Multi-agent Coordination for Market Environments
The financial crisis has deflated oil prices, prolonging the attractiveness of fossil fuel combustion as a method of energy generation. However, mankind faces a future of a hot, flat, and crowded world , making a critical transformation away from the use of fossil fuels imperative. After years of research and experimentation, a number of Renewable Energy Sources (RES) have become technically available as alternatives. Yet a pivotal task which still needs to be carried out is that of adapting the existing electricity infrastructure – still a very efficient energy delivery facility – to allow it to incorporate emerging RES openly and equally. To stimulate the widespread adoption of RES, which would result in the evolution to next generation infrastructures for electricity, incentives should include economic and political measures rather than only technology. In this chapter we summarize the properties of different RES and introduce the ‘microgrid’, a grid architecture allowing high RES penetration. We also analyze the prevailing electricity market structure and describe existing economic incentives for RES accommodation. Most importantly, we elaborate on the multi-agent model of electricity infrastructures based on the microgrid and its coordination mechanism within the market environment.
KeywordsMultiagent System Electricity Market Market Environment Resource Agent Independent System Operator
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- 1.Foundation of Intelligent Physical Agents. URL:http://www.fipa.org/.
- 2.T. L. Friedman. Hot, Flat, and Crowded. Farrar, Straus & Giroux, 2008.Google Scholar
- 3.N. D. Hatziargyriou, A. Dimeas, A. G. Tsikalakis, J. A. Pecas Lopes, G. Kariniotakis, and J. Oyarzabal. Management of microgrids in market environment. In Proceedings of the International Conference on Future Power Systems, Amsterdam, The Netherlands, November 2005.Google Scholar
- 4.A. Henney, J. Bower, and D. Newberry. An independent review of NETA, November 2002.Google Scholar
- 5.International Energy Agency IEA. Distributed generation in liberalized electricity markets, 2002.Google Scholar
- 6.I. G. Kamphuis, M. Hommelberg, C. J. Warmer, F. J. Kuijper, and J. K. Kok. Software agents for matching of power supply and demand: A field test with a real-time, automated imbalance reduction system. In Proceedings of the International Conference on Future Power Systems, Amsterdam, The Netherlands, November 2005.Google Scholar
- 7.J. K. Kok, C. J. Warmer, and I. G. Kamphuis. PowerMatcher: Multiagent control in the electricity infrastructure. In Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems, Utrecht, The Netherlands, 2005.Google Scholar
- 8.R. H. Lasseter and P. Paigi. Microgrid: A conceptual solution. In Proceedings of the 35th Annual IEEE Power Electronics Specialists Conference, Aachen, Germany, 2004.Google Scholar
- 9.Office of Gas and Electricity Markets. URL: http://www.ofgem.gov.uk/.
- 10.Ofgem. The review of the first year of neta, July 2002.Google Scholar
- 11.Reticular Systems Inc. Using intelligent agents to implement an electronic auction for buying and selling electric power. URL: http://www.aesc-inc.com/download/epri.pdf, August 1999.
- 12.T. W. Sandholm. Distributed rational decision making. In Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. The MIT Press, Cambridge, Massachusetts, 2001.Google Scholar
- 13.A. Tsikalakis and N. Hatziargyriou. Economic scheduling functions of a microgrid participating in energy markets. In Proceedings of the DG CIGRE Symposium, Athens, Greece, April 2005.Google Scholar
- 14.M. J. Wooldridge. An Introduction to Multiagent Systems. Wiley, Reading, Massachusetts, 2002.Google Scholar