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A real-time energy management platform for multi-vector district energy systems

Ahmad, Muhammad Waseem, Reynolds, Jonathan, Hippolyte, Jean-Laurent, Rezgui, Yacine, Descamps, Michael Nikhil, Merckx, Christian, van Dessel, Jasper and Lessinnes, Mathieu 2018. A real-time energy management platform for multi-vector district energy systems. Presented at: 19th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2018, Cardiff, UK, 17-19 September 2018. Published in: Camarinha-Matos, Luis M., Afsarmanesh, Hamideh and Rezgui, Yacine eds. Collaborative Networks of Cognitive Systems: 19th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2018, Cardiff, UK, September 17-19, 2018, Proceedings Editors (view affiliations). IFIP Advances in Information and Communication Technology Cham: Springer Verlag (Germany), pp. 560-568. 10.1007/978-3-319-99127-6_48

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Abstract

Management of increasingly complex, multi-vector, district energy systems, operated by separate stakeholders, including prosumers, is a vital challenge to overcome in a fragmented energy landscape. The complex value chain involved forms a cognitive virtual network with the shared objective to reduce energy consumption, greenhouse gas emissions and maximise human comfort. This paper will aim to illustrate the PENTAGON platform for integrated management of key stakeholder data to produce automatic, holistic and pre-emptive decisions that ensure near-optimal management of a district energy system. The PENTAGON platform architecture consists of five key modules including Smart Connector to interface with existing District Energy Management Systems (DEMS), a time series database, a prediction module, a multi-vector optimisation module and a module ensuring the electric grid stability. Integration of these distinct modules is achieved through an underpinning, shared, semantic description of the district components, sensors and scenarios. The ultimate goal of the described platform is to achieve a step-change from static, reactive, rule-based systems to an intelligent, adaptive, and pre-emptive control architecture that makes new decisions based on perceived and predicted conditions.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Springer Verlag (Germany)
ISBN: 9783319991276
ISSN: 1868-4238
Last Modified: 27 Aug 2019 13:57
URI: http://orca.cf.ac.uk/id/eprint/115174

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