Load Shedding In Power Systems Research Articles

Preface

The 2023 2nd International Conference on the Energy Internet and Energy Interactive Technology (EIEIT 2023) took place via virtual form in Nanjing, China from May 26th to 28th, 2023, with the attendance of about 50 people.EIEIT 2023 focused on research areas such as Renewable Energy, Power Quality and Power Experience, Regional Energy Internet, Energy Management Technology, etc. The main goal was to bring together scientists, researchers and developers engaged in fields concerning energy to bridge the gap between research needs and advanced technological solutions.EIEIT 2023 provided an opportunity for researchers and related personnels to present their recent theoretical developments as well as practical applications, discuss unsolved problems and find solutions to them. It fostered research and development in energy internet and energy interactive technology.The program of the Conference was divided into three parts. First day of the Conference was devoted to registration for all the participants. The second day was focused on opening ceremony, keynote speech, oral and poster presentation, as well as discussion on the speeches and the whole meeting. Academic investigation took place during the third day of the Conference.We would like to highlight the main developments presented at the Conference and introduce the prominent speakers. Keynote speaker of the Conference Professor Ismail Musirin (Universiti Teknologi MARA, Malaysia) presented a report on Computational Intelligence Applications in Power System Load Shedding. He mainly dealt with researches on the algorithms and techniques development in solving problems in Power System involving voltage stability, economic power dispatch, renewable energy optimization, and application of artificial intelligence in power system, and he has got 22 years of research experience in the field of Power System Engineering. Associate Professor Norzanah Rosmin (Universiti Teknologi Malaysia, Malaysia) described Hybrid Energy Storage for Virtual Inertial Support in Wind Application System. Associate Professor Nor Asiah Muhamad (Universiti Teknologi Brunei, Brunei) demonstrated her speech on Harmonic Components Analysis of Emitted Ultraviolet Signals of Aged Transmission Line Insulators under Different Surface Discharge Intensities.Finally, we would like to thank the members of the Technical Program Committee and the Local Committee for giving such an opportunity to share scientific ideas and new developments at the Conference and Universiti Putra Malaysia, Malaysia for its friendly support. Last but not the least, our special acknowledgement also goes to the editors and staff of Journal of Physics: Conference Series, for their kindness and assistance in publishing this paper volume.The Committee of EIEIT 2023List of Committee Member is available in this pdf.

A Review on Application of Soft Computing Techniques for Load Shedding in Power Systems

In power systems, system frequency decreases if load exceeds generation and increases when power generation is greater than load demand. Load shedding is highly required in power systems to stabilize frequency in electrical power systems. Though conventional load shedding techniques are being used in many power system applications, soft computing techniques such as artificial neural networks (ANNs), fuzzy logic, genetic algorithm and particle swarm optimization have been presented by many researchers to provide optimum load shedding. However, there are many problems associated with machine learning based control techniques in real time. In this work, the utilization of soft computing algorithms and its benefits and drawbacks are discussed. A comprehensive survey is presented about the soft computing based load shedding and comparison is made among these techniques and conventional techniques. Neural network based learning rule is applied with back propagation network to minimize the error. In fuzzy based technique, load shedding position at each node was predetermined by applying certain fuzzy rules. Genetic algorithm and particle swarm optimization are robust and applied to solve many nonlinear and multi-objective problems. ANN and fuzzy logic are combined to build an adaptive neuro fuzzy inference system to provide accurate load shedding.

An Improved Algorithm for Optimal Load Shedding in Power Systems

A blackout is usually the result of load increasing beyond the transmission capacity of the power system. A collapsing system enters a contingency state before the blackout. This contingency state is characterized by a decline in the bus voltage magnitudes. To avoid blackouts, power systems may start shedding load when a contingency state occurs called under voltage load shedding (UVLS). The success of a UVLS scheme in arresting the contingency state depends on shedding the optimum amount of load at the optimum time and location. This paper proposes a hybrid algorithm based on genetic algorithms (GA) and particle swarm optimization (PSO). The proposed algorithm can be used to find the optimal amount of load shed for systems under stress (overloaded) in smart grids. The proposed algorithm uses the fast voltage stability index (FVSI) to determine the weak buses in the system and then calculates the optimal amount of load shed to recover a collapsing system. The performance analysis shows that the proposed algorithm can improve the voltage profile by 0.022 per units with up to 75% less load shedding and a convergence time that is 53% faster than GA.

Assessing Effectiveness of Research for Load Shedding in Power System

The research on loadshedding issues dates back to 1972 and till date many studies were introduced by the research community to address the issues. A closer review of existing techniques shows that still the effectiveness of loadshedding schemes are not yet benchmarked and majority of the existing system just considers the techniques to be quite symptomatic to either frequency or voltage. With an evolution of smart grids, majority of the controlling features of power system and networks are governed by a computational model. However, till date not enough evidences of potential computational model has been seen that claims to have better balance between the load shedding schemes and quality of power system performance. Hence, we review some significant literatures and highlights the research gap with the existing technqiues of load balancing that is meant for assisting the researcher to conclude after the selection process of existing system as a reference for future direction of study.

Load Shedding in Power Systems Based on a Fuzzy Logic Decision and Sensibility of Stability Prediction and Fast Voltage Indices

Current and traditional methods of load shedding are based on threshold values of frequency and voltage; thus, when the power system falls below these values, some mechanical control actions are activated as countermeasure against stability problems. Nevertheless, in many cases the traditional mitigation methods based on load shedding cannot ensure that the network remains in continuous operation after the presence of suddenly and severe perturbations. In addition, the development of control systems of wide area based on PMU’s measurements has allowed the development of different kinds of indices for the stability prediction. Thus, on-line diagnosis of the power system about angular stability and voltage stability gives better information about the system’s situational awareness. This paper proposes a methodology that allows the system operator to take a decision if applying the load shedding as a countermeasure for system instabilities caused by severe contingencies. In addition, the methodology identifies which are the nodes where the load shedding must be applied and the quantity of load shedding to be applied. The proposed load shedding methodology is based on a fuzzy logic decision method using the stability prediction index (SPI) and fast voltage stability index (FVSI). These indices can be computed on-line and they give the stability status of several operating areas. The methodology is tested on the NYPP-NETS of 68 nodes and it is illustrated with a cascade.

Distributed control of small-scale power systems using noncooperative games

Distributed energy resources (DERs) coordination problems and load shedding in power systems can be modeled as a distributed resource allocation problem with physical constraints. The dynamic resource allocation problem can be approached using nonlinear methods based on population dynamics. This paper presents an optimization model to minimize the load curtailments needed to restore the equilibrium of the operating point when the system is in a fault condition (e.g., loss of generation). A mathematical model of the proposed strategy for the dispatch generation and an optimal load shedding algorithm are shown. The developed methodology minimizes the load cuts depending on the load relevance of each node of the system, and carrying out the power distribution of the generators under the new demand power condition using the replicator dynamics or the local replicator equation. This model can be used for the operation planning of electrical power systems with DERs. In order to illustrate our methodology, some simulations are performed using the IEEE 34 node test feeder in OpenDSS.

Comparative Analysis of Bacterial Foraging Optimization Algorithm and Evolutionary Programming for Load Shedding in Power System

Comparative Analysis of Bacterial Foraging Optimization Algorithm and Evolutionary Programming for Load Shedding in Power System

Application of fuzzy-analytic hierarchy process algorithm and fuzzy load profile for load shedding in power systems

Abstract In an electrical competitive market, load shedding decision support systems are needed to find the ways to process load shedding to satisfy both economic and technical conditions. This paper proposed the Fuzzy Analytic Hierarchy Process algorithm (Fuzzy-AHP) and fuzzy logic for load profile approach to determine the weight of the load nodes of the system and select the control strategy when the system operates at various load levels. Weights of the load nodes were calculated by triangular fuzzy numbers in the pair comparison. These results combined with the fuzzy logic for load profile to draw out the suitable control strategies for various load levels in case of emergency. The effectiveness of the algorithm was demonstrated through the experiment of load shedding of IEEE 37 bus compared with traditional AHP algorithm.

GSO based optimization of steady state load shedding in power systems to mitigate blackout during generation contingencies

Load shedding is considered as a last alternative to avoid the cascaded tripping and blackout in power systems during generation contingencies. It is essential to optimize the amount of load to be shed in order to prevent excessive load shedding. To minimize load shedding, this paper proposes the implementation of nature inspired optimization algorithm known as glowworm swarm optimization (GSO) algorithm. The optimal solution of steady state load shedding is carried out by squaring the difference between the connected and supplied power (active and reactive). The proposed algorithm is tested on IEEE 14, 30, 57, 118 and Northern Regional Power Grid (NRPG)-(India) 246 bus test systems. The viability of the proposed method in terms of solution quality and convergence properties is compared with the conventional methods, namely, projected augmented Lagrangian method (PALM), gradient technique based on Kuhn–Tucker theorem (GTBKTT) and second order gradient technique (SOGT).

Under Voltage Load Shedding Scheme Using Meta-heuristic Optimization Methods

Load shedding has been extensively studied because of multiple power system failure occurrences worldwide. Reliable techniques are required to provide rapid and precise load shedding to avert voltage collapse in power networks. Meta-heuristic optimization approaches are currently the widely developed methods because of their robustness and flexibility in dealing with complex and non-linear systems. These methods include genetic algorithm, fuzzy logic control, particle swarm optimization, artificial neural network, ant colony optimization, big-bang big-crunch optimization, and many others. This study provides an overview of all the meta-heuristic methods implemented for under voltage load shedding in power systems.

The Existing Investigation and Prospect of Under-Frequency and Under-Voltage Load Shedding in Power Systems

Under-frequency load shedding (UFLS) and under-voltage load shedding (UVLS) as the last defense of the power system to maintain its stability are of great significance, especially for the current development of large-scale interconnected power system. At first, this paper presents the existing methods of load shedding and analyses their drawbacks. Then, a brief overview of the application of wide area measurement system (WAMS) in UFLS and UVLS is provided. Finally, a novel intelligent load shedding scheme which realizes load adjustment instead of load shedding based on the characteristics of smart grid and smart home is proposed.

Application of computational intelligence techniques for load shedding in power systems: A review

Recent blackouts around the world question the reliability of conventional and adaptive load shedding techniques in avoiding such power outages. To address this issue, reliable techniques are required to provide fast and accurate load shedding to prevent collapse in the power system. Computational intelligence techniques, due to their robustness and flexibility in dealing with complex non-linear systems, could be an option in addressing this problem. Computational intelligence includes techniques like artificial neural networks, genetic algorithms, fuzzy logic control, adaptive neuro-fuzzy inference system, and particle swarm optimization. Research in these techniques is being undertaken in order to discover means for more efficient and reliable load shedding. This paper provides an overview of these techniques as applied to load shedding in a power system. This paper also compares the advantages of computational intelligence techniques over conventional load shedding techniques. Finally, this paper discusses the limitation of computational intelligence techniques, which restricts their usage in load shedding in real time.

Flexible load shedding strategy considering real‐time dynamic thermal line rating

In recent years, real-time dynamic thermal line rating (RT-DTLR) technology has been developed and implemented in operations to improve transmission line capacity, so that congestion costs and/or risk of load shedding can be reduced. Power systems obviously benefit from this technology but also bear the risks because of thermal overload of transmission lines. To involve RT-DTLR technology into conventional power system load shedding strategies, this study explains the concept of flexibility in power system analysis, expresses transmission line capacity constraints into flexible forms and proposes a flexible load shedding strategy considering RT-DTLR. The two sub-objective functions, representing load shedding quantity and system risk increment, are normalised and expressed as the confidence levels from system security consideration and load loss consideration, respectively. The proposed method takes advantage of both the advanced hardware and software technologies in smart grids and the simulation results showed that it can trade off the benefits and risks brought about by RT-DTLR in load shedding.

Under voltage load shedding in power systems with wind turbine-driven doubly fed induction generators

This paper presents the design and implementation of a new method of under voltage load shedding in a power system incorporating the use of wind generators to maintain voltage stability following a severe disturbance. This design considers the dynamic modeling of the load as well as the dynamic model of wind turbines. This work demonstrates a method to determine the minimum amount and the most appropriate location of load shedding. The proposed technique in this research involves an iterative algorithm based on trajectory sensitivity analysis to solve the load shedding problem. The load to be shed for each iteration is set at a small amount. Furthermore, the trajectory sensitivity factor (TSF) at all load buses and aggregated wind generator buses are calculated to provide information about the bus that has the prevalent influence in enhancing the system stability. The bus with the highest TSF is then selected as the location of the load shedding. This process is reiterated until the voltages at all buses are stable. Dynamic simulations are performed with the IEEE 14 bus Reliability Test System as a case study.

Long Term Voltage Stability Enhancement by Model Predictive Control

Power system load shedding has been used as an emergency control method to prevent possible power system instability problems. In this paper, a model predictive control based load shedding scheme is proposed to enhance long term voltage stability of a power system. The main advantage of this method is the capability to handle control actions with proper time instant. A new cost function is defined for the model predictive control scheme. The proposed scheme is tested on the New England 39-bus system to validate its efficiency and effectiveness in preventing system long term voltage stability problems.

A Load Shedding Scheme for Long Term Voltage Stability Enhancement by Multi-Stage Model Predictive Control

Power system load shedding has been used as an emergency control method to prevent possible power system instability problems. In this paper, a new multi-stage model predictive control based load shedding scheme is proposed to enhance long term voltage stability of a power system. The main advantage of this new method is the capability of the scheme to handle multi-stage control actions, which may also be disturbances to a power system at a critical status toward instability. A new cost function is defined for the model predictive control scheme. The proposed scheme is tested on the New England 39-bus system to validate its efficiency and effectiveness in preventing system long term voltage stability problems.

Effect of Advanced Static VAR Compensator on Control of Power System Load Shedding

This paper investigates the effect of Static VAR Compensator (SVC) on power system load shedding. SVC is mainly used in power system stability improvement. This paper proposes a new use of SVC to reduce load shedding. An algorithm of Newton Raphson method (NR) to reduce the load shedding for installing SVC in the system is proposed in this paper. 5 bus test system example is used to demonstrate the effect on load shedding. The test results show that the effect of SVC is significant, in this Static VAR compensator (SVC) is incorporated in Newton Raphson method in which Power Flow Solution is a solution of the network under steady state conditions subjected to certain constraints under which the system operates. The power flow solution gives the nodal voltages and phase angles given a set of power injections at buses and specified voltages at a few, the model of SVC i.e. SVC Susceptance model is discussed. It is also shown that the power system losses are decreased after incorporating the SVC in this N-R method. The results are generated for 5-Bus system. By incorporating the SVC the amount of load shedding is reduced to get the voltages in their limits.

Application of Constriction Factor Particle Swarm Optimization to Optimum Load Shedding in Power System

Load shedding is an important action during contingency situations in electrical power systems. This paper presents a new application of constriction factor particle swarm optimization (CPSO) technique for solving the steady state load shedding (SSLS) problem, due to capacity deficiency conditions caused by unscheduled outages in the bulk generation and transmission system. The problem is formulated to minimize the sum of curtailed load in contingency situations and restore the power system to its normal security and operation conditions. The feasibility of the proposed approach is demonstrated and compared with genetic algorithm (GA) in terms of solution quality and convergence properties over realistic test systems.

On influence of load modelling for undervoltage load shedding studies

This paper explores the influence of load models on decisions of undervoltage load shedding in power systems. A controlled load rejection can be used as an emergency countermeasure to avoid widespread blackout when system voltages are unstable. In this paper, dynamic simulations of a small power system using both static and dynamic load models are presented. When using a static load model, the system includes an explicit model of a transformer with load tap changer. The aim is to demonstrate how different load models influence the analysis and calculation of the amount of load needed to be shed to stabilise the system voltage.

On undervoltage load shedding in power systems

This paper presents an analytical basis and criterion for undervoltage load shedding in power systems. If the voltage is unstable, the proposed undervoltage load shedding criterion can be used to calculate the amount of load which must be shed to stabilise the voltage magnitude. By using a dynamic load model it has been possible to form the criterion which gives the amount of load which has to be shed to stabilise the system in each point in time. The criterion is general and can be applied to any power system.