4264383
doi
10.1002/er.5963
oai:zenodo.org:4264383
user-eu
Stefanos Ntomalis
Centre for Research & Technology Hellas/Chemical Process and Energy Resources Institute, 6th km. Charilaou-Thermis, Thermi, GR 57001, Greece
Konstantinos Atsonios
Centre for Research & Technology Hellas/Chemical Process and Energy Resources Institute, 6th km. Charilaou-Thermis, Thermi, GR 57001, Greece
Athanasios Nesiadis
Centre for Research & Technology Hellas/Chemical Process and Energy Resources Institute, 6th km. Charilaou-Thermis, Thermi, GR 57001, Greece
Nikos Nikolopoulos
Centre for Research & Technology Hellas/Chemical Process and Energy Resources Institute, 6th km. Charilaou-Thermis, Thermi, GR 57001, Greece
Panagiotis Grammelis
Centre for Research & Technology Hellas/Chemical Process and Energy Resources Institute, 6th km. Charilaou-Thermis, Thermi, GR 57001, Greece
Energy management and techno‐economic assessment of a predictive battery storage system applying a load levelling operational strategy in island systemsv
Petros Iliadis
Centre for Research & Technology Hellas/Chemical Process and Energy Resources Institute, 6th km. Charilaou-Thermis, Thermi, GR 57001, Greece
url:https://onlinelibrary.wiley.com/doi/epdf/10.1002/er.5963
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Battery Energy Storage System
load levelling
Load Forecast
Peak Shaving
RES forecasting
<p>In the present study, a predictive battery energy storage system (BESS) for application in geographical non‐interconnected islands with high renewable energy penetration is proposed, capable of performing load levelling. The system under consideration is composed of diesel and heavy oil generators, a photovoltaic farm, and a small wind turbine. The proposed solution integrates machine learning (ML) methods for the forecasting of load and intermittent solar and wind power productions, alongside a custom scheduling algorithm, which calculates the necessary BESS setpoints that accomplish the desired levelling effect. An important feature of the scheduling algorithm is that the charge and discharge energy amounts of each day are by design equal and independent of the forecasts’ accuracy. This aspect enables economic investigations to identify the appropriate BESS capacity for the particular system, also taking into account the battery's capacity degradation. The overall system is modelled and simulated utilizing the open‐source languages Python and Modelica. Simulations presented a 9.8% peak‐to‐mean ratio (PMR) reduction of the thermal plant's load. Furthermore, economic investigations estimated a marginal BESS cost of 287.1 €/kWh revealing the financial viability of the proposed integrated system, in at least the case of geographical islands.</p>
Zenodo
2020-10-02
info:eu-repo/semantics/article
4264382
user-eu
award_title=SMart IsLand Energy systems; award_number=731249; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/731249; funder_id=00k4n6c32; funder_name=European Commission;
1604924825.521196
1959232
md5:ec34fa1958270c4a898d4bfbe4f9b7e8
https://zenodo.org/records/4264383/files/ER-20-16038.R1-preprint.pdf
public
https://onlinelibrary.wiley.com/doi/epdf/10.1002/er.5963
Is cited by
url
International Journal of Energy Research
2020
1-19
2020-10-02