaptive LinDistflow model is proposed based on the single commodity flow model in graph theory in this paper. Specifically, we show that active and reactive powers can be represented as commodities, which are sent from one node to each of its adjacent nodes in the graph. Then, the power flow and nodal voltage calculation based on the commodity flow only requires adjacent node information of the original topology rather than various MG topologies caused by the dynamic deployment of mobile energy resources (MERs). Furthermore, by incorporating the adaptive LinDistflow model as constraints, a dynamic MG formation approach is proposed for resilient load restoration considering large-scale MER deployment. The problem is formulated as a mixed-integer nonlinear programming problem (MINLP). A linearization technique is proposed based on the propositional logic constraints. It employs the propositional logic that partitions the solution space into two separated regions. Accordingly, the region that the solution lies in can be selected linearly. The effectiveness of the proposed approach is demonstrated based on the IEEE 37-Node, 123-Node and 8500-Node Test Feeders.

An enhanced method for fault location in distribution systems based on optimal power flow

This paper proposes a method based on synchronized RMS measurements and an optimal power flow for fault location in Distribution Systems (DS). A modified interior point method is adopted to improve the accuracy of the fault location process. Simulations were carried out on the IEEE13, IEEE34, IEEE123, and IEEE8500 node test feeders, and the results showed that it presents a great performance in terms of accuracy and computational efficiency. The proposed method is also robust to deal with variations in the DS and can accurately locate faults under different operating conditions. This method significantly affects managing distribution systems, improving system reliability.

Expanding the Role of DSOs in Establishing Flexible Local Markets: A Brokerage Perspective

As more distributed energy resources are deployed into distribution networks, supply and demand matching between prosumers becomes a new challenge for distribution system operators (DSOs) due to the intermittency of DERs. Recent studies begin to expand the responsibilities of DSOs towards local generation and consumption optimization, such as establishing a flexible local market. However, the difficulty of boosting local power exchange while reducing operation costs still remains unsolved. In this paper, we propose a new way of power trading management for DSOs from a brokerage perspective. Specifically, DSOs endeavor to coordinate prosumers in the local market by setting a fair transactive price as incentive signals. Meanwhile, to compensate for the operation cost (e.g., power line loss), DSOs need to charge commission. We investigate how to compute the optimal transactive price in a privacy-preserving way by using a distributed algorithm and set a fair service fee, which attracts more power transactions in the local market as well as reduces the operation cost of DSOs. Moreover, to accelerate the distributed algorithm, we derive an optimal transactive price updating rule for DSOs, and the closed-form expression of the optimal strategy for each prosumer with respect to the transactive price. Numerical results on Southern California Edison 56-bus feeder and IEEE 123-node test feeder show that the proposed power trading management for DSOs from a brokerage perspective leads to a TRIPLE win. With the proposed mechanism, the prosumers' payoffs largely increase by as much as 192.85%. Then, the prosumers are more willing to exchange power locally, which significantly reduces the increasing transmission burden in transmission networks of power systems. Moreover, DSOs obtain compensation by charging a service commission and reduce their operating costs.

Power flow traceable P2P electricity market segmentation and cost allocation

This study explores peer-to-peer (P2P) electricity trading, emphasizing not just the export and consumption, but also the feasible physical supply of electricity and the use of distribution network assets. Building on a transaction-oriented dynamic power flow tracing model, a novel P2P market architecture is proposed. This architecture integrates the electricity market with the power network, considering technical constraints, network losses, and asset usage. The network is segmented into potential markets using second-order cone programming (SOCP), with an optimization problem introduced for loss-allocation. This problem merges network physical analysis and variable outputs from distributed energy resources (DERs). A graph-based P2P electricity trading model is designed to determine optimal transaction cost allocation and maximize benefits for both DERs and consumers. A case study on a modified IEEE 33-node test feeder substantiates the benefits of this market structure, demonstrating increased revenues for DERs and reduced bills for consumers compared to traditional feed-in-tariffs.

An Efficient Modular Optimization Scheme for Unbalanced Active Distribution Networks With Uncertain EV and PV Penetrations

Integrated control strategies with Network Reconfiguration (NR), Demand Response (DR), and voltage control can reduce peak demand, energy loss, and system-wide unbalances in modern three-phase active Electric Distribution Networks (EDNs). However, simultaneous handling of these strategies is computationally complex and challenging. This paper presents a stochastic optimization formulation that slices the problem, solves the sub-problems separately, and splices them back to find optimal solutions efficiently. At the outset, all constraint-violating configurations are eliminated using a graph-theory-based edge traversal search developed from the classic Knuth’s Algorithm-S. Subsequently, deploying suitable indices, cyclic NR-DR assignments are performed to find near-optimal topologies and load schedules concerning the minimum loss, peak load, and unbalances. Further, Voltage Regulators (VRs) are set to achieve loss and unbalance reduction with minimal tap operations. The proposed scheme is tested on the modified IEEE 123-node test feeder with extensive EV and PV penetrations. The results show that this modular scheme provides superior solutions with an almost 75% reduction in time than the conventional co-optimization method. Various other case studies illustrate the effectiveness of the proposed scheme.

A New Digital Twins-Based Overcurrent Protection Scheme for Distributed Energy Resources Integrated Distribution Networks

This paper presents a novel overcurrent protection scheme based on digital twins for a distribution network with distributed energy resources. A coordination protection standard is employed to perform settings and coordinate intelligent electronic devices, evaluating the effects of distributed energy resources. In addition, some integration criteria for distributed energy resources are proposed to identify the impact on overcurrent protections. The power hardware-in-the-loop (PHIL) scheme is designed to develop digital twins (DT) that connect the real relays to the simulated network. Moreover, a standard for substation automation is employed to define the communication protocol for reading Generic Object-Oriented Substation Events (GOOSE) messages. Furthermore, the IEEE 13-node test feeder is employed to validate the method and model in the real-time simulation software. The results show a miscoordination of the overcurrent protection scheme installed in the distribution network with the action of different distributed energy resources.

Optimal Defensive Strategy for Power Distribution Systems Against Relay Setting Attacks

Improving the cyber security of critical protection devices is of paramount importance for reducing the impact of cyber attacks on power distribution system. This paper proposes a game-theoretic approach to identify the optimal defensive strategy and scheme to minimize the impact of relay setting attack on power distribution systems. To this end, two types of attacks, namely active and passive relay setting attacks (RSA), are introduced and modeled as the potential threats to the protection relays. The active RSA impacts the distribution system immediately, while passive RSA disrupts the relay coordination and escalates the outages resulted from future system faults. The optimal budget allocation problem for strengthening the security of relays is modeled by formulating the interaction between the defender, the attacker and the distribution system operation as a tri-level optimization problem, which is then cast into a single-level operation and a bi-level defender-attacker problem. The bi-level problem is further reduced to an expected energy loss minimization problem using an auxiliary constrained parameter. The proposed approach is tested on the IEEE 123-node test feeder, the results of which exhibit the effectiveness of the proposed model on determining the most vulnerable relays and reducing the expected energy not served.

Research on Optimal Voltage Control of Distribution Network with the Participation of EVs and PVs

With the accelerating penetration of photovoltaics (PVs) and electric vehicles (EVs), distribution networks face the risks of voltage violations and fluctuations. On the one hand, conventional voltage regulation resources like OLTC transformers and capacitor banks feature slow response and limited lifetime duration, making them incapable of quickly responding to the temporary voltage issues created by PVs and EVs. On the other hand, EVs and PVs interact with the power grid via fully controllable power electronic converters capable of real-time adjusting their operating settings, making them ideal voltage support resources. To exploit the voltage support capability of PVs and EVs, this paper proposes a two-stage control scheme for the voltage regulation of distribution networks, consisting of the day-ahead and intraday control stages. The day-ahead control mitigates potential voltage violations via day-ahead scheduling of the operation settings for OLTC transformers and capacitor banks. The intraday control further alleviates voltage deviations and voltage fluctuations based on the reactive power support of PV systems and the rational EV charging/discharging scheduling. A rolling optimization-based control technique is proposed in the intraday control stage to achieve real-time control of EVs and PVs with the stochastic nature of EV charging behaviors inherently considered. The proposed two-stage voltage regulation scheme is validated via case studies performed on the IEEE 123-node test feeder integrated with PVs and EVs.

Low Computational Burden Adaptive Overcurrent Protection for Active Distribution Networks

Conventional distribution networks have evolved into active distribution networks due to the high penetration of distributed energy resources. These new networks have several protection issues; some are related to the high fault and load current variations due to the various operating conditions. This situation is faced by adaptive protection development, as proposed in this study, where variations among active distribution network operating conditions are considered to estimate the adaptive pickup current. This is used to estimate the overcurrent relay’s time dial setting, avoiding miscoordination. The proposed protection approach uses fast calculations and local measurements; consequently, a low computational burden is required to update the adaptive relay parameters for each operating condition without using communication infrastructure. The obtained results in the IEEE 34-nodes test feeder demonstrate the advantages of the proposed approach, accomplishing the coordinating time interval for phase and ground faults. These results show a high performance of primary and backup protection at different operating conditions of the proposed test system compared to the performance of the conventional protection approach. The proposed approach’s high performance, low computational burden, and communicationless characteristics make it suitable for immediate real-field implementation.

Estimation Method of Short-Circuit Current Contribution of Inverter-Based Resources for Symmetrical Faults

This paper proposes a practical approach to estimate the symmetrical short-circuit current (SCC) levels in overcurrent protection devices (OCPDs) installed on radial feeders for any penetration level of inverter-based distributed energy resources (DERs). The proposed method restores the lost phase protection coordination by estimating SCC values and changing the TMS of OCPDs accordingly. The method is validated by comparing the results with simulations on the IEEE 34-Node Test Feeder using MATLAB/Simulink, which shows an average error of 1.5% and a maximum error of 3.0%. For a 100% penetration level, the SCC variation through OCPDs installed on the main fault trunk (MFT) exceeds ± 10%, leading to compromised phase protection coordination. The SCC flowing reversely through OCPDs on lateral branches and the fault on the MFT could cause improper tripping. Higher SCC levels are estimated and measured for fault impedances equal to zero. The phase protection is restored by changing the TMS of OCPDs using the estimated values. The study proposes two phase protection schemes to accommodate inverter-based DERs injecting 1.2 pu and 2.0 pu of SCC for a 100% penetration level. This study contributes to improving the protection coordination of distribution networks with high penetration levels of DERs. The findings have practical implications for distribution system operators and planners to maintain safe and reliable operation of distribution feeders.

A Robust Model Predictive Control-Based Scheduling Approach for Electric Vehicle Charging With Photovoltaic Systems

This article presents a robust model predictive control (R-MPC)-based scheduling approach for electric vehicle (EV) charging service at a station with power supply from a photovoltaic (PV) system and grid. The designed algorithm aims to maximize the operational revenue while handling high power loads in the grid, intermittent solar power supply, charging demands uncertainties, and online computational burdens. To this end, the scheduling problem is formulated as a mixed-integer nonlinear program (MINLP) with solar power generation and charging demands models embedded. The time-of-use price is employed for cost calculation. The resulting optimization problem then can be further simplified as a mixed-integer linear program (MILP). The proposed R-MPC is evaluated in the IEEE-13 node test feeder with simulated power load profiles and real charging demands data for demonstrations. The results show that R-MPC achieves near-optimal profit and meets all charging requests even under high uncertainties.

Master-slave strategy based in artificial intelligence for the fault section estimation in active distribution networks and microgrids

Fault location plays an essential role in the integration of self-healing functionalities in active distribution networks and microgrids. However, the fault location methods formulation presents great challenges for these types of networks because the operating changes that occur them, such as changes in topology, DER connection/disconnection and microgrids operating modes. Several fault location solutions have been proposed; nevertheless, these are strongly dependent on robust communication systems. This paper presents an artificial intelligence-based master–slave strategy for the estimation of the fault section in active distribution networks and microgrids using dispersed measurements. The strategy is composed by two stages. The master stage uses a genetic algorithm that determines the location and number of devices which maximize the faulted location e performance. The slave stage uses artificial neural networks to predict the fault section by using local voltage and current measurements trough an intelligent electronic device (IED). This approach is useful because it neglects the need of a robust communication systems and synchronization process between measurements. Here, each IED estimates the faulted section and then sends it through the single communication system to the distribution system operator control center. The presented method is validated on the modified IEEE 34-nodes test feeder where the accuracy of the strategy was 95%. The results obtained and its easy implementation indicate potential for real-life applications.

Cyberattack Correlation and Mitigation for Distribution Systems via Machine Learning

Cyber-physical system security for electric distribution systems is critical. In direct switching attacks, often coordinated, attackers seek to toggle remote-controlled switches in the distribution network. Due to the typically radial operation, certain configurations may lead to outages and/or voltage violations. Existing optimization methods that model the interactions between the attacker and the power system operator (defender) assume knowledge of the attacker’s parameters. This reduces their usability. Furthermore, the trend with coordinated cyberattack detection has been the use of centralized mechanisms, correlating data from dispersed security systems. This can be prone to single point failures. In this paper, novel mathematical models are presented for the attacker and the defender. The models do not assume any knowledge of the attacker’s parameters by the defender. Instead, a machine learning (ML) technique implemented by a multi-agent system correlates detected attacks in a decentralized manner, predicting the targets of the attacker. Furthermore, agents learn optimal mitigation of the communication level through Q-learning. The learned attacker motive is also used by the defender to determine a new configuration of the distribution network. Simulations of the technique have been performed using the IEEE 123-Node Test Feeder. The simulation results validate the capability and performance of the algorithm.

OPTIMAL PLACEMENT AND SIZING OF ELECTRIC VEHICLE CHARGING INFRASTRUCTURE USING DC POWER FLOW MODEL

With the burgeoning interest in electric vehicles (EVs) due to their sustainable attributes, concerns arise regarding the electrical grid’s capacity to handle the consequent rise in electricity demand from charging stations. Ontario’s aspiration to ensure that 5% of all vehicle sales are electric by 2020, driven by the province’s Climate Change Action Plan, accentuates these concerns, particularly with the potential rise in fossil fuel power generation. This study delves into the optimization of generator outputs and the strategic placement and sizing of EV charging stations in Ontario. The goal is to curtail overall generation costs, adhering to the demand, generation, and transmission constraints. Through the utilization of a representative system, modeled after the IEEE 34-node test feeder due to data unavailability, the research explores Ontario’s power dynamics over a 24-hour period in 2020. The findings provide insights into ideal locations and dimensions for charging stations, while also quantifying the environmental ramifications of the increased electrical grid load. This paper offers a comprehensive strategy to mitigate grid stress while bolstering EV infrastructure efficiently.

Economic dispatch of an islanded microgrid

Microgrids are increasingly becoming popular to improve energy access and increase the resiliency of weakly connected rural networks. The economic operation of these microgrids with renewable energy resources is key for maximizing the benefits. The objective of this paper is to implement the economic dispatch of a microgrid using quadratic programming, considering the active and reactive power capability of the renewable energy resources. The open distribution system simulator (OpenDSS) is used for obtaining the load flow of the distribution network, and the converged solution is given as input to MATLAB for optimization. The simulation is carried out for one day with a variation of hourly load, solar PV radiation, and wind in both grid-connected and islanded modes under six different cases. The methodology is implemented on a modified IEEE-13 node test feeder distribution system. The simulation result shows that the microgrid with distributed generation (DG) capable of supplying both active and reactive power under islanded conditions is economical when compared to the grid-connected mode. The novelty of the work is that it considers the capability of distributed generation to supply both active and reactive power. That can make it fully autonomous as it can meet the hourly load requirement of the network under islanding mode as per IEEE Standard 1547.4.