A smart grid in Ponza island: battery energy storage management by echo state neural network

Renewable electricity generation has variable and non-dispatchable output that rises several technical, economic and feasibility concerns, calling for energy storage capacity and forecasting techniques to allow the integration of large amounts of variable generation into existing grids. These problems need careful attention in small islands that are not connected to the national transmission grid. In this paper, we present a study for the small Italian island of Ponza on the use of Echo State Networks to forecast real-world energy time series. In particular, the prediction is applied to the PV plant production and to the load of the electric grid of the whole island. The prediction results are then used to relieve the use and the cost of the diesel generation, by optimally managing a Battery Energy Storage System. This forecasting strategy has good performance, proving that Echo State Networks are suited to the focused application