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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]

This paper presents a new method for allocating transmission system costs in hydro prevailing energy markets. The proposed approach is based on nodal pricing, and it considers multiple hydrological scenarios obtained from optimized energy models. These models are much closer to the operational reality of hydro dominant systems and more adequate to the transmission operation planning. The nodal pricing strategy comprises two steps to recover the total costs of the transmission system. The first one calculates the charges corresponding to the utilized network capacity, estimated at a specified operating condition, and it brings the locational signal to the tariff. In the second step, the charges related to the transmission capacity available but not used in the system are assessed by a postage stamp. This latter step accomplishes the task of recovering the total transmission charges and defining the final tariff for specific operating dispatching. On the other hand, the optimized energy models, based on stochastic dual dynamic programming, are used to minimize the operating costs while penalizing possible energy spillages. These models create monthly sequences of hydrothermal coordinated generating scenarios and dispatching conditions, which are used by the nodal pricing strategy to produce the generation/load charges and tariffs and the corresponding statistics. The proposed method, combining the nodal pricing strategy and the optimized energy model, is applied to a Brazilian network configuration and the results are fully discussed. Moreover, probabilistic analyses are also performed to describe the annual costs of transmission of thermoelectric and hydroelectric plants and industrial electrointensive consumers installed in different areas of the network. [ABSTRACT FROM AUTHOR]

• 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]

• 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]

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]

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]