Under-frequency Load Shedding Research Articles

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.

Selective optimal under-frequency load shedding based on trajectory sensitivities

This paper proposes an optimal selective under-frequency load shedding strategy to perform a corrective control action that prevents the loss of frequency stability. The proposal decomposes this optimal load shedding problem into two mutually connected subproblems: the frequency stability analysis and the optimal load shedding. These two subproblems are sequentially solved until the minimum load shedding needed to recover the center-of-inertia frequency close to its nominal value is determined. The link between those two subproblems is a frequency performance index, which indicates the frequency deviation at each generation node with respect to the nominal frequency value. To selectively perform the load shedding, two novel methods are developed to identify which nodal loads most affect the frequency dynamics and to determine how the selected loads must be shed to return the frequency close to its nominal value at the maximum rate of change. The effectiveness of the proposed method is numerically demonstrated in two benchmark power systems.

Robust optimal design of UFLS schemes in island power systems

Since settings of conventional underfrequency load shedding (UFLS) schemes cannot be adjusted in real-time, this paper proposes a novel robust optimal design method of the UFLS scheme for an island power system (IPS) that once fixed, gives an adequate performance in all possible operating conditions in the IPS. The core of the novel robust optimal design method is the implementation of a synthetic data generation algorithm that covers all the feasible generator operating points, which are further classified into a set of large number (but computationally manageable) representative generation dispatches using clustering techniques. A real-world Spanish island will be used to illustrate the method, where the UFLS robust design will be tested and compared with non-robust UFLS designs tuned using generation dispatches of an actual day of operation provided by both standard and frequency-constrained unit commitment algorithms. Results will show that the proposed robust design avoids frequency constraints violations in contingencies and obtains better frequency metrics.

A methodological assessment to determine the frequency response of utility-scale deloaded PV under variability

Replacing the traditional synchronous generators with photovoltaic systems (PV) causes an overall reduction in grid inertia. Further, as the newly installed PV plants do not require any rotational mechanism to generate active power, they are not also able to ramp the generation during contingency situations leading to frequency collapse of the grid. To deal with this issue, a portion of peak solar generation can be kept as deloaded reserve which can be released during emergency conditions emulating the conventional system. However, the allocation of deloading as a percentage of PV power can introduce randomness on the expected support as the dispatched PV power varies depending on the irradiance and temperature values. Therefore, it is essential to accurately estimate the frequency support of a given deloading percentage to maintain frequency stability considering PV variability. In this regard, this study presents a generalized approach to determine the frequency response of deloaded support under utility-scale PV integration. To achieve this, linear and quadratic regression are employed to establish a mathematical correlation using the data derived from dynamic simulations conducted on a modified IEEE 39 bus grid at various PV penetration levels. The obtained results indicate a minimum generation of PV power is required to prevent under-frequency load shedding for a fixed deloading percentage. These findings are later verified by comparing them with two other techniques - battery energy storage system and synchronous condenser based on the simulated results achieved from DIgSILENT PowerFactory software. Moreover, this paper provides insights into how this required minimum PV generation changes with the variation in sanctioned deloading percentage. This can aid the grid designers to allow proper deloaded operation depending on the environmental condition.

Under-frequency load shedding setting method of new energy sending-end system based on improved IMQ-GWO algorithm

Aiming at the problems of low qualification rate, poor stability, and insufficient timeliness of the existing under-frequency load shedding implementation scheme at the new energy sending end, this paper proposes an under-frequency load shedding setting method for the new energy sending end system based on the improved IMQ-GWO algorithm. Firstly, considering the fluctuation of the trend of the line of the sending end system relative to the load curve of the whole network, the index system of the implementation scheme based on the under-frequency load shedding is established. Secondly, the objective function is established for the index system of the under-frequency load shedding scheme, and the under-frequency load shedding setting model is established considering the static constraints and transient constraints of the system. Then, the grey wolf optimization algorithm (GWO) is updated by using the characteristics of the IMQ function. At the same time, the individual position is adaptively updated based on the Sigmoid exponential function to better search and optimize the solution space of the problem. Finally, based on the modified IEEE39 node, the empirical analysis is carried out, and the improved IMQ-GWO algorithm proposed in this paper is used to solve the under-frequency load shedding setting model. It is proved that the proposed method is superior to other methods in terms of execution qualification rate, stability, and application timeliness.

Optimal adaptive decentralized under-frequency load shedding for islanded smart distribution network considering wind power uncertainty

The increasing share of renewable distributed generations has raised a challenge about the frequency stability. Under-frequency load shedding (UFLS) can deal with this challenge, but it usually considers a prorated load-shedding strategy and neglects the impact of uncertainty from renewable generation. This may result in unnecessary additional economic losses from the system blackout. In this paper, a risk-decision-based optimal adaptive decentralized UFLS method is proposed to coordinate the distribution of shedding load in the smart distribution network to avoid unnecessary additional economic losses, while considering the wind power uncertainty. Firstly, the uncertainty from wind power generation is modeled by confidence intervals and evaluated by the calculation of the conditional value at risk (CVaR). Then, to economically distribute the power deficit among load agents, a decentralized consensus on the incremental cost among decentralized agents is reached, where the interruption cost of the local agent varies with its load as a nonlinear relationship. Finally, a two-layer adaptive risk-decision decentralized UFLS control framework is proposed to enable the frequency stability and economic operation of the smart distribution network. With the proposed strategy, local information sharing and important global information discovery are achieved in a distributed way. Simulations on an improved IEEE 33-bus system show that the proposed strategy can effectively avoid rigid shedding and implement the optimal allocation of the system power deficit.

A Coordinated Control Strategy of Multi-Type Flexible Resources and Under-Frequency Load Shedding for Active Power Balance

With the increasing expansion of power systems, there is a growing trend towards active distribution networks for decentralized power generation and energy management. However, the instability of distributed renewable energy introduces complexity to power system operation. The active symmetry and balance of power systems are becoming increasingly important. This paper focuses on the characteristics of distributed resources and under-frequency load shedding, and a coordinated operation and control strategy based on the rapid adjustment of energy storage power is proposed. The characteristics of various controllable resources are analyzed to explore the rapid response capabilities of energy storage. The energy storage types are categorized based on the support time, and the final decision is achieved with power allocation and adjustment control of the energy storage system. Additionally, a comprehensive control strategy for under-frequency load shedding and hierarchical systems is provided for scenarios with insufficient active support. The feasibility of the proposed model and methods is verified via a multi-energy system case.

Microgrid-Based Small Modular Reactor for a High-Renewable-Energy Penetration Grid in Ghana

Achieving the necessary energy balance entails the capacity to adapt both power supply and demand, which is known as flexible operation. At present, the Ghana National Commission on Culture depends on a combination of well-coordinated measures designed to uphold the system’s integrity when confronted with abnormal system conditions stemming from significant disturbances within the system. The set of coordinated measures comprises manual (controlled load shedding) and automatic (Special Protection Schemes, Under-Frequency Load Shedding, Over-Frequency Control Scheme, scheme against voltage collapse, etc.) steps to control important system parameters to keep the power system stable and cascading effects leading to major blackouts. Ghana’s system suffers from voltage and frequency degradation as a result of a mismatch of power supply and demand; to maintain grid stability and avoid power outages, a flexible operation is required for real-time supply and demand balancing. SMRs, with their load-following capability, can adjust their output to match the change in demand. This research proposes a microgrid-based design for a high-renewable-energy penetration grid in Ghana with the inclusion of an SMR for voltage stability.

Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection

Under-frequency load shedding (UFLS) schemes are the latest safety measures applied for safeguarding the integrity of the grid against abrupt frequency imbalances. The overall inertia of electrical power systems is expected to decrease with an increased penetration of renewable energy as well as elements connected through power electronic interfaces. However, voltage source converter-based high voltage direct current (VSC-HVDC) links can provide virtual inertia through a control loop that allows for a reaction to occur at certain frequency fluctuations. This paper evaluates a UFLS scheme that considers the injection of virtual inertia through a VSC-HVDC link. A genetic algorithm (GA) is used to determine the location of the UFLS relays, the activation threshold of each stage, the delay time and the percentage of load shedding at each stage. It was found that the virtual inertia causes the nadir to delay and sometimes reach a greater depth. Furthermore, the implemented GA approximates the frequency response to the limits set with the constraints, reducing the load shedding but achieving a steeper nadir and a lower steady-state frequency level than traditional UFLS. The simulations were performed using the IEEE 39-bus test system.

Under-Frequency Load Shedding for Power Reserve Management in Islanded Microgrids

Under-Frequency Load Shedding for Power Reserve Management in Islanded Microgrids

A Multitask Integrated Deep-Learning Probabilistic Prediction for Load Forecasting

Spinning reserve without accurate load forecasting can lead to automatic disconnection of critical loads by under-frequency load shedding devices. Such a predicament poses a grave threat to the economic and social welfare of the affected region, and in extreme scenarios, can result in a debilitating grid collapse. However, existing models lack the deep feature extraction capability and cannot accurately predict the uncertainty of electricity demand. Thus, a multitask integrated deep-learning probabilistic prediction with multidimensional feature extraction based on granularity information and quantile regression is explored in this study to solve the insufficient feature extraction problem in probabilistic prediction. In the proposed scheme, we designed a multi-layer feature extraction framework, which information granularity based on fuzzy rough sets to extract time-domain features, multilayer Laplace operators to extract features globally, and quantile regression recurrent neural network variants to extract time context information. In addition, the Pareto integration method is extended to capture better individuals. The experimental results demonstrate that the proposed system can effectively improve deterministic forecasting and uncertainty analysis of electricity demand and manage daily fluctuations.

Large-Scale BESS for Damping Frequency Oscillations of Power Systems with High Wind Power Penetration

With the high penetration of renewable energy into power grids, frequency stability and oscillation have become big concerns due to the reduced system inertia. The application of the Battery Energy Storage System (BESS) is considered one of the options to deal with frequency stability and oscillation. This paper presents a strategy to size, locate, and operate the BESS within the power grid and, therefore, investigate how sizing capacity is related to renewable energy penetration levels. This paper proposes an identification method to determine the best location of the BESS using the Prony method based on system oscillation analysis, which is easy to implement based on measurements while actual physical system models are not required. The proposed methods for BESS size and location are applied using MATLAB/Simulink simulation software (version: R2023a) on the Kundur 2-area 11-bus test system with different renewable energy penetration levels, and the effectiveness of the applied method in enhancing frequency stability is illustrated in the study cases. The case studies showed a significant improvement in steady-state frequency deviation, frequency nadir, and Rate of Change of Frequency (ROCOF) after implementing BESS at the selected bus. The integration of BESS can help to avoid Under-frequency Load Shedding (UFLS) by proper selections of size, location, and operating strategy of the BESS within the power grid.

Optimal placement of BESS in a power system network for frequency support during contingency

In this work, a strategy is proposed for the optimal placement of a Battery Energy Storage System (BESS) in a power system network for frequency support during a power system contingency. It is an optimization algorithm that considers the best location for the integration of a BESS for a frequency support as the place that will result in a minimum rate of change of frequency (RoCoF) during a power system disturbance. The formulation (which aims at determining the minimum RoCoF during contingency) was based on the inertia constant contributions and active power injections (during contingency) from a mixed power generation sources of conventional power plants (CPPs), wind power plants (WPPs) and a battery energy storage system (BESS). Three different optimization solvers, particle swarm optimization (PSO), Fmincon MATLAB solver, and genetic algorithm (GA)) were used in solving the optimization problem for the purpose of comparing the results obtained in order to choose the minimum RoCoF value among the three optimization solvers. The proposed methodology was tested using two network models namely a modified 12-bus and 53 -bus (Western Cape) test systems each consisting of CPPs, WPPs and BESS as energy sources. Simulation results show that when BESS is placed on its optimal location of bus 61 (using the modified 53-bus Western Cape Network model) the power imbalance due to contingency was reduced to about 38 % of its maximum value of 4791 MW and consequently the system frequency nadir was improved from 49.49 Hz to 49.60 Hz. This is very necessary in order to keep the system frequency from falling beyond the limit that may activate the underfrequency load shedding relays.

A multi‐area design of under frequency load shedding schemes considering energy storage system

AbstractThis paper presents a comprehensive under frequency load shedding (UFLS) model that can be implemented in a multi‐area power system with real network characteristics. Conventional single‐area UFLS models operate on the basis of an equivalent center‐of‐inertia (COI) model, which ignores the local dynamics of system frequency response (SFR) and the impacts of load shedding location. Unlike the single‐SFR model, that is commonly utilized in previous works, the suggested multi‐area or multi‐SFR UFLS plan of this research has the distinct benefit of taking into account the dynamics of power transfer across different electric areas. The proposed multi‐area UFLS design incorporates a flywheel energy storage system (FESS) to support the inertial system frequency response and alleviate more than 30% load shedding while improving the frequency nadir by 25%. In order to investigate the performance of the proposed method under high penetrations of inertia‐free renewables, the inertia of the power network is reduced by around 30%; therefore the proposed UFLS scheme is assessed under a low inertia scenario. The proposed multi‐stage UFLS scheme is formulated as a mixed‐integer linear programming problem, and the optimal settings, including frequency set‐points and load shedding, are then optimized. The efficiency of the proposed model is verified using the IEEE‐118 Bus dynamic test system.

A RoCoF-constrained underfrequency load shedding scheme with static voltage stability-based zoning approach

The modern power system is proliferating in terms of size, capacity, and nature of integrated components. Such expansion engendered an increasing risk of system strength and security, posing a more significant operational challenge to power system operators (PSO). The frequency dynamics of the system under numerous possible contingencies serve as a metric to evaluate how efficiently the grid is being controlled. There are a couple of defensive mechanisms in case of inadvertent contingency events, namely - inertial response, governor control, underfrequency load shedding, and dispatch control. Inertial response and governor control institute the primary line of defense to arrest the frequency variation and Rate of Change of Frequency (RoCoF) and are triggered automatically from the dispatched machines. However, the final defense mechanism - load shedding (LS)– is designed and often controlled by the operators. There are numerous methodologies of how LS can be designed and implemented in a power system, many of which rely on some manipulation of historical operational data. Although it has been a widespread practice for PSOs to divide the grid into several zones for higher operation and control advantage, there has been very little research on how LS mechanisms can make use of the zonal allocation to tend to the local grid needs for more secure and reliable network performance. To this extent, this paper proposes a static voltage stability-based zoning mechanism using Fisher-Jenks natural breaks algorithm. The zoning scheme uses the reactive power margin of buses to cluster them into several zones. Subsequently, unique underfrequency load shedding (UFLS) schemes are devised for each zone stemming from the frequency response characteristics of that zone, particularly RoCoF. The proposed RoCoF-constrained zonal UFLS scheme is tested on IEEE Standard 39 bus network. The zonal scheme not only shows significant improvement in frequency response in individual zones over conventional UFLS schemes but also achieves that with less load shedding throughout the grid.

Hierarchical under frequency load shedding scheme for inter-connected power systems

Severe disturbances in a power network can cause the system frequency to exceed the safe operating range. As the last defensive line for system emergency control, under frequency load shedding (UFLS) is an important method for preventing a wide range of frequency excursions. This paper proposes a hierarchical UFLS scheme of “centralized real-time decision-making and decentralized real-time control” for inter-connected systems. The centralized decision-layer of the scheme takes into account the importance of the load based on the equivalent transformation of kinetic energy (KE) and potential energy (PE) in the transient energy function (TEF), while the load PE is used to determine the load shedding amount (LSA) allocation in different loads after faults in real-time. At the same time, the influence of inertia loss is considered in the calculation of unbalanced power, and the decentralized control center is used to implement the one-stage UFLS process to compensate for the unbalanced power. Simulations are carried out on the modified New England 10-generator 39-bus system and 197-bus system in China to verify the performance of the proposed scheme. The results show that, compared with other LSA allocation indicators, the proposed allocation indicators can achieve better fnadir and td. At the same time, compared with other multi-stage UFLS schemes, the proposed scheme can obtain the maximum fnadir with a smaller LSA in scenarios with high renewable energy sources (RES) penetration.

A Binary Archimedes Optimization Algorithm and Weighted Sum Method for UFLS in Islanded Distribution Systems Considering the Stability Index and Load Priority

This study proposes an under-frequency load-shedding (UFLS) scheme based on a binary Archimedes Optimization Algorithm (BAOA) and the Weighted Sum Method (WSM) to maintain the stability of an islanded distribution system. These methods consider stability indices and load priorities to ensure effective load shedding during frequency deviations. The BAOA determines the optimal load shedding based on the stability index and power mismatch that minimizes the impact on critical loads while maintaining system stability in an islanded distribution system. The WSM determines the rank of the load to be shed based on four criteria: the load priority, the load category, the stability index, and the load size. Each load is assigned a weight based on its priority. These weight variables determine the order in which loads are shed during frequency deviations. The effectiveness of the proposed UFLS was tested on an 11 kV Malaysian distribution network with two mini hydro distributed generation systems. A comparative study was conducted based on five result outputs, including the number of loads shed, the size of the loads shed, the frequency undershoot, the frequency overshoot, and the time taken to achieve a stable frequency in three cases: base load, peak load, and peak load with photovoltaics (PV). The proposed UFLS showed the best results for 11 of 15 outputs (73.3%) for islanding events and 9 of 15 outputs (60%) for overloading events. The voltage profile and stability index, also, were improved after the proposed UFLS was applied.

Proactive Frequency Stability Scheme: A Distributed Framework Based on Particle Filters and Synchrophasors

The reactive nature of traditional under-frequency load shedding schemes can lead to delayed response and unnecessary loss of load. This work presents a proactive framework for power system frequency stability. Bayesian filters and synchrophasors are leveraged to produce predictions after disturbances are detected. By being able to estimate the future state of frequency corrective actions can be taken before the system reaches a critical condition. This proactive approach makes it possible to optimize the response to a disturbance, which results in a decrease in the amount of compensation utilized. The framework is tested via Matlab simulations based on Kundur’s Two-Area System, and the IEEE 14-Bus System. Performance metrics are provided and evaluated against other contemporary solutions found in literature. During testing this framework outperformed other solutions by drastically reducing the amount of load dropped during compensation.

Highly transferable adversarial attack against deep‐reinforcement‐learning‐based frequency control

AbstractWith the increase in inverter‐based renewable energy resources, the complexity and uncertainty of low‐carbon power systems have increased significantly. Deep reinforcement learning (DRL)–based approaches have been extensively studied for frequency control to overcome the limitations of traditional model‐based approaches. The goal of DRL‐based methods for primary frequency control is to minimise load shedding while satisfying frequency safety requirements, thereby reducing control costs. However, the vulnerabilities of DRL models pose new security threats to power systems. These threats have not been identified and addressed in the existing literature. Therefore, in this paper, a series of vulnerability assessment methods are proposed for DRL‐based frequency control with a focus on the under‐frequency load shedding (UFLS) problem. Three adversarial sample production methods are designed with different optimisation directions: Q‐value‐based FGSM (Q‐FGSM), action‐based JSMA (A‐JSMA), and state‐action‐based CW (SA‐CW). Furthermore, combining the advantages of the above three attack methods, a hybrid adversarial attack algorithm is designed, Q‐value‐state‐action‐based mix (QSA‐MIX), to significantly affect the decision process of the DRL model. In case studies of the IEEE39 bus system, the proposed attack methods had a severe impact on system operation and control. In particular, the high attack transferability of the proposed attack algorithms in a black‐box setting provides further evidence that the vulnerability of current DRL‐based control schemes is prevalent.

A logistic regression approach for improved safety of the under-frequency load shedding scheme owing to feeder machine inertia

Under Frequency Load Shedding (UFLS) is the last resource to maintain the power system frequency after contingences. Load shedding can be performed by conventional, adaptive, computational or wide area monitoring techniques. The most common is a conventional UFLS. Load shedding is accomplished when predetermined frequency thresholds are reached. Although the conventional methods are simple, easily implemented and dependable, they may operate improperly. In the Brazilian electric power system, several actions of the UFLS scheme were found to be improper owing to the dynamics of induction motors and distribution generation during the main source outage of distribution feeders. In this way, when the main source is out of service, machine inertia maintains the frequency for some electric cycles, leading the frequency relay to undue tripping. In this paper, the negative sequence voltage and the rate of change of frequency are handled by a logistic regression method to identify the events of improper action. The proposed method was evaluated through the modified IEEE 9-bus test system, and several cases have been tested. The proposed method reached a level of performance higher than 99% of correct operation, and it needs a small training set.