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The still growing share of intermittent renewable sources in the electric energy matrix is a factor that greatly increases the complexity of modelling electrical power systems. Although its exploitation is conditioned to the geographic location where the natural resource emerges itself, when this location coincides with the grid‐edge of the system, close to the consumer, the composite reliability assessment is exposed to the combination of two problems: (i) representation of the intermittent energy availability of generators; and (ii) failure event rarity. To address the first problem, a fast Monte Carlo tool capable of representing chronological aspects can be used. For the second problem, an efficient variance reduction technique that adapts well to the composite problem of generation and transmission must be established. Therefore, a quasi‐sequential Monte Carlo simulation tool aided by importance sampling via the cross‐entropy method is proposed as an efficient and robust procedure for evaluating the composite reliability of systems with renewable participation at grid‐edge. The method is evaluated through a modified version of the IEEE Reliability Test System ‐ 1996, which presents renewable generation in consumer load buses and rarity in the composite failure. [ABSTRACT FROM AUTHOR]

• A new method that allows the effects of generator aging to be included in the reliability analysis of power systems is proposed. • A novel model for the failure rate capable of representing the stages of normal operation and wear-out is described. • Catastrophic failures and equipment refurbishing actions are also included in the proposed method. • The new method is built using a chronological Monte Carlo simulation and variance reduction techniques. • The aging of components can cause significant reductions in the reliability of generating systems. Equipment aging has been a major concern among electric utilities' planners, since quality of service can be put at risk. This paper proposes a new method that allows the effects of the aging of generators to be properly included into the reliability analysis of power systems. First, a new model for the failure rate capable of representing the stages of normal operation and wear-out, replacing the adoption of constant failure rates, is described. The effects of repairable and non-repairable (i.e., catastrophic) failures on the reliability assessment of generation systems are also discussed. The proposed method is built from the previous models using a sequential Monte Carlo simulation and variance reduction techniques. To validate the proposed method, the IEEE Reliability Test System is used with modifications in which different ages are defined for existing generating units that are progressively aging over the years. Several tests are described together with strategies for adopting preventive refurbishments and the corresponding results are duly discussed. [ABSTRACT FROM AUTHOR]

Abstract: This paper discusses the development of a Multi-Agent Systems (MAS) technology-based platform with potential applications in management and simulation processes in power systems. In order to explore some of the features of MAS, a new methodology is proposed to assess power systems reliability based on Monte Carlo simulation (MCS), exploiting the benefits of the distributed artificial intelligence area and, mainly, the use of the distributed capacity in two ways: building autonomous behaviors to the applications and mitigating computational effort. Through the use of this technology, it was possible to divide the MCS algorithm into distinct tasks and submit them to the agents’ processing. Two different approaches to solve generating capacity reliability problems based on chronological MCS illustrate the potential of MAS in power systems reliability assessment. [Copyright &y& Elsevier]

This paper proposes a new methodology to evaluate system generating capacity reliability indices considering time-dependent power sources and loads. Based on sequential Monte Carlo simulation (MCS) and the cross-entropy (CE) method, the basic idea is to find an optimal distortion for the equipment transition rates using the CE concepts. A chronological simulation is then carried out using these newly found optimal reference parameters. This process suitably modifies the chronological evolution of the system in order to improve its statistical efficiency and convergence properties. As a result, the computational efforts of the sequential simulation can be greatly reduced while retaining many of its main advantages. Comparisons with the chronological, pseudo-chronological, and quasi-sequential MCS are carried out using the IEEE-RTS-96 (Reliability Test System) and modifications of this system that include renewable sources. [ABSTRACT FROM PUBLISHER]

Abstract: This paper proposes a new methodology to solve transmission expansion planning (TEP) problems in power system, based on the metaheuristic ant colony optimisation (ACO). The TEP problem includes the search for the least cost solution, bearing in mind investment cost and reliability worth. Reliability worth is considered through the assessment of the interruption costs represented by the index LOLC – loss of load cost. The focus of this work is the development of a tool for the multi-stage planning of transmission systems and how reliability aspects can influence on the decision-making process. The applications of the proposed methodology are illustrated through case studies carried out using a test system and a real sub-transmission network. [Copyright &y& Elsevier]

This paper presents a new Monte Carlo simulation (MCS) approach based on cross-entropy (CE) method to evaluate generating capacity reliability (GCR) indices. The basic idea is to use an auxiliary importance sampling density function, whose parameters are obtained from an optimization process that minimizes the computational effort of the MCS estimation approach. In order to improve the performance of the CE-based method as applied to the GCR assessment, various aspects are considered: system size, rarity of the failure event, number of different units, unit capacity sizes, and load shape. The IEEE Reliability Test System is used to test the proposed methodology, and also various modifications of this system are created to fully verify the ability of the proposed approach against both, a crude MCS and an extremely efficient analytical technique based on discrete convolution. A configuration of the Brazilian South-Southeastern generating system is also used to demonstrate the capability of the proposed CE-based MCS method in real applications. [ABSTRACT FROM AUTHOR]

This paper proposes improvements in the sequential Monte Carlo (MC) simulation based approaches for reliability evaluation of composite generation and transmission systems. The main idea is to compute a threshold load level to facilitate the process of state evaluation, which is very time-consuming mainly due to optimal power flow computations. Two main sampling techniques namely random sampling (RS) and Latin Hypercube sampling (LHS) are applied to the proposed sequential MC simulation technique. Their performances in terms of simulation time as well as solution accuracy are compared. Case studies using the IEEE Reliability Test System (IEEE RTS) are utilized to demonstrate the performance of the proposed approach. Reliability indices including loss of load probability, expected unserved energy, loss of load frequency and duration are estimated. It is shown that the proposed approach considerably reduces the computational requirements during state evaluation, and that the application of LHS can further decrease the simulation time and required sample size to reach convergence. [ABSTRACT FROM PUBLISHER]

Summary: Composite generation and transmission system reliability evaluation allows the assessment of the risks of system operation failure, taking into account the uncertainties associated with the availability of equipment. One of the great challenges faced in the use of techniques based on probabilistic assessment during the planning stages is related to the required computational costs. Depending on the reliability levels of the system under study and on the grid size, a large number of operation performance analyzes are necessary. In this sense, this article proposes a new and simple method to efficiently evaluate the composite reliability of electrical power networks. The nonsequential Monte Carlo simulation (MCS) method is combined with unsupervised machine learning (UML) techniques to reduce the computational effort involved in the process of estimating composite reliability indices. The proposed approach allows different unsupervised techniques to be employed, in order to obtain significant reductions in CPU times, without losing the accuracy of the estimated indices. The IEEE‐RTS system, considering the original load and generation and its modified version with the transmission network stressed, in addition to a real large system, is used for evaluating the performance of the proposed method. The results obtained with the use of three different classification techniques (Kohonen self‐organizing map, K‐means, and K‐medoids) are presented and analyzed. [ABSTRACT FROM AUTHOR]