Abstract
Complex, intelligent, distributed systems in dynamic environments, such as the power grid need to be designed to adapt autonomously. Self-management, in particular of large scale adaptive systems such as the power grid, is necessarily distributed. Agent and peer-to-peer based decentralized self-management can change the future of energy markets in which the power grid plays a core role.
Assuming that both consumers and providers of energy are autonomous systems, represented by software agents or peers capable of self-management, virtual organizations of systems can emerge and adapt when necessary. Communication structures between systems, e.g., hierarchical or clustered organizations, can emerge, organizations between and within which systems can choose to cooperate and coordinate their actions, or compete. Overlay structures (as defined within p2p research) define such adaptive communication structures, multi-agent research provides interaction patterns. Global goals are achieved by local management on the basis of local goals and knowledge. The appropriate delegation of managerial responsibility determines the control structure. Aggregate information differs depending on the position of a system in an organization, the aggregation mechanisms and policies.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
James, G., Peng, W., Deng, K.: Managing Household Wind-energy Generation. IEEE Intelligent Systems 23, 9–12 (2008)
Ogston, E., Zeman, A., Prokopenko, M., James, G.: Clustering Distributed Energy Resources for Large-Scale Demand Management. In: Proceedings of the 1st Int. Conf. Self-Adaptive and Self- Organizing Systems (SASO 2007), pp. 97–108. IEEE (2007)
Varga, L.Z., Jennings, N.R., Cockburn, D.: Integrating Intelligent Systems in to a Cooperating Community for Electricity Distribution Management. International Journal of Expert Systems with Applications 7, 563–579 (1994)
Jennings, N.R.: An agent-based approach for building complex software systems. Commun. ACM 44, 35–41 (2001)
Luis, M.: Complex system modeling: Using metaphors from nature in simulation and scientific methods. BITS: Computer and Communications News, Computing, Information and Communications Divisions (1999)
Theocharopoulou, C., Partsakoulakis, I., Vouros, G.A., Stergiou, K.: Overlay networks for task allocation and coordination in dynamic large-scale networks of cooperative agents. In: AAMAS 2007: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1–8. ACM (2007)
A., J., Davidsson, P., Carlsson, B.: Coordination models for dynamic resource allocation. In: Coordination Models and Languages, pp. 182–197 (2000)
Mazza, P.: The smart energy network: Electrical power for the 21st century. Climate Solutions (2002)
Pournaras, E., Warnier, M., Brazier, F.M.T.: A distributed agent-based approach to stabilization of global resource utilization. In: The International Conference on Complex, Intelligent and Software Intensive Systems (CISIS 2009). IEEE (2009)
Hammerstrom, D.: Part II. Grid FriendlyTM Appliance Project. PNNL 17079, Pacific Northwestern National Laboratory (2002)
James, G., Cohen, D., Dodier, R., Platt, G., Palmer, D.: A deployed multi-agent framework for distributed energy applications. In: 5th International Joint Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2006), Hakodate, Japan (2006)
Overeinder, B.J., Brazier, F.M.T.: Scalable Middleware Environment for Agent-Based Internet Applications. In: Dongarra, J., Madsen, K., Waśniewski, J. (eds.) PARA 2004. LNCS, vol. 3732, pp. 675–679. Springer, Heidelberg (2006)
Pournaras, E., Warnier, M., Brazier, F.M.T.: Adaptive Agent-based Self-organization for Robust Hierarchical Topologies. In: Proceedings of the First International Conference on Adaptive and Intelligent Systems (ICAIS 2009). IEEE (2009) (to appear)
Li, R., Wang, P.: Pattern Learning and Decision Making in a Photovoltaic System. In: Li, X., Kirley, M., Zhang, M., Green, D., Ciesielski, V., Abbass, H.A., Michalewicz, Z., Hendtlass, T., Deb, K., Tan, K.C., Branke, J., Shi, Y. (eds.) SEAL 2008. LNCS, vol. 5361, pp. 483–492. Springer, Heidelberg (2008)
Abu-Sharkh, S., Arnold, R., Kohler, J., Li, R., Markvart, T., Ross, J., Steemers, K., Wilson, P., Yao, R.: Can Microgrids Make a Major Contribution to UK Energy Supply? Renewable and Sustainable Energy Reviews 2, 78–127 (2006)
Hatziargyriou, N., Asano, H., Iravani, R., Marnay, C.: Microgrids. IEEE Power and Energy Magazine 4, 78–94 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Brazier, F., Ogston, E., Warnier, M. (2012). The Future of Energy Markets and the Challenge of Decentralized Self-management. In: Beneventano, D., Despotovic, Z., Guerra, F., Joseph, S., Moro, G., de Pinninck, A.P. (eds) Agents and Peer-to-Peer Computing. AP2PC AP2PC 2009 2008. Lecture Notes in Computer Science(), vol 6573. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31809-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-31809-2_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31808-5
Online ISBN: 978-3-642-31809-2
eBook Packages: Computer ScienceComputer Science (R0)