Interoperable Coordination and Regulation Schemes in Multi-Agent based Energy Management Systems

Interoperable Coordination and Regulation Schemes in Multi-Agent based Energy Management Systems

The research objective of this project is to study and propose models for building interoperable multi-agent based energy management systems. Basing the approach on existing ontologies and platforms and on standards, the aim is to develop models integrating various market models and platforms

  • Advisors – Olivier Boissier, Gabriel Santos (GECAD), Maxime Lefrançois, Baptiste Rossi (ENGIE)
  • Contact – send application to Olivier.Boissier@emse.fr, Maxime.Lefrancois@emse.fr, baptiste.rossi @ engie.com
  • Location – Laboratoire Hubert Curien & Institut Henri Fayol, Mines Saint-Etienne
  • Team – Connected Intelligence & Computer science and Intelligent Systems Dpt
  • Keywords: Multi-Agent Systems, SemanticWeb Technology, Smart Energy Management

 

Summary

The global context of this research concerns the changes taking place in the global energy market introduced by the evolution of digital technologies. This global market is transforming into a decentralized eco-system of several local and agile energy markets where prosumers trade the energy they produce as well as the one they consume. In this context, Multi-Agent System (MAS) technologies are proving to be promising solutions [6, 8]. Since the preliminary works with the ARCHON system[5], several proposals have been done in relation to energy eco-system simulation [11], energy allocation optimisation [1, 3], energy trading management on behalf of human users (individuals or energy stakeholders), virtual plant formation[7], etc.

This research project is mainly concerned with the integration of several management units, each dedicated to individual energy management models, and their cooperation and coordination in a virtual power plant [4, 12]; or several. Beyond the definition of coordination and optimisation algorithms, a key issue in such approaches concerns the inter-operability of the various agent knowledge models. To this aim, several domain ontologies are proposed: Smart Energy Aware Systems, [9], Electricity Markets Ontology (EMO), ontology for electricity and natural gas energy markets [2, 13].

Since electricity markets are becoming extremely complex and dynamic, enlarging at regional and continental scale and integrating several energy sources, it is of first importance to go one step further in the study of interoperability. The research objective of this project is to study and propose models for building interoperable multi-agent based energy management systems. Basing the approach on existing ontologies and platforms (e.g. [10]), and on standards (http://sites.ieee.org/pes-mas/), the aim is to develop models integrating various market models and platforms addressing:

1. interaction capabilities among heterogeneous autonomous agents (agent communication languages, actions on resources);

2. coordination capabilities among the heterogeneous decision capabilities (e.g. Call for Proposals (CFP) ontology, Electricity Markets Results (EMR) ontology);

3. regulation and organisation capabilities among the heterogeneous normative and coordination structures ruling the cooperation within the various virtual power plants, energy markets, etc (e.g. [14]).

 

Expected results

Theoretical

Models and ontologies for the interoperability among Multi-Agent based Energy Management Systems at the coordination and organisational levels

 

Practical

Proof of concepts demonstrating the interoperability between an Energy Management System developed at GECAD Laboratory in Porto, an Energy Management System developed at Connected Intelligence in Saint-Etienne, and an existing platform in use at ENGIE.

 

References

[1] C. Akasiadis and G. Chalkiadakis. Decentralized large-scale electricity consumption shifting by prosumer cooperatives. In G. A. Kaminka, M. Fox, P. Bouquet, E. Hullermeier, V. Dignum, F. Dignum, and F. van Harmelen, editors, ECAI 2016 – 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands – Including Prestigious Applications of Artificial Intelli- gence (PAIS 2016), volume 285 of Frontiers in Artificial Intelligence and Applications, pages 175-183. IOS Press, 2016.

[2] P. Alexopoulos, K. Kafentzis, and C. Zoumas. Elmo: An interoperability ontology for the electricity market. In ICE-B, pages 15-20, 2009.

[3] J. Cerquides, G. Picard, and J. Rodriguez-Aguilar. Designing a marketplace for the trading and distribution of energy in the smart grid. In 14th International Confer- ence on Autonomous Agents and Multiagent Systems (AAMAS), pages 1285-1293. International Foundation for Autonomous Agents and Multiagent Systems, 2015.

[4] L. Gomes, P. Faria, H. Morais, Z. Vale, and C. Ramos. Distributed, agent-based intelligent system for demand response program simulation in smart grids. IEEE Intelligent Systems, 29:56-65, 2014.

[5] N. R. Jennings, E. Mamdani, J. M. Corera, I. Laresgoiti, F. Perriolat, P. Skarek, and L. Z. Varga. Using archon to develop real-world dai applications. 1. IEEE expert, 11(6):64-70, 1996.

[6] S. D. Ramchurn, P. Vytelingum, A. Rogers, and N. R. Jennings. Putting the’smarts’ into the smart grid: a grand challenge for artificial intelligence. Communications of the ACM, 55(4):86-97, 2012.

[7] V. Robu, R. Kota, G. Chalkiadakis, A. Rogers, and N. R. Jennings. Cooperative virtual power plant formation using scoring rules. In Proceedings of the 11th Inter- national Conference on Autonomous Agents and Multiagent Systems-Volume 3, pages 1165-1166. International Foundation for Autonomous Agents and Multiagent Systems, 2012.

[8] A. Rogers, S. D. Ramchurn, and N. R. Jennings. Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research. In J. Hoffmann and B. Selman, editors, Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, July 22-26, 2012, Toronto, Ontario, Canada. AAAI Press, 2012.

[9] G. Santos, T. Pinto, I. Praça, and Z. Vale. An interoperable approach for energy systems simulation: Electricity market participation ontologies. Energies, 9(11):878, 2016.

[10] G. J. L. d. Santos. Ontologies for the interoperability of multiagent electricity markets simulation platforms. Master’s thesis, 2015.

[11] F. Silva, B. Teixeira, T. Pinto, G. Santos, Z. Vale, and I. Praça. Generation of realistic scenarios for multi-agent simulation of electricity markets. Energy, 116:128-139, 2016.

[12] Z. Vale, H. Morais, P. Faria, and C. Ramos. A communication and resources management scheme to support the smart grid integration of multiplayers access to resources information. IFAC Proceedings Volumes, 47(3):11244-11249, 2014.

[13] K. H. Van Dam and E. J. Chappin. Coupling agent-based models of natural gas and electricity markets. In ATES 2010: 1st International Workshop on Agent Technologies for Energy Systems, Toronto, Canada, 11 May 2010. Workshop of the 9th Interna- tional Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), 10-14 May 2010, Toronto, Canada, 2010.

[14] A. Zarafin, A. Zimmermann, and O. Boissier. Integrating semantic web technologies and multi-agent systems: A semantic description of multi-agent organizations. In S. Ossowski, F. Toni, and G. A. Vouros, editors, Proceedings of the First International Conference on Agreement Technologies, AT 2012, Dubrovnik, Croatia, October 15-16, 2012, volume 918 of CEUR Workshop Proceedings, pages 296-297. CEUR-WS.org, 2012.