An advanced neural network based solution to enforce dispatch continuity in smart grids

In energy generation systems including a photovoltaic park, fluctuations are the norm: both production and demand levels can vary on hourly basis. Hence, energy management and dispatching systems have to cope with the possibility of inadequate production while satisfying as much as possible user demands. We put forward a management solution that models the behaviour of each production plant and consumption device, and determines energy allocation. For this, gathered data are wavelet transformed to let us retain only the useful characteristics of data on both large and small scales of the signal. Models are handled by several neural networks which perform predictions in advance of 48-hour, with a granularity of half an hour. Moreover, according to realtime user demands, the management solution determines energy flows between production plants and consumption devices. Therefore, while in some cases it might be necessary to postpone the activation of some consumption devices, in others we can take advantage of a production surplus. Thanks to the proposed solution proper actuators can be programmed beforehand to improve the fairness to users, and use peaks of energy production, thus reducing green energy shortage, and extra costs.