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Peak Demand Shaving Based on Solar and Load Forecasting at Port of Gavle
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0003-1678-1588
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0002-6095-7811
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0003-4763-9429
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2022 (English)In: 2022 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022, Institute of Electrical and Electronics Engineers (IEEE) , 2022Conference paper, Published paper (Refereed)
Abstract [en]

Solar energy is considered as one of the most promising solutions to prevent climate change, and optimal utilization of solar energy contributes to reducing dependence on non-renewable energy sources. Distributed generation can help support the delivery of clean and reliable power to consumers. In this paper, an Energy Management System (EMS) is developed along with load and Photovoltaic (PV) generation forecasting to perform peak shaving of electricity demand. The results from the proposed method are implemented in a real case study, in Port of Gavle in Sweden. Total revenue generated from services of peak-shaving and frequency regulation is maximum in the scenario considering high PV generation and high consumption on a selected experimental day in July. The results show that PV-battery storage systems are effective options to increase the integration of renewable energies in the grid, flatten the load, and achieve stabilized operation.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022.
Series
International Conference on Probabilistic Methods Applied to Power Systems, ISSN 2642-6730
Keywords [en]
electricity generation, forecast, frequency regulation, peak shaving, XGBoost
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:kth:diva-320422DOI: 10.1109/PMAPS53380.2022.9810648ISI: 000853744900091Scopus ID: 2-s2.0-85135074197OAI: oai:DiVA.org:kth-320422DiVA, id: diva2:1705232
Conference
17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022, Manchester, 12 June 2022, through 15 June 2022
Note

QC 20221021

Part of proceedings: ISBN 978-166541211-7

Available from: 2022-10-21 Created: 2022-10-21 Last updated: 2022-10-21Bibliographically approved

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Gopalakrishnan, PavithraAlikhani, ParnianShafique, HamzaBertling Tjernberg, Lina

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