Parametrization of stochastic load profile modeling approaches for smart grid simulations

This paper presents an adaption of stochastic load profile modeling for application in distribution grid simulations. Such load profiles are necessary for network expansion planning as well as for state estimation in case of unavailable measurements. Relevant properties of the synthetic load profiles generated by Markov chain-based approach and a linear regression model will be adapted by a denormalization for using in smart grid simulations. First, a briefly explanation of the used profile modeling approaches will be given and the resulting weak points will be demonstrated. In relation to this, the denormalization process for the adoption of the synthetic profiles will be presented. The validation of the proposed method will be carried out by the comparison of the relevant properties for smart gird simulations before and after the denormalization. The results of this evaluation are contributed to assess potential fields of application of the synthetic profiles presented in this paper.