Power System Reliability Evaluation Research Articles

A Model and Data Hybrid-Driven Method for Operational Reliability Evaluation of Power Systems Considering Endogenous Uncertainty

A Model and Data Hybrid-Driven Method for Operational Reliability Evaluation of Power Systems Considering Endogenous Uncertainty

Reliability Evaluation of Composite Power Systems Integrated with Wind and Solar Energy Sources: A Comprehensive Review

Reliability Evaluation of Composite Power Systems Integrated with Wind and Solar Energy Sources: A Comprehensive Review

A Region-Based Approach for the Operational Reliability Evaluation of Power Systems With Renewable Energy Integration

Power system operational reliability evaluation is computationally time-consuming due to the need to analyze numerous system states resulting from uncertain renewable energies and loads as well as random outages of components. This paper presents a load feasible region (LFR) approach to accelerate the process of operational reliability evaluation. The LFR of each state, which is defined as a polyhedron in the load space, can be built in advance offline. The inner points of the LFR correspond to the loads that can be satisfied under the system operational constraints. Then, the preestablished LFR can be corrected with little computing effort considering the renewable energy fluctuation during the operation phase. The optimal load shedding under each state can be obtained directly by calculating the minimum Manhattan distance to the LFR's boundary. Multiple application examples are presented to demonstrate the efficiency and scalability of the proposed region-based approach.

An efficient cross-entropy method addressing high-dimensional dependencies for composite systems reliability evaluation

Cross-entropy (CE) based importance sampling (IS) accelerates the reliability evaluation of power system greatly, but is mainly focused on the CEIS of independent random variables (RVs) or low-dimensional correlated RVs (CRVs). To extend the CE method to high-dimensional CRVs while avoiding the “curse of dimensionality” in optimizing a high-dimensional IS probability density function (IS-PDF), an efficient CE method termed as CE-DRDM, is developed from a dimension-reduced dependence model (DRDM). First, based on the DRDM’s original hierarchical disaggregate structure (HDS), the CE-DRDM which conducts the CEIS for a single aggregate RV rather than the high-dimensional CRVs is proposed. Second, to solve the issue that the performance of CE-DRDM may degrade in the case of high penetration of renewable energy, the CE-DRDM is enhanced by improving the DRDM’s HDS, and then a novel CE optimization method is proposed, through which the analytical updating formulas of IS-PDF parameters can be derived. Thirdly, the CE-DRDM is further enhanced by using a sparse density estimator, based on which the number of IS-PDF parameters needed to be optimized in the CE optimization reduces greatly, and consequently the optimization accuracy improves accordingly. Finally, the validity of CE-DRDM is verified by several numerical cases with high-dimensional dependencies.

Modeling Multi-Temporal Correlation of PV Stations Output via Improved Nonparametric Disaggregation Method in Power System Reliability Evaluation

Modeling Multi-Temporal Correlation of PV Stations Output via Improved Nonparametric Disaggregation Method in Power System Reliability Evaluation

An Efficient Method for the Reliability Evaluation of Power Systems Considering the Variable Photovoltaic Power Output

The operational reliability of power systems is threatened by the random failure of components and uncertain power output of renewable energies, such as photovoltaics. Under such circumstances, reliability evaluation is necessary for maintaining a continuous and stable energy supply. However, traditional reliability evaluation methods are usually extremely time-consuming, considering the numerous system states that need to be analysed. Hence, the reliability evaluation process cannot follow up the dynamic changes in PV output, which makes the timeline of the evaluation disappointing. This paper proposes an efficient reliability evaluation method for power systems with PV integration. The method reveals the analytical relationship between the reliability levels of the power system and the uncertainty factors that influence the reliability, such as the PV output. In this way, the dynamic reliability evaluation is achieved, and the evaluation results can be updated timely when the output of PV changes. First, a Gaussian mixture-hidden Markov model (GMM-HMM) is used to model the distribution characteristics of PV output. Then, the state enumeration and the hyperbolic truncated polynomial chaos expansion method are used to determine the analytical relationship between the reliability indices and PV output. Lastly, based on the analytical function, the operational reliability of the power systems is dynamically evaluated considering the real-time PV output. The effectiveness of the proposed method is verified using the modified IEEE 30 system as an example.

Power System Reliability Evaluation Based on Chronological Booth–Baleriaux Method

The complexity of modern power systems is increasing because of the development of various intermittent generators. In practical reliability evaluations, it is essential to include both the failure of conventional generators and the output characteristics of renewable energy; the use of the latter has increased rapidly. The weather-dependent nature of renewable energy output, which is inexplicable in the load duration curve method, highlights the need for further study of the methods of a reliability evaluation that can consider temporal characteristics. This paper proposes a deterministic reliability evaluation method based on the Booth–Baleriaux method, chronologically extended to address the preventative maintenance schedule of a generator and the characteristics of renewable energy. The proposed method was applied to an IEEE reliability test system for performance verification, and a reliability evaluation was performed considering various chronological patterns. The proposed method was also applied to determine the adequate capacity reserve that should be installed in a Korean power system. The proposed method is stable, and it produced robust results.

Power System Stability Estimation Scheme Based on Big Data

Aiming at the poor convergence of power flow calculation and the difficulty in location of the power system operation, this paper proposes a fast parallel calculation scheme for power system stability assessment based on big data technology. Through the analysis and research of Newton method which is sensitive to the initial value selection in the actual calculation of regional power flow, an approach for selecting the optimal initial value is proposed by genetic algorithm. Then, by the state sampling method and Monte Carlo parallel simulation, the coarse granularity algorithm under the message passing programming model is designed. The experimental analysis adopts the improved method to test the data in IEEE 9 system, actual environment, the automatic power flow adjustment. The results show that our scheme can achieve high parallel efficiency and acceleration ratio, which improves the calculation ability and accuracy of power flow and reliability evaluation of large-scale power systems.

Cluster-based stratified sampling for fast reliability evaluation of composite power systems based on sequential Monte Carlo simulation

An essential aspect in the operation and expansion of the electricity infrastructure is to ensure a reliable power supply, despite uncertainty (mainly caused by random failures, variability of renewable sources, and load growth). Reliability evaluation of composite power systems is a valuable tool for identifying possible deficiencies in operation. Power systems operation is becoming more variable and stochastic. Consequently, there is an urgent need to update the tools used to analyze their reliability. This article presents a novel method based on cluster-based stratified sampling (CBSS) and sequential Monte Carlo simulations (SMCS) to improve the reliability evaluation of composite power systems. Here, clustering algorithms are applied to reduce the number of observations required by traditional SMCS. The proposed approach is applied to RTS-79, RTS-96, and RTS-GMLC electrical networks to verify its accuracy and speed. The results obtained demonstrate that CBSS increase calculation speed in highly reliable networks, while preserving information on the probability distribution of the reliability indices.

Model-and-data hybrid driven method for power system Operational reliability evaluation with high penetration of renewable energy

With the rising penetration of renewable energy, the fluctuation of operating conditions such as wind speed and light intensity affects the reliability of the power system. It leads to the repeated reliability evaluation of the power systems and increases the computational complexity. Based on the non-sequential Monte Carlo simulation (NMCS) and back propagation neural network (BPNN), a new model-and-data hybrid driven method for power system reliability evaluation is proposed. Firstly, NMCS is used to calculate the system reliability indices under different operating conditions to obtain the input data for neural network training. Then, the proposed Multiple BPNN (M-BPNN) is used to establish the highly nonlinear mapping relationship between operating conditions and system reliability indices, which can significantly reduce the calculation time of power system operational reliability evaluation with high penetration of renewable energy. The IEEE-RTS 79 system is used to verify the effectiveness and efficiency of the proposed method, and sensitivity analysis is performed.

Review on Power System Reliability Indices and Evaluation Techniques

Now a day’s most accountable issue in the power industry is Reliability. power systems reliability using Quantitative analysis can be examined with the help of reliability indices. For a long period of time different methods introduced and developed to assess the reliability indices of the power system. In modern society Uninterrupted and Reliable electric power supply is necessary. The wide use of electricity has been lead to a high vulnerability to electric power failures. This is the reason, reliability of electric power has gained spotlight and it’s considered significant role while planning for a electric power system operation.In this paper, a summary is given of the technique of finding out the reliability of a power system. Readers will get an idea about the possibilities of measuring Reliability indices.The inspiration at the back of this paper is to set up a comprehensive general idea about Analytical and simulation technique for reliability assessment and serving for future research and development in the area of electrical power system.

Three-parameter interval number theory and its application in power system reliability evaluation

With the increasing integration of renewable energy in the power system, its strong volatility and randomness will greatly affect the planning and operation of the power system. In this situation, compared to the traditional point-value-based reliability assessment results, system operators prefer the results which can give the most likely recommended values and their possible ranges. To this end, in this paper, we present a novel framework to evaluate the reliability of multi-state power systems that incorporates three-parameter interval number theory and improved universal generating function method. In particular, we first improve the three-parameter interval number theory by proposing a similarity-based indicator to describe the distance (or closeness-degree) between two three-parameter interval numbers and redefine the probability that one three-parameter interval number is greater or less than another one. Secondly, we extended the traditional universal generating function to a three-parameter interval number-based universal generating function. Moreover, the three-parameter interval number-based universal generating functions of conventional generators, renewable energy generators and load demand are further established to describe their multi-state characteristics. At last, a three-parameter interval number-based loss of load expectation metric is presented for reliability evaluation. Compared with the existing methods, the proposed framework can well evaluate the reliability of multi-state power systems, and can describe the uncertainty of reliability evaluation results through the three-parameter interval number, thus providing the most likely value of the evaluation result and its fluctuation range at the same time. In the numerical simulation part, a comparison of five different scenarios is performed on an IEEE Reliability Test System. The results show that the proposed method is suitable for the reliability assessment of power systems with high penetration of renewable energy, which can describe the fluctuation range and indicate the most possible value of reliability simultaneously.

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%).

풍력발전기를 고려한 복합전력계통에서의 신뢰도 평가에 관한 연구

In this paper, the probabilistic reliability evaluation algorithm in terms of probabilistic supply adequacy according to the system input of wind turbine generator(WTG) previously proposed by this research team is presented in more detail for a composite power system that also considers the transmission system, and furthermore, the usefulness of the application is verified. In this study, the method was presented in detail through a sample case that can be done manually. Additionally, based on the proposed methodology, a model system similar to the system size of Jeju Island in Korea was assumed, and the reliability evaluation of the composite power system(HL II) considering a large number of WTG in the actual power system size was simulated in various ways, and its usefulness was shown in this paper. Finally, as an application example, the utility of the method developed in this study was verified by showing the possibility in terms of probabilistic reliability of the transmission system expansion planning, which has recently been emphasized more in importance. Therefore, it is expected that the method presented in this study will be practically usable for obtaining the optimal scenario in terms of probabilistic reliability evaluation when conducted the probabilistic transmission system expansion plan considering multiple WTGs.

Reliability evaluation of a composite power system in the presence of renewable generations

Power system reliability evaluation plays a vital role in the planning and operation studies by reflecting the system safety level. In this paper, a combination of a non-sequential Monte Carlo simulation (MCS)-based model and an improved Estimation of Distribution Algorithm (EDA) is exploited for evaluating the reliability of the composite power systems considering variability and uncertainty of wind farms (WFs) and Photovoltaic (PV) units. Variability of these resources is defined as the random fluctuation of wind speed and solar irradiation caused by changes in the atmosphere, while their uncertainty results from output power forecast errors. In the proposed model, the states of traditional generating units, transmission lines, WFs, and PV units are constructed using non-sequential MCS. These states can be achieved based on the component failure probability for dispatchable traditional generators and transmission lines along with the Probability Distribution Functions (PDFs) of renewable generations. To enhance the computational efficiency of the MCS in the sampling step, the improved EDA upgraded with the Population-Based Incremental Learning (PBIL) algorithm is employed. The proposed mathematical model for reliability evaluation of composite power system is applied to the IEEE RTS 24-bus system, and numerical studies are performed under several case studies. The simulation results confirm the proficiency of the proposed method to improve the computational efficiency, while the high accuracy of reliability evaluation of the composite power system is attained.

Mixed Aleatory-epistemic Uncertainty Modeling of Wind Power Forecast Errors in Operation Reliability Evaluation of Power Systems

As the share of wind power in power systems continues to increase, the limited predictability of wind power generation brings serious potential risks to power system reliability. Previous research works have generally described the uncertainty of wind power forecast errors (WPFEs) based on normal distribution or other standard distribution models, which only characterize the aleatory uncertainty. In fact, epistemic uncertainty in WPFE modeling due to limited data and knowledge should also be addressed. This paper proposes a multi-source information fusion method (MSIFM) to quantify WPFEs when considering both aleatory and epistemic uncertainties. An extended focal element (EFE) selection method based on the adequacy of historical data is developed to consider the characteristics of WPFEs. Two supplementary expert information sources are modeled to improve the accuracy in the case of insufficient historical data. An operation reliability evaluation technique is also developed considering the proposed WPFE model. Finally, a double-layer Monte Carlo simulation method is introduced to generate a time-series output of the wind power. The effectiveness and accuracy of the proposed MSIFM are demonstrated through simulation results.

Data-driven power system reliability evaluation based on stacked denoising auto-encoders

The increasing penetration of renewable energy resources in power systems inadvertently leads to a surge in the number of random states. The calculation of optimal load shedding for all the proliferated states is rather overwhelming. It has been considered a bottleneck in the reliability evaluation process. To address this issue, a deep-learning-based approach is proposed to evaluate system reliability more efficiently considering the fluctuation of generation and loads. In the proposed power flow model, a stacked denoising auto-encoder (SDAE) serves as a multilayer neural network for the deep-learning process. Its stacked structure and encoding-decoding recursion enable it to extract high-order features even from non-linear equations. The features of contingency states are crucial to directly acquire the minimum load curtailment without the time-consuming optimal power flow (OPF). The after-trained model is applied to states simulated with the Monte-Carlo method in the RTS-79 system. The numerical result reveals the great advantage of the SDAE-based method in computation time while ensuring high accuracy. As an alternative way to classic OPF algorithms, it can be integrated with the impact-increment method and other state selection approaches.

A data-driven accurate battery model to use in probabilistic analyses of power systems

Analytical battery models depend on a set of complex nonlinear equations that make them impractical to use in probabilistic analyses (e.g., reliability evaluation) of power systems. Machine-learning algorithms have the potential to reduce or even avoid the computational complexities of incorporating actual battery characteristics in probabilistic analyses. In this paper, a neural network (NN)-based approach is proposed to develop a battery model that captures the non-linear interactions between charging/discharging power and battery state of charge (SoC). In the proposed approach, an NN with a rectified linear unit activation function is trained using historical data generated from an experimentally validated battery model. Another NN with linear activation function is trained to capture the relationship between charging/discharging power limits and SoC. Weights and biases of the trained networks in conjunction with mixed integer linear programming are used to develop an accurate and computationally attractive battery model. Also, a mathematical model is formulated to accommodate the proposed battery model in power system reliability evaluation. Moreover, a genetic algorithm-based approach is used to determine optimal locations for batteries considering the developed model to enhance reliability of power systems. The proposed approach is demonstrated on a modified version of the IEEE 33-bus distribution system. Monte Carlo simulation is performed to calculate reliability indices. The results show that the proposed battery model is effective to incorporate actual battery characteristics in probabilistic analyses (evaluating reliability and finding optimal battery locations) of power systems.

Reliability Evaluation of Cyber-Physical Power Systems Considering Random Failures in Measurement and Remote Control

In integrated cyber-physical power systems (CPPS), the malfunctions of cyber system deeply affect the dispatcher’s remedial action scheme, and thus have a significant impact on the reliability of the physical power system. The random failures of electrical devices in physical power system and the random outages of measurement and remote control in cyber system are both considered for the CPPS reliability evaluation in this paper. Firstly, the component outage models in physical, access, backbone and application layers are presented. Then, a linearized DC state estimation (SE) model is introduced and the SE errors incurred by measurement failures, i.e. bad and lost measurements, are interpreted. Moreover, the impact of SE errors and remote control failures on the corrective action scheme of the control center during the fault recovery process is simulated by a novel modification of the classical optimal load shedding (OLS) model. Finally, to verify the effectiveness and validity of the proposed CPPS reliability evaluation method, the communication network topologies of the cyber system for the RBTS and IEEE-RTS96 are designed, and then the reliability evaluation based on the presented component outage models and the novel modified OLS model are conducted by the non-sequential Monte Carlo simulation (NMCS).

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.