Search

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 work presents a new method to evaluate generation reserve margins in systems with renewable sources. In assessing the adequacy of generation reserve amounts, besides failures in generating units, their capacity intermittencies, unavailability, and capacity limits of the transmission system are duly considered. Risk indices are evaluated using quasi-sequential Monte Carlo simulation techniques. The cross-entropy method is used to treat rare events and also to identify critical equipment for operation in each scenario. The proposed method is applied to the original IEEE RTS system and to a modified configuration with insertion of wind power plants. A subsystem of the Brazilian interconnected network is also used to illustrate the practicality of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to evaluate loss of load indices in composite generation and transmission systems. The main idea is to combine a Cross-Entropy (CE)-based optimization process and nonsequential Monte Carlo Simulation (MCS) to obtain an auxiliary sampling distribution, which can minimize the variance of the reliability index estimators. This auxiliary sampling distribution will properly modify the original unavailabilities of both generation and transmission equipment, so that important failure events are sampled more often. As a result, the MCS algorithm can reach convergence faster and with fewer samples, leading to significant gains in computational performance, especially when dealing with very reliable system configurations. The proposed method is tested using several composite power systems, including the IEEE RTS 79, IEEE RTS 96, and a configuration of the Brazilian system. [ABSTRACT FROM PUBLISHER]

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]

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]