Abstract
Automated grids are new solutions for enhanced electrical services to the active consumers from both social and technical aspects. This paper develops an advanced deep learning based framework for optimal management of the distribution automated systems considering the social and technical costs with regards to the renewable sources and electric vehicles. To this end, generative adversarial network model is utilized to predict the solar unit power generation for the next 24 h. In order to tackle with the uncertainty effects, a suitable framework based on unscented conversion is introduced which can model the correlated uncertainty. In order to consider the practical model for the electric vehicles, a smart charging and discharging scheme based on V2G concept is devised. This will let the automated grid make use of the electric vehicles as active and mobile power storages in the area. In order to solve the problem, a new optimization method based on harmony search algorithm is proposed. An IEEE test system is used as the case study to assess the model performance.
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