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• A new quasi-sequential Monte Carlo simulation (QS-MCS) method properly designed to the reliability assessment of distribution grids with renewable DG and other time-dependent events is proposed. • ew test functions are described and discussed to calculate the usual distribution reliability indices. • The computational efficiency of the proposed QS-MCS is similar to non-sequential-based tools, but its accuracy and modeling flexibility are at the same level as sequential-MCS tools. • The reliability indices of two distribution systems, a test system and a real distribution network from Brazil, are evaluated under the presence of renewable DG sources. • The algorithm is built using object-oriented programming and all the results obtained are compared and validated to those evaluated by a Sequential-MCS tool. The increasing insertion of distributed generation (DG) in distribution systems has profoundly altered the typical structure of these grids. In general, renewable DG resources are intermittent and make the reliability performance evaluation of distribution networks more complex. Thus, it is essential to have an efficient method and models capable of adequately representing the stochastic characteristics of load, generation and grid, including the protection system response. The proposed quasi-sequential Monte Carlo simulation (QS-MCS) aims to capture in detail the behavior of these characteristics, just like sequential or chronological simulations do, but with the same simplicity and performance of non-sequential simulations. Based on new unbiased test functions, distribution reliability indices are calculated to cope with the insertion of DG considering two systems, and discussions are raised about the flexibility, accuracy and computational efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a simple and computationally efficient methodology for computing robust dynamic equivalents. While simple to implement, the methodology involves many factors, and these are discussed, explained and justified throughout the paper. It classifies clusters of generators to be equivalenced by their electrical distances, and identifies second order reduced models for them. It maps them into classical synchronous machine models and aggregates them. Reduction of the network is based on the Ward PV method. The quality of the dynamic equivalent, in terms of flexibility and reduced dependence on disturbances, is automatically verified by performance indices. The methodology can easily be embedded in time domain simulation packages. Two large real power system networks are used for testing the performance of the approach. [ABSTRACT FROM AUTHOR]

This paper proposes a new evolutionary algorithm to solve transmission expansion planning problems in electric power systems. In order to increase the robustness of the search process and to facilitate its use by planners on different networks and operation conditions, the proposed method uses multi-operators and a mechanism for dynamic adaptation of the selection probabilities of these operators. Two sets of search operators are proposed: evolutionary and specialized. The mathematical formulation considers a DC network model including transmission losses and the “N-1” deterministic criterion. The proposed method is applied to a well known academic test system and a configuration of the Brazilian network. [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]

• The GMS problem is assessed from the ISO point of view based on GENCOs schedules. • The generation maintenance scheduling problem is assessed from both points of view, ISO and GENCOs. • Production cost and load shedding are minimized ensuring reliability constraints. • Uncertainties of load, generating capacity unavailability and renewable energy are duly modeled. • Non-sequential Monte Carlo simulation and cross entropy methods are properly combined. • The evolution strategy metaheuristic is used as the main tool to solve the problem. This work proposes an efficient method for solving the generation maintenance scheduling (GMS) problem, defined through an interactive process carried out between the independent system operator (ISO) and generation companies (GENCOs). In this problem, starting from a schedule previously informed by GENCOs, it is desired to minimize the expected costs of production and system load shedding for the period of analysis from the ISO point of view. The Evolution Strategy metaheuristic is used to solve the resulting optimization problem. The non-sequential Monte Carlo simulation and Cross-Entropy methods are combined to efficiently assess the maintenance schedules searched during the solving process. Uncertainties related to the behavior of load, unavailability of generation equipment, and variability of renewable energy sources are taken into account in the modeling and solution of the GMS problem. The performance of the proposed method is tested with the IEEE-RTS modified with the inclusion of renewable sources. [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]

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]

• More realistic system representation when compared to previous assessment methods for distribution transformer stock sizing. • The effects of using load transfers and mobile unit substations on the reliability indices are demonstrated. • Any probability distribution can be used to represent operating and waiting times. • Exponential and non-exponential times to failure are tested and the results discussed. • Aging exerts a significant influence on reliability indices and operating costs. • The proposed method is tested in a real system through several examples to demonstrate its flexibility and practicality. This paper proposes a new and practical probabilistic method for calculating reliability indices and costs associated with distribution substations that share spare transformers of a same group. Based on Monte Carlo simulation, the proposed algorithm considers the chronological representation of failure occurrences, and enhances previous methods by modeling emergency actions, such as transferring load to neighboring feeders and connecting a mobile unit substation to ensure electricity supply to customers during the replacement of failed transformers. Thus, spare transformer requirements with new acquisitions to replenish the stock or even to expand the system are probabilistically assessed. All waiting times associated to these actions can be duly represented by any probability distribution, including the usual exponential model. A more accurate calculation of reliability indices and costs associated with the interruption of consumers is achieved. The corresponding computational algorithm is used in several numerical applications, considering a class of transformers of a Brazilian electricity utility. The results of these applications are deeply discussed to demonstrate the practical relevance of the proposed method. [ABSTRACT FROM AUTHOR]

In power system planning studies that involve the search for optimal investments with low risks for the power grid, probabilistic reliability assessments provide very useful tools and indices. A challenge faced in the application of these tools is related to the computational effort demanded by the evaluation process, especially when the combined effects of failure of generation and transmission equipment are considered. In this context, the present work proposes a new method for efficient estimation of the main composite reliability indices by combining Binary Logistic Regression (BLR) technique, a machine learning tool used for binary data classification, with the non-Sequential Monte Carlo simulation (NS-MCS) method. In addition, a computational parallelization strategy is incorporated to the proposed method to improve even more the efficiency of the composite reliability assessment. The performance of the proposed approach is analyzed by evaluating composite reliability indices for the IEEE-RTS considering two different generation and load scenarios, in addition to a real large-scale power system. The results obtained are compared with those using the NS-MCS method in its conventional version. • Method based on a machine learning tool and the use of computational parallelization. • Use of knowledge related to the unavailability of critical transmission equipment. • Sensitivity analysis in relation to the main parameters that define the method. • Analysis of the method performance in the evaluation of a real and large-scale system. [ABSTRACT FROM AUTHOR]