Distribution network planning based on statistical load modeling applying genetic algorithms and Monte-Carlo simulations

Two types of load uncertainties for planning studies, namely long term related to economic development and short-term related to time/weather factors, can be identified. In this paper the attempt is made first to establish a probabilistic model of short-term load uncertainties caused by time/weather factors. Then the algorithm able to cope with noisy function, such as planning criteria depending on stochastic loads, is suggested. The algorithm is based on simple GA with built in Monte-Carlo simulation block. The paper also contains the investigation of convergence properties of stochastic GA for different levels of noise. It is shown that it is possible to obtain the optimal compromise number of trials.