Chronological Monte Carlo Simulation Research Articles

Probabilistic Method for Optimizing the Number and Timing of Substation Spare Transformers

This paper proposes a new probabilistic method based on chronological Monte Carlo simulation for computing optimal distribution substation spare transformers. The method allows the representation of events such as aging process, load growth, and other conditions not supported by traditional methods based on Poisson and Markov processes. The lifetimes of the transformers are represented by discrete probability distributions, determined by an algorithm that combines the aging of the insulating material, estimated by Arrhenius theory, with the loss of life caused by short-circuits, lightning and switching surges. To illustrate the importance of sizing the inventory based on reliability indices and costs, the proposed method is applied to a group of substations with 177 transformers of 138–13.8 kV, with power rating of 25 MVA. Finally, the proposed methodology is used in combination with a metaheuristic algorithm for determining the optimal timing strategy for composing of the stock of spare transformers over a pre-established planning horizon.

Probabilistic Methodologies for Determining the Optimal Number of Substation Spare Transformers

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-kV distribution transformer system, and the obtained results are compared to those from a widely used model based on a Poisson distribution.