Appropriate load modeling for evaluating the reliability of smart distribution systems in order to decrease the computing time concerning the accuracy of calculations

This paper presents a novel appropriate load modeling for decreasing the load levels and evaluating the reliability of smart distribution system including various distributed generations by using the analytical method. Because of several uncertainties in a smart distribution system, the computing time and complexity of calculations in evaluating the reliability indices are the major challenges. Hence by decreasing the computing time of reliability evaluation methods, it is possible to solve the large optimization problems according to the accelerated methods. During the decrement of computing time, it is necessary to achieve a reasonable accuracy. In the proposed method, three scenarios for reliability-based load modeling are presented. The time-based aggregation without any default, time-based aggregation according to several assumptions, and amplitude-based aggregation are the three presented scenarios. Providing the mathematical modeling for implementation of the discussed scenarios is one of the contributions of this article.
The proposed method has been applied to the IEEE 33-Bus Test Distribution System and an actual distribution system from Esfahan Province Electrical Distribution Company (EPEDC). The obtained results illustrated the effectiveness of the proposed method. The results show that, by choosing an appropriate load level reduction scenario (time-based or amplitude-based aggregation scenarios), the 90% of the required time for reliability calculations can be saved, while less than 1% error has occurred. Performing the sensitivity analyses in order to determine the more important parameters in the proposed load level reduction methodology is another advantage of this article. The sensitivity analyses results imply that due to increment in load variance of the distribution system in presence of other uncertain parts, the more comprehensive and smart studies are needed.