Cooperative Strategy for Optimal Management of Smart Grids by Wavelet RNNs and Cloud Computing

Advanced smart grids have several power sourcesthat contribute with their own irregular dynamic to the powerproduction, while load nodes have another dynamic. Severalfactors have to be considered when using the owned powersources for satisfying the demand, i.e., production rate, batterycharge and status, variable cost of externally bought energy,and so on. The objective of this paper is to develop appropriateneural network architectures that automatically and continuouslygovern power production and dispatch, in order to maximize theoverall benefit over a long time. Such a control will improve thefundamental work of a smart grid. For this, status data of severalcomponents have to be gathered, and then an estimate of futurepower production and demand is needed. Hence, the neuralnetwork-driven forecasts are apt in this paper for renewablenonprogrammable energy sources. Then, the produced energy aswell as the stored one can be supplied to consumers inside a smartgrid, by means of digital technology. Among the sought benefits,reduced costs and increasing reliability and transparency areparamount.