» Papers
The FLEXERGY project team has published the following papers:
Venue: 3rd International Conference on Smart energy Systems and Technologies (SEST) – Instambul, Turkey, 7 – 9 September 2020

https://www.sest2020.org/

Paper: 233 - A Hybrid Approach to Load Forecast at a Micro Grid level through Machine Learning Algorithms

Abstract:
Electric power systems’ operation has been facing new challenges. Intermittent renewable energy production and the consumption side uncertainty has been increasing, not only due to the integration of renewable sources but also flexible loads such as plug-in electric vehicles charging and storage devices. For these reasons, electricity load forecasting is crucial, in the sense of being able to determine the stability of the generation system and maintenance of scalable loads. This paper addresses the forecasts of electricity demand in a Micro Grid context and presents the novel HALOFMI methodology, which includes a Micro Grid scenario, selection and reduction of features and subsequently feeding these entries to the Artificial Neural Network. Final measures include validating the results attained from the developed 24-hour load forecast model defined.
Venue: IEEE ISGT Europe – Bucharest, Romania, 29 Sept – 2 Oct 2019

https://site.ieee.org/isgt-europe-2019

Paper: 372 - Integrating Hybrid Off-grid Systems with Battery Storage: Key Performance Indicators

Abstract:
A clear opportunity exists for the integration of Battery Energy Storage Systems (BESS) in hybrid off-grid applications, i.e., isolated grids with renewable sources (e.g. photovoltaic, wind) and small-scale diesel generators. In these applications, renewable sources have the potential to reduce fossil fuels derivatives consumption and reduce Greenhouse Gases (GHG) emissions. BESS present the capability of maximizing the integration of renewable energy and, consequently, further offset the use of diesel-fired generating units. Therefore, this work advances an operating strategy for the day-ahead planning of the generation units in the microgrid based on the use of Key Performance Indicators (KPIs).