Power System Contingency Research Articles

Operation of coupled power-gas networks with hydrogen refueling stations, responsive demands and storage systems: A novel stochastic framework

Because of factors such as low natural gas prices, low greenhouse gas emission and high efficiency has increased the penetration of natural gas-fired generators in electric power systems. Large gas consumption of gas-fired generators has caused intense interdependence of electricity and gas systems. Most often, the operational planning of power and gas systems is conducted through a co-optimisation model in which the total cost of electricity and gas systems is minimised and the constraints of both systems are considered. On the other hand, due to environmental concerns, the usage of fuel cell vehicles (FCVs) is increasing. Hydrogen refueling stations (HRSs) are needed for refueling FCVs. This paper puts forward a stochastic model for co-optimisation of wind-integrated power and gas systems, hosting HRSs, electric storage systems (ESS's) and responsive electricity and gas demands. The effect of ESS's, responsive demands, contingencies, wind uncertainties and demand response on operation of power-gas nexus is scrutinized. An incentive-based demand response program has been used for both electricity and gas demands. The case study is the Belgian gas network connected to the a 24-bus power system. The findings indicate that responsive electric demands reduce electric system cost by 9.4 %, while responsive gas demands reduce the gas network cost and total operation cost by 1 %. The contingency analysis on the power-gas nexus shows that single line outage contingencies in power system does not result in any demand shed and does not increase the operation cost of either electricity network or gas network.

Impact of Under Voltage Load Shedding Approach for Voltage Security Control in Power Quality Environment

Contingencies in power system are an ever-present problem for electrical engineers. They may cause the system to have low voltages due to active and reactive power deficiencies. Engineers shold consider the many solutions to approach this problem. The solution has to be appropriate to the contingency faced so as to be able to solve the issue without major damage to a power system. A drastic measure that can be used is by removing low voltage loads or ‘shedding’ the problematic loads. This method of Under Voltage Load Shedding is done by shedding or ‘islanding’ buses that have fallen to a predetermined minimum voltage for a predetermined time to prevent a cascade of blackouts in the widespread power system. The method to automate the process is by using two optimization techniques, Evolutionary Programming (EP) and Particle Swarm Optimization (PSO). This paper proposes a new hybrid algorithm, named Integrated Chaotic Swarm Based-Evolutionary Programming Optimization (ICSBEP) Technique for Voltage Security Control using Under Voltage Load Shedding (UVLS) approach. Voltage security is indicated by the reduction in voltage stability index, FVSI. In this study, EP, PSO and ICSBEP optimizations algorithms were developed and tested on a IEEE 30-bus reliability test system (RTS). Comparative studies were conducted to observe the advantages of the hybrid algorithm over traditional EP and PSO algorithms. The results obtained from the study can help manage the power quality of the system in ensuring that the customer receive secure electricity supply.

The Frequency Regulation Strategy for Grid‐Forming Wind Turbine Generator and Energy Storage System Hybrid System in Grid‐Connected and Stand‐Alone Modes

This paper proposes a coordinated frequency regulation strategy for grid‐forming (GFM) type‐4 wind turbine (WT) and energy storage system (ESS) controlled by DC voltage synchronous control (DVSC), where the ESS consists of a battery array, enabling the power balance of WT and ESS hybrid system in both grid‐connected (GC) and stand‐alone (SA) modes. The newly developed GFM framework, i.e., DVSC, is adopted in both WT and ESS, which utilizes DC voltage dynamics for synchronization purposes. In this paper, the GC mode and SA mode are transferred by changing the status of the series‐connected switch, and it is necessary to meet the grid connection conditions when the system is transferred to the GC mode, namely, voltage, frequency, and phase sequence. For the GC mode, the inertia response from WT can be realized using the reserved energy of the DC capacitor, while the ESS serves to eliminate the steady‐state frequency deviation and reduce the DC voltage fluctuation of WT using the designed secondary frequency regulation scheme. For the SA mode, the proposed strategy can keep the power balance without external power sources. The small‐signal model of the WT and ESS hybrid system is derived. The stability analysis in both GC and SA modes is fully conducted utilizing the modal analysis method, where the impacts of control parameters on stability are assessed. The performance of the proposed strategy in a weak system is evaluated. Simulation studies are carried out under various power system contingencies to verify the effectiveness of the proposed strategy and validate the correctness of the theoretical analysis.

Steady-State Security and Power System Contingency Analysis: A Review

Steady-State Security and Power System Contingency Analysis: A Review

Virtual‐physical power flow method for cyber‐physical power system contingency and vulnerability assessment

AbstractTraditional power systems have evolved into cyber‐physical power systems (CPPS) with the integration of information and communication technologies. CPPS can be considered as a typical hierarchical control system that can be divided into two parts: the power grid and the communication network. CPPS will face new vulnerabilities which can have network contingencies and cascading consequences. To address this challenge, a virtual‐physical power flow (VPPF) method is proposed for the vulnerability assessment of CPPS. The proposed method contains dual power flows, one is to simulate a virtual power flow from decision‐making units, and the other is to simulate a physical power flow. In addition, a novel hierarchical control model is proposed that includes four layers of CPPS: the physical layer, the secondary device layer, the regional control layer, and the national control layer. The model is based on IEEE test cases using data and structures provided by MATPOWER. Denial‐of‐service (DoS) and false data injection (FDI) are simulated as two major cyber‐attacks in CPPS. A novel vulnerability index is proposed that consists of system voltage, network latency, and node betweenness as three key indicators. This is a comprehensive and adaptive index because it encompasses both cyber and physical system characteristics and can be applied to several types of cyber‐attacks. The results of the vulnerability assessment are compared in national and regional control structures of CPPS to evaluate the vulnerability of cyber‐physical nodes.

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.

Steady state Contingency Analysis of IEEE-39 Bus system Using PSS/E

Nowadays the stable, secure and reliable power system operation is the main objective of anyelectric power utility. The Power system is vulnerable to any contingency i.e. loss of Transmission line,Transformer, Generating unit etc. The power system control engineer should have detail analysis of at leastthe most probable and sever contingencies of the system and accordingly devise the remedial action plan.Thus, Power system contingency analysis and evaluation play vital role in ensuring power system stabilityand reliability. Keeping in view the importance of contingency analysis in power system operation andcontrol, this research focuses on steady state Contingency analysis of IEEE-39 bus system using newtonRaphson method. PSS/E is used for the analysis, different contingencies are evaluated and ranked basedupon the Power flow (Overloading), Voltage and phase angle deviation. Once the ranking is performed, thePV and QV analysis is performed for most severe contingencies. Remedial Actions is proposed for themost severe contingencies and this research could be applied to any real time network.

Deep-Learning-Based Denial-of-Service Resilient Framework for Wide Area Situational Awareness of Power Systems

Vulnerability of the wide area measurement system (WAMS) to Denial of Service (DoS) attacks can hinder the real-time situational awareness of the grid. In this regard, a DoS cyber-attack resilient WAMS framework is presented, which utilizes deep-learning based overcomplete denoising autoencoder (ODAE) architecture. The proposed framework is able to reconstruct PMU data during DoS attacks on PMU-PDC connections for the entire period of power system contingencies, which helps the operator to be aware of the occurrence of any such disturbance and assist them in decision making related to the stability of the grid. The performance of the proposed framework has been validated on WSCC 9 bus system, which is simulated on the developed cyber-physical WAMS testbed involving real time digital simulator (RTDS), hardware PMUs, Hardware PDC, network switches etc. Subsequently, the response of the suggested framework has also been evaluated on the real field PDC data gathered from the Northern Region of the Indian Power Grid (NRIPG). The experimental results indicate that the reconstructed PMU measurements during DoS attacks help in maintaining the dynamic visualization of system-wide information thereby ensuring the resiliency of wide area monitoring applications against DoS attacks.

Climate-combined energy modelling approach for power system planning towards optimized integration of renewables under potential climate change - The Small Island Developing State perspective

While high renewable electricity targets are feasible under current climatic conditions, planning the power sector from a long-term perspective requires great precaution, given the strong dependency of renewable energy potential on climate and potential future changes. Power balance optimization modelling is a powerful tool for adequate power system (contingency) planning, and informed decision making in future perspective. This is demonstrated based on a case study of Suriname, considering scenario simulations and impact assessments for the 2030–2040 time horizon, for multiple load and climate projections. The recently developed REVUB modelling approach is utilized, which facilitates hourly-to-multiannual simulations, the latter being important for inter-annual variability assessments, and key for climate impact studies. Our results show that Suriname has an optimized renewable electricity share potential ranging from 50% to 90% under the considered future scenarios based on complementary exploitation of hydro, wind and solar power resources.

Adaptive Droop Control for Voltage and Frequency Regulation in Virtual Power Plant under Power System Contingencies

The frequent penetration of distributed energy resources in microgrid environment often faces operational challenges like stability and reliability issues. In order to overcome these challenges, the concept of the virtual power plant is suggested. Practically, the chance of voltage and frequency fluctuation increases in virtual power plant scenarios under critical power system contingencies like faults and sensitive load switching. Therefore, implementation of efficient controllers is required to address this issue. Further, the fixed gain droop control topology reported in literature is insufficient to accomplish seamless voltage and frequency regulation of the virtual power plants. Therefore, this paper proposes an adaptive droop control scheme to regulate system’s frequency and voltage (as per IEEE 1547) in presence of critical power system contingencies under virtual power plant scenarios. Further, a small signal stability analysis is carried out considering the system Eigenvalues to investigate the effectiveness of the proposed adaptive droop controller. Additionally, a comparative analysis between traditional and proposed droop controller is performed considering critical power system contingencies under MATLAB / Simulink environment. The dynamic responses establish the improved performance of the proposed controller over the traditional control scheme in terms of transient and steady-state stability.

Transmission network tariff volatility assessment under UPFC‐integrated system and N‐1 contingency condition

AbstractIn the restructured electricity markets, transmission usage, usage costs, and loss allocation are critical issues for recovering network embedded and maintenance costs from network users. However, because of the integration of the flexible alternating current transmission system (FACTS) and the occurrence of power system contingencies, this allocation is more complex and critical from the utility's perspective, as it may cause volatility in transmission network costs. In this work, the unified power flow controller (UPFC) is utilized to show the effect of its integration on the system cost and cost allocation. Further, contingency conditions are common in power systems. Therefore, for determining usage, usage cost, and loss allocation volatility under UPFC‐integrated system and N‐1 contingency conditions, this work utilized a power flow tracing‐based transmission usage allocation technique. Reliability factors are used to determine transmission line flows in the event of a failure. Furthermore, contingency line flows are employed to assess transmission tariff volatility in terms of risk premiums. The cost of transmission utilization is recovered using a modified MW‐mile technique. The devised approach was put to the test on a 6‐bus system and an IEEE 14‐bus system.

Power system contingency classification using machine learning technique

One of the most effective ways for estimating the impact and severity of line failures on the static security of the power system is contingency analysis. The contingency categorization approach uses the overall performance index to measure the system's severity (OPI). The newton raphson (NR) load flow technique is used to extract network variables in a contingency situation for each transmission line failure. Static security is categorised into five categories in this paper: secure (S), critically secure (CS), insecure (IS), highly insecure (HIS), and most insecure (MIS). The K closest neighbor machine learning strategy is presented to categorize these patterns. The proposed machine learning classifiers are trained on the IEEE 30 bus system before being evaluated on the IEEE 14, IEEE 57, and IEEE 118 bus systems. The suggested k-nearest neighbor (KNN) classifier increases the accuracy of power system security assessments categorization. A fuzzy logic approach was also investigated and implemented for the IEEE 14 bus test system to forecast the aforementioned five classifications.

Hierarchical Clustering-Based Framework for Interconnected Power System Contingency Analysis

This paper investigates a conceptual, theoretical framework for power system contingency analysis based on agglomerative hierarchical clustering. The security and integrity of modern power system networks have received considerable critical attention, and contingency analysis plays a vital role in assessing the adverse effects of losing a single element or more on the integrity of the power system network. However, the number of possible scenarios that should be investigated would be enormous, even for a small network. On the other hand, artificial intelligence (AI) techniques are well known for their remarkable ability to deal with massive data. Rapid developments in AI have led to a renewed interest in its applications in many power system studies over the last decades. Hence, this paper addresses the application of the hierarchical clustering algorithm supported by principal component analysis (PCA) for power system contingency screening and ranking. The study investigates the hierarchy clustering under different clustering numbers and similarity measures. The performance of the developed framework has been evaluated using the IEEE 24-bus test system. The simulation results show the effectiveness of the proposed framework for contingency analysis.

Application Contingency Analysis of An IEEE 30 Bus System Using Matpower

Contingency analysis is significant for power system protection. Internal component failures or external causes such as lightning and equipment overloading can cause power system contingencies. Multiple outage scenarios are crucial in today’s deregulated environment for how the transmission network is utilized. The transmission network is highly pressured due to rising load demand and the need to operate economically. Contingency analysis of an IEEE 30 bus system is provided in this article utilizing Matpower software, which includes the source code. Matlab and Matpower are suggested as platforms for developing a screening process that examines the severity of each non-islanding branch failure, generator outage, MVA, and voltage violation. The generator terminal voltages will be permitted to fluctuate between 0.95 per unit and 1.05 per unit, as specified in the case30 input data files, to provide a broader practical zone. The performance index (PI) value is generated by adding the apparent performance index and the voltage performance index to rank the severity of the line outages. For the extreme contingency situation, a load flow study was performed. The four most severe scenarios, generator 22, generator 13, generator outage 23, and branch outage 28, should be constantly monitored to keep the electrical system safe.

Blocking cascading failures with optimal corrective transmission switching considering available correction time

Corrective transmission switching has been a promising technic for handling contingencies in power systems. The corrective solutions obtained from optimal formulations or heuristic methods are usually considered executable. However, when considering the contingency-induced cascading failures, where the temperature relay of lines may act quickly, the systems may have not enough time to take corrective actions. Thus some corrective solutions may be infeasible in practical applications. To further study the feasibility of corrective solutions, in this paper, considering the available correction time, which is derived from a continuous temperature evolution model, two methods are proposed to validate the corrective solutions obtained from a modified optimal corrective transmission switching formulation. Besides, based on the available correction time, a comprehensive method is proposed to block cascading failures and a critical index is proposed to evaluate critical cascading failures. Numeric studies on test systems show that there are considerable infeasible solutions in the solutions obtained from the optimal corrective formulation, which indicates that it is critical to consider the available correction time when utilizing the corrective solutions in real cases. Results also show the proposed comprehensive method and index are effective to handle cascading failures.

Implementation of adaptive neuro fuzzy inference system and back propagation neural network for the appraisal of power system contingency analysis

Power System Security and Contingency analysis is one of the most important tasks in power systems. In operation, contingency analysis assists engineers to operate the power system at a secure and safe operating point where equipment are loaded within their safe operating area (SOA). Power is dispatched to customers with acceptable quality standards. The results of off-line load flow calculations are used to estimate performance indices (PI flow, PI V). MATLAB toolbox was the proposed methodology used for the implementation. The proposed approach for contingency analysis was found to be appropriate for screening and ranking fast voltage and line flow contingencies.

Data-Driven Classifier for Extreme Outage Prediction Based On Bayes Decision Theory

The growing concern over catastrophic weather events, mostly as a direct result of climate changes, has underscored the need for expanding traditional power system contingency analyses to handle the associated risks of extreme power outages. To enable power system operators to make timely decisions when facing extreme events, we explore in this paper the viability of a classifier which uses the machine learning approach based on the Bayes decision theory as a means of predicting power system component outages. However, owing to an excessively imbalanced and largely sparse power component outage datasets, the corresponding classifier learning is a challenging problem in the data mining community. In the proposed approach, we apply a resampling method to overcome the class imbalance problem. The proposed classifier provides an effective framework that not only minimizes outage prediction errors for power system components, but also considers the cost of each preventive action according to its implication in extreme events. The outcome of the proposed model can be used for introducing operation-oriented preventive measures that allow the rescheduling of generation resources for maximizing the power system resilience. The performance of the proposed classifier is examined through numerical simulations by utilizing the confusion matrix.

Novel cascading scheme of VSC‐HVDC with DC voltage synchronisation control for system frequency support

This paper introduces a novel cascading frequency regulation scheme (CFRS) for VSC-HVDC to provide strong support for the connected weak AC grid without phase-locked loop and remote-communication considering the cost-effective property. The proposed CFRS utilises a DC voltage synchronisation control to enable the self-synchronisation of VSC-HVDC without phase-locked loop, while the corresponding inertia response can be exerted via utilising the stored HVDC capacitor energy. Meanwhile, the DC voltage acts as the reflection of system frequency, enabling the coordination between two-end VSC stations without communication. More importantly, to fully utilise the VSC-HVDC potentials, the CFRS that sequentially activates HVDC capacitor and primary frequency control is implemented so that the energy loss and the detrimental control impacts on the sending-end systems can be minimised while making sure the requirement of system frequency support. Analytical derivations have been done to evaluate the contribution of the proposed CFRS to system frequency regulation. Furthermore, the effectiveness of the proposed scheme has been well validated in PSCAD/EMTDC under several power system contingencies by fully comparing with existing control schemes. The proposed CFRS stands out by the reduced complexity of the control structure, the robustness for connecting the very weak AC grid and the fast frequency regulation ability with consideration of cost-effective property.

Development and Application of Probabilistic Performance Index for Ranking N-1 Contingencies

Contingency analysis and ranking are important tasks in modern electrical power systems which aim at keeping the power system secure, reliable, and stable. N-1 contingency is the loss of any one component of power system and is obviously the most frequent contingency in power system. Contingency ranking has most often been done using deterministic indices which can be either active power performance index (PIP), voltage performance index (PIV) or the overall performance index (PI). Power system contingencies are ranked based on the calculated Performance index for each contingency. Ranking is from the contingency with the highest performance index first and proceeds in a descending manner which corresponds to the most severe to the least severe contingency. Due to the fact that Contingencies are unpredictable events, researchers of recent have suggested the inclusion of the probability of the occurrence of a contingency in its ranking index. This makes the index probabilistic. In this work, the development and application of probabilistic performance index for ranking N-1 contingencies is considered. It is illustrated with a case study.

Development and Application of Probabilistic Performance Index for Ranking N-1 Contingencies

Contingency analysis and ranking are important tasks in modern electrical power systems which aim at keeping the power system secure, reliable, and stable. N-1 contingency is the loss of any one component of power system and is obviously the most frequent contingency in power system. Contingency ranking has most often been done using deterministic indices which can be either active power performance index (PIP), voltage performance index (PIV) or the overall performance index (PI). Power system contingencies are ranked based on the calculated Performance index for each contingency. Ranking is from the contingency with the highest performance index first and proceeds in a descending manner which corresponds to the most severe to the least severe contingency. Due to the fact that Contingencies are unpredictable events, researchers of recent have suggested the inclusion of the probability of the occurrence of a contingency in its ranking index. This makes the index probabilistic. In this work, the development and application of probabilistic performance index for ranking N-1 contingencies is considered. It is illustrated with a case study.