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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]

• 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 light of the additional needs imposed by high penetration level of renewable sources on the planning of electric networks, this paper introduces new models and procedures for obtaining more robust and flexible networks. A new methodology, computational algorithm, and a new set of performance indices are presented based on the concept of chronological power flow (CPF). In view of the presence of intermittent energy resources, aspects related to the operation of the system during moments of high energy availability will be duly evaluated, since that is when there might be a waste of renewable energy, and also during low renewable production conditions, when the system may be more fragile. The results obtained for the IEEE Reliability Test System—1996 will also be presented and discussed. [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 proposes a new methodology based on Monte Carlo chronological simulation to evaluate the operating reserve requirements of generating systems with large amounts of renewable energy sources. The main idea is to build a new probabilistic approach to assess several performance indices bearing in mind the long-term planning. Besides the conventional indices used in generating capacity reliability assessment and also those obtained from the well-being framework, a new set of indices are proposed to measure the availability of different types of operating reserve. The relationship between the proposed approach and the traditional PJM method is also discussed. Renewable in this study mainly comprises wind power sources and for that the IEEE RTS-96 is adapted to illustrate the proposed methodology. Finally, different planning criteria are discussed to deal with the design of generation system capacity, static and operational, in the long-term planning. [ABSTRACT FROM AUTHOR]