Composite Power System Reliability Research Articles

An Open-Source Tool for Composite Power System Reliability Assessment in Julia™

An Open-Source Tool for Composite Power System Reliability Assessment in Julia™

N − 1 Security Criteria Based Integrated Deterministic and Probabilistic Framework for Composite Power System Reliability

Unpredictable variations in load demand and unanticipated component failures are progressively impacting the operation of modern power systems, making system evaluation more stochastic in nature. Although deterministic approaches were formerly the norm for determining system status, probabilistic approaches have greatly improved the capacity to capture the stochastic behavior characteristic of power system operations. The presented work in the paper recommends the use of probabilistic modelling approaches with deterministic approaches, highlighting their crucial function in augmenting the reliability and security of contemporary power systems to unanticipated failures. In this paper, N − 1 security criteria based reliability of the composite power system (CPS) is proposed using an integrated deterministic and probabilistic framework (D-P) considering outage of the transmission line. For the deterministic approach (DA), line overloading on available lines is determined using the static security index (SSI). For the probabilistic approach (PA), reliability indices such as expected loss of power (ELOP), expected frequency of contingency (EFOC), expected loss of load (ELOL), probability of load curtailment (PLC), and expected duration of load curtailments (EDLC) are calculated. Further, for each contingency, a performance index is determined using both approaches to assess the severity of the contingency that occurred on the power system. Based on the N − 1 security criteria based reliability analysis using an integrated D-P framework, a credible critical set of transmission lines is obtained, which can serve as important information to system operators. The proposed techniques have been tested on IEEE 24 bus reliability test system (RTS).

Reliability Improvement of Composite Power System using UPFC

Together with quality power supply criteria, Composite Power System (CPS) reliability is also one of the important aspects of deciding the security of the CPS for a given load demand. In the current paper, the reliability of CPS is evaluated. The Newton Raphson (NR) approach is implemented in Power System Simulation for Engineering (PSSE) software to take into account, the load flows, then the power available to the load points, to evaluate the reliability of CPS. Newton Raphson NR Method was implemented by considering different contingencies of the CPS. Power available to the load points obtained by the NR method is critical in identifying the successful operating state of the CPS. Expected Load Curtailment (ELC), Number of Load Curtailment (NLC), Expected Energy Not Supplied (EENS), and Severity Index (SI) are the crucial dependability indices that are assessed. The main objective of the Reliability evaluation of the CPS is to investigate the scope for the improvement of reliability and explore the possible schemes to improve CPS reliability. The Unified Power Flow Controller (UPFC) one of the Flexible AC Transmission Systems is incorporated into the CPS to analyze the improvement in the reliability of the system. Further, the effect of UPFC on the power-carrying capacity of the lines under contingency conditions, resulting in the failure mode of CPS operation is examined. Improvement in the reliability indices of the system is observed due to UPFC incorporation.

Reliability Evaluation of Power System based on Demand Response Program in the Presence of the Electric Vehicles

The use of Demand-Side Management (DSM) to increase the reliability of composite power systems at hierarchical level II (HLII) with Electric Vehicles (EVs) is an important issue that has not been studied so far. Studies that have been conducted assumed that EVs are connected to the power system during the mid-peak load and peak load in two charge levels with uncertainty in influence and three load shifting levels (85%, 90%, and 95%). The reliability indices Loss of Load Expectation (LOLP), Expected Energy Not Supplied (EENS), Expected Health Duration (EHDUR), and Expected Margin Duration (EMDUR) are calculated. The present paper uses Monte Carlo Simulation (MCS) in modeling the uncertainty in the generation and transmission capacity of the power system and the influence of EVs. The modeling was performed on IEEE-RBTS standard system using the MATLAB software. The result indicates that more penetration of EVs will lead to higher load levels, and thereby LOLP and EENS indices will change much more, a trend that increases even more when EVs are charged during peak load. It is possible to increase EHDUR and EMDUR values by increasing load-shifting levels (95% to 90% and 85%).

Prioritization of Transmission Network Components Based on their Failure Impact on Reliability of Composite Power Systems

This paper proposes an efficient method to identify the importance of transmission network components from the network’s reliability perspective. The proposed method is able to reveal the weak points of the network and can be employed as useful tool by power system planners to identify where investments should be made to increase the overall system reliability. The proposed approach has two main stages, including evaluation of the network contingency states and a sensitivity analysis which shows the link between reliability of each component and overall system reliability. Unlike the similar methods in this area and with the help of two reasonable simplifications, the proposed method can be employed to real transmission networks with acceptable computational burden. The proposed method is implemented on two test systems including the IEEE Reliability Test System (IEEE RTS) and the Roy Billinton Test System (RBTS). The obtained results demonstrate the efficiency of the proposed approach.

A convolutional neural network-based approach to composite power system reliability evaluation

This paper proposes a machine learning-based approach in conjunction with Monte Carlo simulation (MCS) to improve the computation efficiency of composite power system reliability evaluation. Traditional composite system reliability evaluation approaches are computationally demanding and may become inapplicable to large integrated power grids due to the requirements of repetitively solving optimal power flow (OPF) for a large number of system states. Machine learning-based approaches have been used to avoid solving OPF in composite system reliability evaluation except in the training stage. However, current approaches have been derived to classify system states into success and failure states (i.e., up or down). In other words, they can be used to evaluate power system probability and frequency reliability indices, but they cannot be used to evaluate power and energy reliability indices unless OPF is solved for each failure state to determine minimum load curtailments. In this paper, a convolutional neural network (CNN)-based regression approach is proposed to determine the minimum amount of load curtailments of sampled states without solving OPF, except in the training stage. Minimum load curtailments are then used to evaluate power and energy indices (e.g., expected demand not supplied) as well as to evaluate the probability and frequency indices. The proposed approach is applied on several systems including the IEEE Reliability Test Systems (The IEEE RTS and IEEE RTS-96) and Saskatchewan Power Corporation in Canada. Results show that the proposed approach is computationally efficient (fast and accurate) in calculating the most common composite system reliability indices. The developed source code of the proposed method is available to the community for future research and development.

Probabilistic Operational Reliability of Composite Power Systems Considering Multiple Meteorological Factors

External weather conditions have significant effects on the operation of composite power systems. Stochastic renewable generations, random loads, dynamic thermal ratings, and failure probabilities of outdoor power devices are all influenced by diverse meteorological factors. To integrate the probabilistic impacts of multiple meteorological factors and their resulting uncertainties, a new nested sampling framework for probabilistic operational reliability evaluation of composite power systems is proposed. As one critical step of the framework, a general modeling method to fit the probability distribution of each meteorological factor at each time point by historical weather records is presented in details, and several specific distributions are introduced for the first time to refine the accuracy of probabilistic weather representation. In subsequent tests, the actual failure records and corresponding meteorological data are used to validate the performance of the proposed framework. Several existing methods, considering but not fully considering meteorological effects, are adopted for comparisons to verify the effectiveness of the proposed method in the test case. As one benefit of the proposed framework, uncertainty importance measures of all involved random variables are available and discussed as well.

Enhanced Cross Entropy Method for Composite Power System Reliability Evaluation

Cross entropy method (CEM) can effectively accelerate the reliability evaluation of power system. It is mainly utilized to conduct importance sampling (IS) on discrete component state variables and independent or fully correlated continuous variables. To carry out IS on continuous variables with complicated correlation, this paper proposes an enhanced CEM (ECEM) that addresses the following two problems: the first one is the probabilistic modeling of the joint probability density function (PDF) for the correlated continuous variables, and the second one is the IS on the modeled joint PDF. For the first one, a Gaussian mixture model (GMM) is utilized to estimate the original joint PDF of correlated continuous variables. For the second one, the parameters of the GMM based joint PDF are optimized directly by a direct-updating-rule based method (DURM) to obtain the optimal IS-PDF. Furthermore, as an alternative to DURM, a Nataf transformation based method (NTM) is also presented to distort the parameters of the GMM-based joint PDF indirectly. Compared with NTM, which is suitable for normal copula correlation, DURM has a much wider application scope without any restriction on the correlation type. Finally, an improved unbiased estimator for the reliability index is presented to further accelerate ECEM. Test studies on RTS79 and MRTS with wind farm verify the effectiveness of ECEM.

A Hybrid Monte Carlo Simulation and Multi Label Classification Method for Composite System Reliability Evaluation

This paper presents a new approach for reliability evaluation of composite power systems by combining Monte Carlo simulation and multi label k-nearest neighbor (MLKNN) algorithm. MLKNN is a classification technique in which target vector of each instance is assigned into multiple classes. In this paper, MLKNN is used to classify states (failure or success at system or bus level) of a complete power system without requiring optimal power flow (OPF) analysis, except in the training phase. As a result, the computational burden to perform OPF is reduced dramatically. For illustration, the proposed method is applied to the IEEE 30 BUS Test System and IEEE Reliability Test System. The obtained results from various case studies demonstrate that MLKNN based reliability evaluation provides promising results in both classification accuracy and computation time in evaluating the composite power system reliability.

MPLP-Based Fast Power System Reliability Evaluation Using Transmission Line Status Dictionary

Composite power system reliability evaluation is computationally time-consuming because the optimal power flow (OPF) with the least load curtailment is calculated for a large number of samples. Most current studies of probabilistic simulation methods focus on sampling techniques to improve the sampling efficiency and decrease the calculations of the OPF. This paper proposes a fast reliability evaluation method that accelerates the calculation of the OPF using multi-parametric linear programming (MPLP). In this paper, the sampled transmission line statuses are considered by the transmission line status dictionary (TLSD). We match the line status of each sample with the scenarios in the TLSD. For matching samples, the generation status and the sampled load are treated as MPLP parameters of the DC-OPF evaluation model. A dynamic learning algorithm is applied to solve the MPLP problem. Case studies are conducted on both IEEE Reliability Test Systems and a provincial power system in China. The results show that the proposed method improves the evaluation efficiency by 23–30 times compared with the normal DC-OPF-based model.

A novel importance sampling method of power system reliability assessment considering multi-state units and correlation between wind speed and load

With the rapid integration of wind energy, the increasing uncertainty and high reliable property of power systems have resulted in great difficulties in reliability assessment. To solve the problem, traditional cross entropy based importance sampling (CE-IS) methods are improved in this paper. The improved method is capable of efficiently assessing the reliability of composite power systems with wind energy integrated. First, we introduce the differences between the improved CE-IS (ICE-IS) and traditional CE-IS. Particularly, ICE-IS takes the correlation of random variables (RVs) into account and models multi-state RVs with multinomial distribution. Therefore, ICE-IS can obtain much better suboptimal distributions for the RVs than CE-IS, which accelerates the reliability assessment. Then the procedures of ICE-IS are detailed by two parts, which are a pre-simulation stage and a main simulation stage, respectively. Finally, we modify IEEE-RTS 79 and IEEE-RTS 96 test systems based on wind speed data observed in northwest China. Several case studies are designed and carried out on the modified systems to validate the proposed method.

Contingency selection and ranking for composite power system reliability evaluation

In this paper, a probabilistic performance index (PI) has been developed to perform contingency selection and ranking. The results show that considering up to the second level contingency is satisfactory for both accuracy and computation time requirements. The results obtained constitute an essential basis for the analysis of power systems in terms of adequacy assessment and reliability evaluation. To substantiate the study results, comparison with other similar studies was performed.

Probabilistic Performance Index based Contingency Screening for Composite Power System Reliability Evaluation

Composite power system reliability involves assessing the adequacy of generation and transmission system to meet the demand at major system load points. Contingency selection was being the most tedious step in reliability evaluation of large electric systems. Contingency in power system might be a possible event in future which was not predicted with certainty in earlier research. Therefore, uncertainty may be inevitable in power system operation. Deterministic indices may not guarantee the randomness in reliability assessment. In order to account for volatility in contingencies, a new performance index proposed in the current research. Proposed method assimilates the uncertainty in computational procedure. Reliability test systems like Roy Billinton Test System-6 bus system and IEEE-24 bus reliability test systems were used to test the effectiveness of a proposed method.

A Systematic Review of Reliability Studies on Composite Power Systems: A Coherent Taxonomy Motivations, Open Challenges, Recommendations, and New Research Directions

Power systems has been subjected to significant upgrades in terms of structure and capacity. Reliability evaluation of composite power systems has surfaced as an essential step in operation and planning stages of the modern power system. It is an effective tool to investigate the ability of power systems to supply customers with reliable power service. The purpose of this review is to enhance the knowledge of reliability studies conducted on composite power systems by providing a critical and systematic review. This work investigates peer-reviewed articles published between 2007 and 2017 in three reliable databases. The findings reveal that the reliability of composite power systems has received considerable attention over the last few years. Secondly, investigation studies demonstrated a crucial role in verifying the impact of adopting new technologies. Third, studies on this topic have been intensively conducted in Asia, which highlights the promising sectors in these regions. However, researchers have generally focused on developing several aspects (e.g., evaluation speed and wind power integration) at the expense of others (e.g., realistic studies and other renewable energy resources). The lack of practical applications is evident in the surveyed publications. These findings imply a potential incoordination between the needs of the real applications and researchers’ tendencies. Future reliability evaluation scholars are advised to consider the findings of this systematic review including concentrating on insufficiently covered topics and enhance the coordination among the efforts devoted in this area.

A novel petri nets algorithm using conditional probability for the evaluation of composite power system reliability

Reliability of an electrical power system plays a vital role in providing continuous power supply to consumers with greater quality. The power demand is increasing day by day due to the increased population, modern society and are seeking highly reliable power. The assessment of reliability is very difficult due to the presence of large number of components and complex power system network con-figurations. This paper address a novel useful step by step algorithm for the assessment of average power availability at load buses us-ing the concept of modified Petri nets with conditional probability. The proposed algorithm is very efficient and can applicable to any number of the bus system. The proposed algorithm is tested with Roy Billiton practical example, IEEE 6 bus, IEEE 14 bus and IEEE RTS-96 bus system. The obtained results are validated by Monte Carlo simulation method, Classical Node elimination method and mod-ified minimal cut set method.

Composite power system reliability evaluation using modified minimal cut set approach

The composite power system reliability analysis is generally based on minimal path or cut enumeration, tracing of power flow paths from which the related reliability indices are calculated. The minimal cut set is a popular method in the reliability analysis for simple and complex configurations. Average availability of power supply at the consumer end is one of the reliability assessment parameter. This paper is concerned about the evaluation of this reliability index. A step by step procedure for a modified minimal cut set method is explained in this paper using IEEE 6 bus, 14 bus and Single area IEEE RTS 96 system. The proposed algorithm is easy to program and can be applicable to any system. The proposed algorithm is validated with the Classical Node Elimination method, Step by Step algorithm using Conditional Probability and Monte Carlo Simulation method. The proposed technique is tested with a practical example taken from Roy Billinton paper (Reliability evaluation in distribution and transmission systems).

A case study in multi-core parallelism for the reliability evaluation of composite power systems

The probabilistic evaluation of composite power system reliability is an important but computationally intense task that requires the sampling/searching of a large search space. While multiple methods have been used for performing these computations, a remaining area of research is the impact that modern platforms for parallel computation may have on this computation. Studies have been performed in the past, but they have been primarily limited to cluster-based computing. In addition, the most recent works in this area have used outdated technology or been evaluated using smaller test systems. In the modern era, a wide variety of platforms are available for achieving parallelism in computation including options like multi-core processors, clusters, and accelerators. Each of these platforms provides unique opportunities for accelerating computation and exploiting scalability. In order to fill this gap in the research, this study implements and evaluates two methods of parallel computation—batch parallelism and pipeline parallelism—using a multi-core architecture in a cloud computing environment on Amazon Web Services using up to 36 virtual compute cores. Further, the methodologies are contrasted and compared in terms of computation time, speedup, efficiency, and scalability. Results are collected using IEEE reliability test systems, and speedups upwards of 5x are demonstrated across multiple test systems.

Non-Sequential Monte Carlo Simulation for Cyber-Induced Dependent Failures in Composite Power System Reliability Evaluation

Cyber-induced dependent failures are important to be considered in composite system reliability evaluation. Because of the complexity and dimensionality, Monte Carlo simulation is a preferred method for composite system reliability evaluation. The non-sequential Monte Carlo or sampling generally requires less computational and storage resources than sequential techniques and is generally preferred for large systems where components are independent or only a limited dependency exists. However, cyber-induced events involve dependent failures, making it difficult to use sampling methods. The difficulties of using sampling with dependent failures are discussed and a solution is proposed. The basic idea is to generate a representative state space from which states can be sampled. The probabilities of representative state space provide an approximation of the joint distribution and are generated by a sequential simulation in this paper but it may be possible to find alternative means of achieving this objective. The proposed method preserves the dependent features of cyber-induced events and also improves the efficiency. Although motivated by cyber-induced failures, the technique can be used for other types of dependent failures as well. A comparative study between a purely sequential methodology and the proposed method is presented on an extended Roy Billinton Test System.

A Continuous Time Markov Chain Based Sequential Analytical Approach for Composite Power System Reliability Assessment

This paper proposes a continuous time Markov chain (CTMC) based sequential analytical approach for composite generation and transmission systems reliability assessment. The basic idea is to construct a CTMC model for the composite system. Based on this model, sequential analyses are performed. Various kinds of reliability indices can be obtained, including expectation, variance, frequency, duration and probability distribution. In order to reduce the dimension of the state space, traditional CTMC modeling approach is modified by merging all high order contingencies into a single state, which can be calculated by Monte Carlo simulation (MCS). Then a state mergence technique is developed to integrate all normal states to further reduce the dimension of the CTMC model. Moreover, a time discretization method is presented for the CTMC model calculation. Case studies are performed on the RBTS and a modified IEEE 300-bus test system. The results indicate that sequential reliability assessment can be performed by the proposed approach. Comparing with the traditional sequential Monte Carlo simulation method, the proposed method is more efficient, especially in small scale or very reliable power systems.

Application of Bayesian networks in composite power system reliability assessment and reliability‐based analysis

Bayesian network (BN) is a strong framework for handling probabilistic events which have received limited attention in power system reliability assessment. By applying BN for bulk power system reliability assessment, additional capabilities are provided at modelling and analysis levels in comparison with conventional methods. This study proposes a methodology to apply BNs to composite power system (CPS) reliability modelling, reliability assessment and reliability-based analyses. A minimal cutset (MC)-based method is proposed to extract the BN structure. Moreover, BN parameters are defined based on logical relationships between components, MCs and system failure. In addition, some issues of BN application to large systems are investigated. A variety of reliability-based analyses, useful in different power system studies are introduced and discussed in details which are provided by applying BNs to CPS reliability. The computational efficiency of the presented method is demonstrated by comparing it with state enumeration and Monte Carlo simulation methods. The proposed methodology is implemented on Roy Billinton Test System to extract its BN model based on which analysis issues are considered. Finally, the proposed methodology is applied to the simple, but representative, system the IEEE-reliability test system to show its feasibility in larger systems.