Undervoltage Load Shedding Research Articles

Emergency voltage control strategy for power system transient stability enhancement based on edge graph convolutional network reinforcement learning

Emergency control is essential for maintaining the stability of power systems, serving as a key defense mechanism against the destabilization and cascading failures triggered by faults. Under-voltage load shedding is a popular and effective approach for emergency control. However, with the increasing complexity and scale of power systems and the rise in uncertainty factors, traditional approaches struggle with computation speed, accuracy, and scalability issues. Deep reinforcement learning holds significant potential for the power system decision-making problems. However, existing deep reinforcement learning algorithms have limitations in effectively leveraging diverse operational features, which affects the reliability and efficiency of emergency control strategies. This paper presents an innovative approach for real-time emergency voltage control strategies for transient stability enhancement through the integration of edge-graph convolutional networks with reinforcement learning. This method transforms the traditional emergency control optimization problem into a sequential decision-making process. By utilizing the edge-graph convolutional neural network, it efficiently extracts critical information on the correlation between the power system operation status and node branch information, as well as the uncertainty factors involved. Moreover, the clipped double Q-learning, delayed policy update, and target policy smoothing are introduced to effectively solve the issues of overestimation and abnormal sensitivity to hyperparameters in the deep deterministic policy gradient algorithm. The effectiveness of the proposed method in emergency control decision-making is verified by the IEEE 39-bus system and the IEEE 118-bus system.

A new comprehensive framework for cascading outage simulation in power systems

Simulating accurately and fast cascading outages play a critical role in power system security and resilience assessment. This paper proposes a new comprehensive framework for cascading outage simulation, articulated around an extended AC power flow. To address the key modelling features of cascading outage simulation, the proposed framework develops new algorithms for: a) detecting possible islanding, b) frequency control in each island: primary frequency control (PFC), under-frequency load shedding (UFLS) and over-frequency generator tripping (OFGT) and c) improved under-voltage load shedding (UVLS) and load models to avoid the divergence of AC power flow and allow handling voltage instability during the cascade. Tests on a realistic 60-bus system show that the proposed cascading outage simulator can successfully handle islanding, PFC, UFLS, OFGT, and UVLS; it simulates the cascade until there is no violation of system frequency, voltage, and power flow limits in islands that do not experience blackout.

A Novel Centralized Load Shedding Approach to Assess Short-Term Voltage Stability: A Model-Free Using Time Series Forecasting

In the wake of ever-growing integration of dynamic loads and power electronics-interfaced devices, power systems are becoming more complex with growing nonlinear behaviors, and preserving these systems' dynamic stability is a crucial challenge more than ever. Under voltage load shedding (UVLS) is one of the protection schemes adopted to enhance stability of the system at the events jeopardizing the system voltage. Despite underway efforts to design UVLS using analytical approaches/models, it is barely possible to estimate their various parameters with sufficient accuracy. Also, due to steady changes in characteristics of elements, especially loads, the estimated model at a time is not legitimate long lasting. Eluding these difficulties, this paper presents a novel data-driven UVLS scheme with a centralized design to enhance short-term voltage stability during fault-induced delayed voltage recovery (FIDVR) events. The proposed methodology utilizes time series forecasting in conjunction with the long short-term memory neural network. In order to quantize FIDVR severity, a novel dynamic index called short-term voltage index (STVI) is introduced for motor-load buses. STVI is a function of both voltage magnitude and load angle. The proposed approach is successfully verified on an actual power system model.

Particle Swarm Optimization Trained Feedforward Neural Network for Under-Voltage Load Shedding

This paper suggests an under-voltage load shedding (UVLS) approach to avoid voltage collapse in stressed distribution systems. Prior to a blackout, a failing system reaches an emergency state, and UVLS is executed as the final option to prevent voltage collapse. Hence, this article introduces an optimal UVLS method using a feedforward artificial neural network (ANN) model trained with the particle swarm optimization (PSO) algorithm to obtain the optimal load shedding amount for a distribution system. PSO is used to obtain the best topology and optimum initial weights of the ANN model to enhance the precision of the ANN model. Thus, the dispute between the optimum fitting regression of the allocation of ANN nodes and computational time was disclosed, while the MSE of the ANN model was minimized. Moreover, the proposed method uses the stability index (SI) to identify the weak buses in the system following an emergency state. Different overload scenarios are examined on the IEEE 33-bus distribution network to validate the efficacy of the suggested UVLS scheme. A comparative study is performed to further assess the performance of the proposed technique. The comparison indicates that the recommended method is effective in terms of voltage stability and remaining load.

Real-time under-voltage load shedding method considering integer-value load model and feeder participation in demand response

Real-time under-voltage load shedding method considering integer-value load model and feeder participation in demand response

An emergency control strategy for undervoltage load shedding of power system: A graph deep reinforcement learning method

AbstractUndervoltage load shedding (UVLS) is the last line of defense to ensure the safe and stable operation of the power system. The existing UVLS technique has difficulty adapting and generalizing to new topology variation scenarios of the power network, which greatly affects the reliability of the control strategy. This paper proposes a UVLS emergency control scheme based on a graph deep reinforcement learning method named GraphSAGE‐D3QN (graph sample and aggregate‐double dueling deep q network). During offline training, a GraphSAGE‐based feature extraction mechanism of the power grid with topology variation is designed that can better capture the changes in system state characteristics. A novel reinforcement learning framework based on D3QN is developed for UVLS modeling, which reduces overestimations of control action and leads to a better control effect. Then, online emergency decision‐making is achieved. The simulation results on the modified IEEE 39‐bus system and IEEE 300‐bus power system show that the proposed UVLS scheme can always provide more economical and reliable control strategies for power networks with topology variations and achieves better benefits in both adaptability and generalization performances for previously unseen topology variation scenarios.

Novel Relative Slip Based Under-Voltage Load Shedding Protection Scheme to Mitigate FIDVR

Induction motors play a vital role in the voltage stability of power systems. This role would become more significant after clearing short-circuit faults in which the system voltage slowly recovers with a time delay referred to as fault-induced delayed voltage recovery (FIDVR). The FIDVR phenomenon can be classified into the stable and unstable types. To preserve network stability and reliability, the under-voltage load shedding (UVLS) protection schemes should i) be secure for the stable case and ii) quickly remove the disturbed loads for the unstable case. To do this, a new decentralized UVLS protection scheme against FIDVR events is proposed in this paper. A novel concept named relative slip, which is equal to the ratio of the motor slip to the critical one is developed. Using the proposed concept, the behavior of motor loads during stable and unstable regions can be discriminated. Since the proposed index can be exploited just by using the electrical signals, it can be simply implemented in the UVLS schemes without requiring any additional information and infrastructure. To evaluate the efficiency of the proposed method, multiple test systems are considered. Based on various simulation studies, the proposed UVLS scheme can superiorly preserve system voltage stability.

An Improved Under-Voltage Load-Shedding Control Scheme for Power Network Based on Mean Spectral Radius Method

Under-voltage load-shedding (UVLS), which executes load shedding round by round, is one of the most effective ways to solve the under-voltage problem. However, the traditional UVLS has the problems of large load shedding and slow voltage recovery. To tackle these problems, this paper proposes an improved UVLS based on the mean spectral radius method (MSR). Firstly, the MSR of the power network is constructed by building a high-dimensional random matrix. Secondly, the voltage vulnerability of the power system is identified by building the voltage weak evaluation index based on MSR. And then, an improved UVLS control scheme is proposed based on the voltage vulnerability. Finally, the effectiveness of the proposed UVLS is validated in the IEEE 10-generator 39-node power system. The proposed UVLS has the advantages of less load shedding, better economy, and fast voltage recovery.

Dual techniques of load shedding and capacitor placement considering load models for optimal distribution system

<span lang="EN-US">Voltage stability represents one of the main issues in electrical power system. Under voltage load shedding (UVLS) has long been regarded as one of the most successful techniques to prevent the voltage collapse. However, the ordinary load shedding schemes do not consider the different load models and decreasing in the ‎economic cost that resulted from load disconnection, so the dual techniques of load shedding with reactive compensation are needed. Usually loads being modeled as constant power, while in fact of load flow the various load models are utilized. An investigation of optimal dual load shedding with reactive compensation for distribution system based on direct backward forward sweep method (DBFSM) load flow along with a comparison among the other load models are presented in this paper. The teaching learning-based optimization (TLBO) algorithm is executed in order to reduce power losses and enhance the voltage profile. This algorithm is tested and applied to IEEE-16 bus distribution test system to find the optimal superior capacitor size and placement while minimizing load shading for the network. Five different load shedding sequences are considered and the optimization performance of load models demonstrated the comparison through MATLAB program.</span>

Under voltage load shedding and penetration of renewable energy sources in distribution systems: a review

ABSTRACT The growing energy demand and the emerging environmental concerns across the globe caused increasing penetration of renewable energy sources (RESs) in distribution systems. One of the major issues faced by modern distribution systems with high RESs penetration is voltage instability. Generally, voltage instability can lead to voltage collapse, which is one of the key drivers for blackout incidents around the world. Under voltage load shedding (UVLS) schemes are usually executed as the final option to prevent a large-scale blackout or voltage collapse. But ever-increasing integration of RESs in distribution systems creates an additional threat to the existing UVLS schemes. Therefore, new UVLS methods are necessary to provide the appropriate corrective scheme for distribution systems, particularly when connected with RESs. This paper reviews and updates the recent UVLS methods emphasizing on distribution network application. In addition, the contributions and limitations of the conventional, adaptive, and computational intelligence techniques of UVLS are highlighted. Moreover, this paper provides an overview of the voltage stability indices applied for UVLS methods. Also, at the end of this review article, the further research directions to improve the safety of modern distribution systems during emergencies using UVLS are drawn as the conclusion.

Real-Time Adaptive UVLS by Optimized Fuzzy Controllers for Short-Term Voltage Stability Control

This paper proposes a novel real-time adaptive under voltage load shedding (UVLS) method by using optimized fuzzy logic controllers to address fault-induced delayed voltage recovery (FIDVR) and short-term voltage instability (STVIS) problems. An intelligent multi-input multi-output fuzzy logic load shedding controller is developed based on a short-term voltage recovery criterion (STVRC). For the controller, the magnitude deviation and the recovery time deviation of voltage trajectory are extracted as inputs, while the load shedding amount at the current load shedding stage and the time delay of the next stage are taken as outputs. Particle swarm optimization (PSO) technique is further employed for the tuning of the controller parameters to realize the voltage recovery requirement at the lowest cost of load shedding. The proposed UVLS method is formed by a group of the fuzzy logic controllers deployed at the load buses, each monitoring its own bus voltage and shedding load in closed-loop. Responding to the severity of voltage variations, the proposed method is able to adaptively determine the location, time and amount of load shedding in real-time to make the voltage satisfy the STVRC. The effectiveness of the proposed method compared to other methods is testified by simulations on the Nordic test system.

Inclusion of Pre-Existing Undervoltage Load Shedding Schemes in AC-QSS Cascading Failure Models

A challenging problem facing AC-Quasi-Steady-State (AC-QSS) cascading failure models of power system is the divergence issue primarily stemming from voltage collapse phenomena. In reality, there are undervoltage load shedding (UVLS) relays, which aim to prevent such a collapse by shedding a pre-specified fraction of load at buses where the corresponding voltages fall below a threshold. However, capturing the UVLS action in QSS models is very difficult, because most of the time the model cannot generate an equilibrium below the voltage threshold due to divergence. To address this problem, current models have applied different variants of uniform load shedding (ULS) till convergence is achieved, which differ from the ground truth. In order to solve this, we propose a methodology that leverages the post-ULS load flow as a starting point when divergence occurs. In this condition, a sensitivity index coupled with the voltage magnitudes of buses is used to recognize the buses that are most prone to voltage collapse. The UVLS scheme is then applied to these buses. To verify the accuracy of the results, we also present a suitable dynamic cascade model with appropriate limits and protection details that can selectively capture UVLS action, thereby revealing the proximate ground truth. Predictions of the proposed model are validated against those of the dynamic model for representative cases in IEEE 118-bus system. In addition, results of the proposed model are contrasted with two ULS schemes on the 2383-bus Polish system.

A new optimal under-voltage load shedding scheme for voltage collapse prevention in a multi-microgrid system

In this paper, a new optimal under-voltage load shedding (UVLS) method is presented to maintain voltage stability of a multi-microgrid (MMG) system. Great benefits are gained by exploiting MMGs, indicating the integration of microgrids (MGs) and distributed energy sources (DERs), at both medium voltage (MV) and low voltage (LV) levels. Advanced strategies should be employed in order to guarantee stability of a MMG. This paper, for the first time, presents an optimal UVLS scheme for the MMGs which considers voltage stability margin, minimum amount of load shedding and minimum operation cost of the system in the objective function, simultaneously. In addition, application of demand response programs (DRPs) and MGs/loads priorities have been addressed in the proposed method. The optimal load shedding problem is solved using hybrid discrete continuous exchange market algorithm (HDCEMA) with regard to system stability, voltage magnitudes and other related constraints. The proposed method is tested on a 55-bus MMG system consisting of 5 MGs with distributed and renewable energy sources. In order to compare results with previous methods, two UVLS schemes and two optimization algorithms in the literature are implemented on the simulated MMG system. Simulation results confirm the effective performance of the proposed method.

Under voltage load shedding using hybrid ABC-PSO algorithm for voltage stability enhancement

Voltage collapse tends to occur due to the voltage instability created during large faults. As a last resort, under-voltage load shedding (UVLS) is performed after all the available power operation and control mechanisms have been exhausted. Load shedding techniques have advanced from the conventional and adaptive methods that are less optimal compared to computational intelligence-based techniques. Recent works have identified hybrid algorithms to give more optimal solutions for UVLS problems with multi-objective functions. In this paper, a novel hybrid ABC-PSO algorithm, adapted from a software estimation project, is used to perform UVLS on a modified IEEE 14-bus system. Eight overload conditions are imposed on the system ranging from 105% to 140% loading, where FVSI ranking is used in identifying weak buses. The load shedding is then performed following decentralized relay settings of 3.5 seconds, 5 seconds and 8 seconds, which gives an overall 99.32% recovery of voltage profiles. The proposed hybrid ABC-PSO algorithm is able to shed optimal amounts of load, giving an 89.56% post-contingency load, compared to GA's 77.04%, ABC-ANN at 84.03% and PSO-ANN at 80.96%. This study has been simulated on MATLAB software, using the Power System Analysis Toolbox (PSAT) graphical user and command-line interfaces.

Dynamic under-voltage load shedding scheme considering composite load modeling

This manuscript recommends an under-voltage load shedding scheme based on the sensitivity of dynamic voltage curves to mitigate voltage collapse in highly stressed power systems. The developed method can be used to determine the minimum amount and proper locations of load curtailment by considering power system dynamics including dynamic load modeling. The selected Indonesian regional system has a large number of dynamic loads in form of air conditioning and water pump loads whose dynamics contribute considerably to the complexity of the power system stability. Loss of the transmission line in the system caused by the impact of the composite load after major disturbance is studied. The proposed scheme involves an iterative procedure based on the sensitivity analysis of the dynamic voltage curve to solve the problem of load shedding. Furthermore, this work developed a new index named dynamic voltage-active power sensitivity (DVPS). The DVPS is calculated in all load buses to give information about the bus that has a predominant influence on improving system voltage stability through load shedding. Dynamic simulations are carried out with an Indonesian power system, the South Sulawesi interconnection power system with the proposed load shedding scheme. The results of this work show a noteworthy improvement in the magnitude of the voltage level as well as the voltage stability margin.

A practical under-voltage load shedding strategy for regional power grid considering multiple operating modes

Nowadays, under-voltage load shedding (UVLS) schemes in China’s regional power grids are often drafted according to one certain operating mode, and then been chosen by experienced technicians based on comparison between simulation results on various operating modes. This process seriously depends on the skill and experience of the technician, and cannot guarantee the safety under some circumstances. Considering the above issues, this paper proposes a configuration method of UVLS applying to distributed control systems, and an automatic scheme formulating process. Specifically, aiming at developing a strategy suitable for multiple operating modes, Analytic Hierarchy Process (AHP) is introduced to obtain the fuzzy comprehensive sensitivities of the buses. The transient voltage stability of a regional power grid is investigated and the UVLS strategy is studied, which indicates the validity and practicality of the proposed configuration method.

Deep Feedback Learning Based Predictive Control for Power System Undervoltage Load Shedding

In practical power systems, it is still very challenging to figure out a cost-effective undervoltage load shedding (UVLS) scheme that can reliably and adaptively react to the short-term voltage stability (SVS) problem. Faced with this challenge, this paper develops an intelligent data-driven predictive UVLS scheme for online SVS enhancement. Inspired by valuable ideas in model predictive control and supplementary excitation control in power systems, a novel deep feedback learning machine (DFLM) is designed to precisely predict future voltage violations after UVLS execution. With the help of the DFLM, the UVLS scheme is aware of potential effects of various candidate UVLS actions. Owing to this desirable nature, it can adaptively respond to diverse SVS conditions and optimize UVLS decisions in a non-iterative way. Further, two well-designed strategies, i.e., stepwise constraint relaxation and incremental DFLM adaptation, are introduced to enhance the scheme's reliability and adaptability during online application. Numerical test results on the Nordic test system and the realistic North GZ Power Grid in China showcase the excellent performances of the proposed scheme.

Under Voltage Load Shedding on a Modified IEEE 14-Bus System Using Hybrid ABC-PSO Algorithm for Voltage Stability Enhancement

Voltage collapse tends to occur due to the voltage instability created during large faults. As a last resort, under-voltage load shedding (UVLS) is performed after all the available power operation and control mechanisms have been exhausted, in order to save a system from voltage collapse. Load shedding techniques have advanced from the conventional and adaptive techniques that were less optimal compared to computational intelligence techniques (CITs). Recent works have identified hybrid algorithms to give more optimal solutions for UVLS problems with multi-objective functions. In this paper, a novel hybrid ABC-PSO algorithm, adapted from a software estimation project, was used to perform UVLS on a modified IEEE 14-bus system. The modified system was made to reflect a real-life system with considerations on different load models. From the FVSI ranking used, 8 overload conditions were imposed on the system ranging from 105% to 140% loading. The load shedding was performed in accordance to decentralized relay settings of 3.5 seconds, 5 seconds and 8 seconds, which resulted in an overall 99.32% recovery of voltage profiles. The proposed hybrid ABC-PSO algorithm was able to shed optimal amounts of load, giving an 85.50% post-contingency load, compared to GA’s 77.04%, ABC-ANN at 84.03% and PSO-ANN at 80.96%. This study was simulated on MATLAB software, using the Power System Analysis Toolbox (PSAT) graphical user and command line interfaces.

Examination of Composite Load and Variable Frequency Drive Air Conditioning Modeling on FIDVR

Fault induced delayed voltage recovery (FIDVR) is a credible concern in power systems with high penetration of single-phase air conditioner loads. Power system planning studies use dynamic load models, such as the Western Electricity Coordinating Council (WECC) composite load model CMPLDW to examine FIDVR in power systems. The behavior of modern variable frequency drive (VFD) air conditioners to voltage drops is not the same as traditional one- or two-speed air conditioning systems. This paper investigates the impact of increasing VFD air conditioner penetration within a power system on FIDVR events, by first modeling VFD air conditioners as a separate load and then as a power electronic component within the CMPLDW model. Simulation results show that increasing the penetration of VFD driven air conditioners decreases the post-fault voltage sag, recovery time, and number of customers interrupted by undervoltage load shedding.

Resilience Analysis and Cascading Failure Modeling of Power Systems Under Extreme Temperatures

In this paper, we propose an AC power flow based cascading failure model that explicitly considers external weather conditions, extreme temperatures in particular, and evaluates the impact of extreme temperature on the initiation and propagation of cascading blackouts. Based on this model, careful analysis is performed on the resilience of the power system under extreme temperatures. Specifically, load and dynamic line rating changes are modeled due to temperature disturbance, the probabilities for transmission line and generator outages are evaluated, and the timing for each type of events is carefully calculated to decide the actual event sequence. It should be emphasized that the correlated events, in the advent of external temperature changes, could together contribute to voltage instability. Besides, we model undervoltage load shedding and operator re-dispatch as control strategies for preventing the propagation of cascading failures. The effectiveness of the proposed model is verified by simulation results on the RTS-96 3-area system and it is found that temperature disturbances can lead to correlated load change and line/generator tripping, which together will greatly increase the risk of cascading and voltage instability. Critical temperature change, critical area with temperature disturbance, identification of most vulnerable buses, and comparison of different control strategies are also carefully investigated.