Abstract
Islands are facing significant challenges in meeting their energy needs in a sustainable, affordable, and reliable way. Traditionally, the primary source of electricity on the islands has been imported diesel fuel, with high financial costs for most utilities. In this context, even replacing part of the traditional production with renewable energy source can reduce costs and improve the quality of life of islanders. However, integrating large amounts of renewable energy production into existing grids introduces many concerns regarding feasibility, economic analysis, and technical implementation. From this point of view, machine learning and deep learning techniques are efficient tools to mitigate these problems. Their potential results are beneficial considering isolated grids of small islands which are not connected to the national grid. In this paper, a study of the Italian island of Ponza is carried out. The isolation leads to several challenges, such as the high cost related to the transport, installation, and maintenance of renewable energy sources in a small area with several constraints and their intermittent power production, which requires the use of storage systems for dispatching purposes. The proposed study aims to identify future developments of the electricity grid by considering the deployment of both renewable energy sources and energy storage systems. Furthermore, future scenarios are depicted through the use of autoregressive and deep learning techniques to give an idea about the economic costs of both energy demand and supply.