Development and testing of a micro-grid excess power production forecasting algorithms

Citations

Angeliki Mavrigiannaki, Nikos Kampelis, Denia Kolokotsa, Daniele Marchegiani, Laura Standardi, Daniela Isidori, Cristina Christalli, Development and testing of a micro-grid excess power production forecasting algorithms, Energy Procedia, Volume 134, 2017, Pages 654-663, ISSN 1876-6102, https://doi.org/10.1016/j.egypro.2017.09.583.

Abstract

Traditional electricity grids lack flexibility in power generation and load operation in contrast to smart-micro grids that form semi-autonomous entities with energy management capabilities. Load forecasting is invaluable to smart micro-grids towards assisting the implementation of energy management schedules for cost-efficient and secure operation. In the present paper is examined the 24h forecasting of excess production in an existing micro-grid. Alternative input parameters are considered for achieving an accurate prediction. The prediction can be used for scheduling the charging process of a thermal storage during weekends based on excess power production levels.