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Decentralised District Multi-vector Energy Management: A Multi-agent Approach
- Source :
- IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 534), 19th Working Conference on Virtual Enterprises (PRO-VE), 19th Working Conference on Virtual Enterprises (PRO-VE), Sep 2018, Cardiff, United Kingdom. pp.551-559, ⟨10.1007/978-3-319-99127-6_47⟩, 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, IFIP Advances in Information and Communication Technology, IFIP Advances in Information and Communication Technology-Collaborative Networks of Cognitive Systems, IFIP Advances in Information and Communication Technology ISBN: 9783319991269, PRO-VE
- Publication Year :
- 2018
- Publisher :
- HAL CCSD, 2018.
-
Abstract
- International audience; Despite its many advantages, the non-controllable and intermittent nature of renewable energy sources is adding further stress to the energy networks and hence, grid operators are often forced to curtail RES generation or to limit its further penetration in the most congested areas. Smart tri-generation districts (electricity, gas, heat) can be key to mitigate these issues and increase the renewable hosting capacity of the grid, provided their feature an optimal use of their energy conversion and storage capabilities. This paper presents a district energy management approach based on Multi-Agent System (MAS) that takes into consideration the tri-energy vectors (electricity, gas, thermal). The optimization problem is solved in a distributed way based on the Alternating Direction Method of Multipliers with the objective of minimizing district costs and preliminary results show the efficiency of our approach to achieve this objective.
- Subjects :
- Mathematical optimization
Multi-vector energy management
Optimization problem
Energy management
Computer science
Renewable energy sources RES
020209 energy
02 engineering and technology
7. Clean energy
Multi-Agent Systems
Distributed optimisation
cost minimization
gas
0202 electrical engineering, electronic engineering, information engineering
[INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY]
Energy transformation
[INFO]Computer Science [cs]
electricity
business.industry
Multi-agent system
[SPI.NRJ]Engineering Sciences [physics]/Electric power
storage capabilities
Grid
Energy conversion
Renewable energy
thermal energy
[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]
Electricity
business
optimization problem
ADMM
Thermal energy
Subjects
Details
- Language :
- English
- ISBN :
- 978-3-319-99126-9
978-3-319-99127-6 - ISSN :
- 18684238 and 1868422X
- ISBNs :
- 9783319991269 and 9783319991276
- Database :
- OpenAIRE
- Journal :
- IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 534), 19th Working Conference on Virtual Enterprises (PRO-VE), 19th Working Conference on Virtual Enterprises (PRO-VE), Sep 2018, Cardiff, United Kingdom. pp.551-559, ⟨10.1007/978-3-319-99127-6_47⟩, 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, IFIP Advances in Information and Communication Technology, IFIP Advances in Information and Communication Technology-Collaborative Networks of Cognitive Systems, IFIP Advances in Information and Communication Technology ISBN: 9783319991269, PRO-VE
- Accession number :
- edsair.doi.dedup.....63509e9d995bf77af43dfa853de2cf52
- Full Text :
- https://doi.org/10.1007/978-3-319-99127-6_47⟩