Distributed Schemes, Innovative Solutions for Smart Grids: P2P, Multi-Agent Systems & Blockchain †
Abstract
:1. Introduction
2. Presentations
2.1. DRIvE: “Multi-Agent Technology for District’s Demand Response”
- Privacy: Prosumer private data (preferences, devices) stay in prosumer premises.
- Scalability: via a divide and conquer approach the original global optimization problem is decomposed into smaller subproblems which can be solved much easier an in parallel by different agents. This will allow true cost-effective mass-market (100’s millions of heterogeneous assets) engagement and therefore support the real next generation smart grids.
- Autonomy: Each prosumer/asset schedules its own power profile based on its economical and comfort preferences.
2.2. SHAR-Q: “Sharing Storage Capacity in Virtual Neighborhoods”
2.3. DELTA: “Permissioned Blockchains and Virtual Nodes for Reinforcing Trust between Aggregators and Prosumers in DR Programs”
2.4. FHP: “The FHP Dynamic Coalition Manager Concept: A Distributed Optimization Scheme for Optimal Flex Activation Decision”
Author Contributions
Funding
Conflicts of Interest
References
- DRIvE website. Available online: https://www.h2020-drive.eu (accessed on 30 June 2019).
- Kraning, M.; Chu, E.; Lavaei, J.; Boyd, S. Dynamic Network Energy Management via Proximal Message Passing. Found. Trends Optim. 2013, 1, 70–122. [Google Scholar]
- Vinyals, M.; Velay, M.; Sisinni, M. A multi-agent system for energy trading between prosumers. In Proceedings of the 14th International Conference on Distributed Computing and Artificial Intelligence, Porto, Portugal, 21–23 June 2017. [Google Scholar]
- Klaimi, J.; Vinyals, M. Decentralised District Multi-vector Energy Management: A Multi-agent Approach. In Proceedings of the 19th IFIP WG 5.5 Working Conference on Virtual Enterprises, Cardiff, UK, 17–19 September 2018. [Google Scholar]
- Boyd, S.; Parikh, N.; Chu, E.; Peleato, B. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. Found. Trends Optim. 2010, 3, 1–122. [Google Scholar]
- The Universal Smart Energy Framework (USEF). Available online: https://www.usef.energy (accessed on 30 June 2019).
- SHAR-Q Website. Available online: http://www.sharqproject.eu/ (accessed on 30 June 2019).
- DELTA Website. Available online: https://www.delta-h2020.eu (accessed on 30 June 2019).
- FHP Website. Available online: http://fhp-h2020.eu (accessed on 30 June 2019).
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Loureiro, T.; Espeche, J.; Vinyals, M.; Stecchi, U.; Tzovaras, D.; Ioannidis, D.; Geysen, D. Distributed Schemes, Innovative Solutions for Smart Grids: P2P, Multi-Agent Systems & Blockchain. Proceedings 2019, 20, 5. https://doi.org/10.3390/proceedings2019020005
Loureiro T, Espeche J, Vinyals M, Stecchi U, Tzovaras D, Ioannidis D, Geysen D. Distributed Schemes, Innovative Solutions for Smart Grids: P2P, Multi-Agent Systems & Blockchain. Proceedings. 2019; 20(1):5. https://doi.org/10.3390/proceedings2019020005
Chicago/Turabian StyleLoureiro, Tatiana, Juan Espeche, Meritxell Vinyals, Ugo Stecchi, Dimitrios Tzovaras, Dimosthenis Ioannidis, and Davy Geysen. 2019. "Distributed Schemes, Innovative Solutions for Smart Grids: P2P, Multi-Agent Systems & Blockchain" Proceedings 20, no. 1: 5. https://doi.org/10.3390/proceedings2019020005