Example Application of an Energy Management of Energy Resources in Industrial Facilities With Renewables
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
The increasing number of Distributed Energy Resources, energy storages and sector-coupling devices requires mechanisms to manage the local energy utilization. Hereby the dependency on fossil resources can be highly reduced. For this, it is essential that energy resources of whole industrial facilities are analysed and adapted. Therefore, the goal for the presented investigation of Industrial Facilities is, to use local available renewables in an optimal way, to reduce the external energy consumption and increase the self-consumption. Physical models, multiple polynomial regression and time series analysis are utilized. The special feature of this investigation is the holistic approach that includes analyses of the energy demand and the local energy generation, profiles of the entire system and parameter fitting. Moreover, the developed methods are independent of the observed local energy system and can be applied in a general way.
Details
Original language | English |
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Title of host publication | IEEE EUROCON 2023 - 20th International Conference on Smart Technologies |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 275-279 |
Number of pages | 5 |
ISBN (electronic) | 978-1-6654-6397-3 |
ISBN (print) | 978-1-6654-6398-0 |
Publication status | Published - 2023 |
Peer-reviewed | Yes |
Publication series
Series | International Conference on Smart Technologies (EUROCON) |
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Conference
Title | 20th International Conference on Smart Technologies, EUROCON 2023 |
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Duration | 6 - 8 July 2023 |
City | Torino |
Country | Italy |
External IDs
ORCID | /0000-0001-8439-7786/work/142244190 |
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Keywords
Sustainable Development Goals
ASJC Scopus subject areas
Keywords
- energy management, industrial facilities, multiple polynomial regression, renewables, time series analysis