Balancing electric power in a microgrid via programmable agents auctions

• Autorzy: Pałka P. , Radziszewska W. , Nahorski Z.
• Języki publikacji: EN

Abstrakty

EN

The paper presents an application of an auction algorithm in a multi-agent computer system for managing the unbalanced energy in a microgrid. The main goal of the system is to control and minimize the deviations of the current energy dem and from the actual energy production, using an auction algorithm. Distributed generation is assumed in the microgrid, with renewable power sources. The energy storages and the controllable power sources improve the system operation. The differences between the actual demand and produced energy are caused by unpredictable level of electric power generation by uncontrolled sources, like wind turbines or solar panels, and/or randomness of power utilization. The system will tend to balance out these differences on-line in short time intervals (less than one minute) to follow-up varying levels of local power generation and loads.

Słowa kluczowe

EN

multi-agent system; microgrid management; shortshortterm imbalance reduction

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2012
• Tom: Vol. 41, no. 4
• Strony: 777--797
• Opis fizyczny: Bibliogr. 27 poz., il., wykr.

Twórcy

autor

Pałka P.

• Warsaw University of Technology, Institute of Control and Computation Engineering

autor

Radziszewska W.

• Systems Research Institute, Polish Academy of Sciences

autor

Nahorski Z.

• Systems Research Institute, Polish Academy of Sciences

Bibliografia

• 1. Abbey, C. and Joos, G. (2005) Energy management strategies for optimization of energy storage in wind power hybrid system. 36th IEEE Power Electronics Specialists Conference. IEEEXplore, 2066–2072.
• 2. Allweyer, T. (2009) BPMN 2.0: Introduction to the Standard for Bussiness Process Modeling. Herstellung und Verlag: Books on Demand.
• 3. Bellifemine, F.L., Caire,G. and Greenwood,D. (2007) Developing Multi-Agent Systems with JADE. Wiley.
• 4. Bernhard, B., Jorg M. and James, O. (2001) Agent uml: a formalism for specifying multiagent software systems. International Journal of Software Engineering and Knowledge Engineering 11(03), 207–230.
• 5. Borbely-Bartis, A.M. and Awerbuch, S. (2003) Small is profitable: the hidden economic benefits of making electrical resources the right size. Energy Policy 31(15), 1705–1708.
• 6. Dimeas, A. and Hatziargyriou, N. (2005) Operation of a multiagent system for microgrid control. IEEE Transactions on Power Systems 20(3), 1447–1455.
• 7. FIPA, (2012) Foundation for Intelligent Physical Agents. http://fipa.org/
• 8. Kaleta, M., Pałka, P., Toczyłowski, E., and Traczyk, T. (2009) Electronic trading on electricity markets within a multi-agent framework. Lecture Notes in Artificial Intelligence, 5796, Springer, 788-799.
• 9. Kaleta, M. and Toczyłowski, E. (2012) M3 – motivations and formal model In: Modeling Multi-commodity Trade: Information Exchange Methods. Advances in Intelligent and Soft Computing, 121, Springer, 3–19.
• 10. Kouluri, M. and Pandey, R. (2011) Intelligent agent based micro grid control. 2nd International Conference on Intelligent Agent and Multi-Agent Systems (IAMA). IEEEXplore, 62–66.
• 11. Kwak, J., Varantham, P., Mahesvaran, R., Tambe, M., Jazizadeh, F., Kavulya, G., Klein, L., Becerik-Gerber, B., Hayes, T. andWood, W. (2012) Saves: A sustanable multiagent application to conserve building energy considering occupant. Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012) IFAAMS, 1–8. Available at: http://teamcore.usc.edu/papers/ 2012/ AAMAS 12–Energy–Demo–Camera–ready.pdf
• 12. Lagorse, J., Simões, M. and Miraoui, A. (2009) A multiagent fuzzy-logic based energy management of hybrid systems. IEEE Transactions on Industry Applications 45(6), 2123–2129.
• 13. Linnenberg, T., Wior, I., Schreiber, S. and Fay, A. (2011) A marketbased multi-agent-system for decentralized power and grid control. Proceedings of 2011 IEEE 16th Conference on Emerging Technologies & Factory Automation ETFA 2011. IEEEXplore, 1–8.
• 14. Marnay, C., Venkataramanan, G. (2006) Microgrids in the evolving electricity generation and delivery infrastructure. Proc. IEEE Power Engineering Society General Meeting. IEEEXplore.
• 15. McArthur, S., Davidson, E., Catterson, V., Dimeas, A., Hatziargyriou, N., Ponci, F. and Funabashi, T.(2007a) Multi-agent systems for power engineering applications. Part I: concepts, approaches, and technical challenges. IEEE Transactions on Power Systems 22(4), 1743–1752.
• 16. McArthur, S., Davidson, E., Catterson, V., Dimeas, A., Hatziargyriou, N., Ponci, F. and Funabashi, T. (2007b) Multi-agent systems for power engineering applications Part II: technologies, standards, and tools for building multi-agent systems. IEEE Transactions on Power Systems 22(4), 1753–1759.
• 17. Nahorski, Z., Radziszewska, W., Parol, M. and Pałka, P. (2011) Intelligent power balancing systems in electric microgrids (in Polish). Rynek Energii 1(98), 59–66.
• 18. Palma-Behnke, R., Benavides, C., Aranda, E., Llanos, J. and Saez, D. (2011) Energy management system for a renewable based microgrid with a demand side management mechanism. 2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG). IEEEXplore, 1–8.
• 19. Ramchurn, S., Vytelingum,P., Rogers,A. and Jennings,N. (2012) Putting the ’smarts’ into the smart grid: a grand challenge for artifitial intelligence. Communications of ACM 55(4), 86–97.
• 20. Ricalde, L., Ordonez, E., Gamez, M. and Sanchez, E. (2011) Design of a smart grid management system with renewable energy generation. 2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG). IEEEXplore, 1–4.
• 21. Rogers, A., Ramchurn, S. and Jennings, N. (2012) Delivering the smart grid: challenges for autonomous agents and multi-agent systems research. Proceedings of the 26th AAAI Conference on Artificial Intelligence. AAAI, 2166–2172. Available at: http://www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/view/5103
• 22. Schaerf, A., Shoham, Y. and Tennenholtz, M. (1995) Adaptive load balancing: A study in multi-agent learning. Journal of Artificial Intelligence Research 2, 475–500.
• 23. Shoham, Y. (1993) Agent oriented programming. Artificial Inteligence 60(1), 51–92.
• 24. Shoham, Y. and Leyton-Brown, K. (2009) Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press.
• 25. Tsikalakis, A. and Hatziargyriou, N. (2011) Centralized control for optimizing microgrids operation. Power and Energy Society General Meeting. IEEEXplore, 1–8.
• 26. Vogt, H., Weiss, H., Spiess, P., and Karduck, A. (2010) Market-based prosumer participation in the smart grid. 4th IEEE International Conference on Digital Ecosystems and Technologies (DEST), 2010. IEEEXplore, 592–597.
• 27. Westermann, D., and John, A. (2007) Demand matching wind power generation with wide-area measurement and demand-side management. IEEE Transactions on Energy Conversion 22(1), 145–149.