期刊
ENERGY REPORTS
卷 8, 期 -, 页码 405-410出版社
ELSEVIER
DOI: 10.1016/j.egyr.2022.01.043
关键词
Clustering; K-means; Energy Storage; Scheduling; Prosumers
资金
- FEDER Funds through COMPETE program
- National Funds through (FCT) [UIDB/00760/2020, PTDC/EEI-EEE/28967/2017, CEECIND/02887/2017, SFRH/BD/144200/2019]
- ANI (project GREEDi)
- Fundação para a Ciência e a Tecnologia [PTDC/EEI-EEE/28967/2017, SFRH/BD/144200/2019] Funding Source: FCT
Active communities are emerging to create a cleaner and safer energy system. The uncertainty of main resources calls for flexibility from the demand side. This study focuses on the impact of Energy Storage Systems (ESS) in an active community, utilizing clustering analysis to identify patterns and highlighting the importance of ESS from both Aggregator and active consumer perspectives.
Active communities are emerging thanks to the necessity of creating a cleaner and safer energy system. The growing concern regarding climate change urges a solution to remove fossil fuels from the production equation. The Distributed Generation (DG) technologies are presented as a substitute, but the main resources' behavior is highly uncertain. Flexibility from the demand side is needed. In this way, the authors resort to mixed-integer linear programming optimization to schedule the active resources introduced by the Smart Grid concept: DG, Demand Response programs, and Energy Storage Systems. In this study, the last one is the focus where the impact of these technologies in an active community is analyzed and discussed. The authors performed a clustering method to identify patterns on Energy Storage System (ESS) profiles, finding the optimal number of clusters first. The results show the importance of ESS from both Aggregator and active consumer perspectives. (C) 2022 The Author(s). Published by Elsevier Ltd.
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