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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 available transfer capability (ATC) is an important parameter for all companies participating in the power transaction activities. The main objective of this work is to propose a new methodology to determine the best points in the system to add new agents (sellers and buyers), in order to maximize the ATC, without violating a pre-established reliability level. In the process, the ATC probability density function is determined considering uncertainties from equipment un-availabilities. The proposed algorithm uses Monte Carlo simulation to select system state and linear programming with a dc power flow model, to analyze and optimize each selected state. The IEEE Reliability Test System (RTS) is used to illustrate the proposed methodology. Index Terms--Available transfer capability, composite reliability, Monte Carlo simulation, transmission capacity.

Summary: In this paper, the static transmission expansion planning (TEP) problem is solved for real large electrical networks. Different generation and load scenarios are considered and the "N‐1" security criterion is ensured. The solution of the problem is achieved based on a constructive metaheuristic algorithm (CMA‐TEP) specially developed for large systems. The proposed method performs a gradual and parallel process of constructing solutions (expansion plans), which incorporates heuristics and procedures generally used by planning engineers. Operational information for the intact network condition and under contingencies is used during the constructive process, to identify reinforcements to be added (most attractive) to or removed (less effective) from the network being set up. Moreover, mechanisms of recombination, perturbation, selection, refinement, and depuration of solutions are also employed during the constructive process. The linear DC network model, including ohmic transmission losses, is used throughout the entire constructive process. The robustness and efficiency of the proposed CMA‐TEP method is tested in a configuration of the Brazilian network, considering two hydrological scenarios: dry and humid. Comparisons based on statistical performance indices are also carried out with specialized genetic algorithms. [ABSTRACT FROM AUTHOR]

This paper presents new probabilistic methodologies for computing the optimal number of transformer spares for power distribution substations. The basic idea consists of three steps: 1) the reliability evaluation of a given system of transformers with inventory of spares; 2) the calculation of investment and operational costs of the system for different alternatives of inventory composition; and 3) the identification of the number of spares that minimizes the total cost. Two new models are proposed for the reliability evaluation step. In the first one, the system operational states are represented by a Markov process. The second one uses a chronological Monte Carlo simulation model to assess the reliability performance of a system with inventory of spares. Both models are able to provide indices such as probability, frequency, and duration of failures, as well as estimates of energy not supplied and the corresponding costs. The proposed methodologies are applied to a 72-ky distribution transformer system, and the obtained results are compared to those from a widely used model based on a Poisson distribution. [ABSTRACT FROM AUTHOR]