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Agent-based competitive simulation: exploring future retail energy markets

Published:02 August 2010Publication History

ABSTRACT

Future sustainable energy systems will need efficient, clean, low-cost, renewable energy sources, as well as market structures that motivate sustainable behaviors on the part of households and businesses. "Smart grid" components can help consumers manage their consumption only if pricing policies are in place that motivate consumers to install and use these new tools in ways that maximize utilization of renewable energy sources while minimizing dependence on non-renewable energy. Serious market breakdowns, such as the California energy crisis in 2000, have made policy makers wary of setting up new retail energy markets. We present the design of an open, competitive simulation approach that will produce robust research results on the structure and operation of retail power markets as well as on automating market interaction by means of competitively tested and bench-marked electronic agents. These results will yield policy guidance that can significantly reduce the risk of instituting competitive energy markets at the retail level, thereby applying economic motivation to the problem of adjusting our energy production and consumption patterns to the requirements of a sustainable future.

References

  1. Ahlert, K.-H., and Block, C. Assessing the impact of price forecast errors on the economics of distributed storage systems. In 43rd Hawaii International Conference on System Science (HICSS-43) (Hawaii, USA, 2010). Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Amin, M., and Wollenberg, B. Toward a smart grid: Power delivery for the 21st century. IEEE Power & Energy Magazine 3, 5 (2005), 34--41.Google ScholarGoogle Scholar
  3. Blaabjerg, F., Teodorescu, R., Liserre, M., and Timbus, A. Overview of control and grid synchronization for distributed power generation systems. IEEE Transactions on Industrial Electronics 53, 5 (2006), 1398--1409.Google ScholarGoogle ScholarCross RefCross Ref
  4. Block, C., Bomarius, F., Bretschneider, P., Briegel, F., Burger, N., Fey, B., Frey, H., Hartmann, J., Kern, C., Plail, B., Praehauser, G., Schetters, L., Schoepf, F., Schumann, D., Schwammberger, F., Terzidis, O., Thiemann, R., van Dinther, C., von Sengbusch, K., Weidlich, A., and Weinhardt, C. Internet der Energie - IKT für Energiemärkte der Zukunft. Bdi-drucksache nr. 418, Bundesverband der Deutschen Industrie e.V. (BDI), 12 2008.Google ScholarGoogle Scholar
  5. Block, C., Collins, J., Ketter, W., and Weinhardt, C. A multi-agent energy trading competition. Tech. Rep. ERS-2009-054-LIS, RSM Erasmus University, Rotterdam, The Netherlands, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  6. Borenstein, S., Bushnell, J. B., and Wolak, F. A. Measuring market inefficiencies in California's restructured wholesale electricity market. The American Economic Review 92, 5 (2002), 1376--1405.Google ScholarGoogle ScholarCross RefCross Ref
  7. Collins, J., Arunachalam, R., Sadeh, N., Ericsson, J., Finne, N., and Janson, S. The supply chain management game for the 2006 trading agent competition. Tech. Rep. CMU-ISRI-05-132, Carnegie Mellon University, Pittsburgh, PA, November 2005.Google ScholarGoogle Scholar
  8. Technology options for the near and long term. Tech. rep., U. S. Department of Energy Climate Change Technology Program, September 2006.Google ScholarGoogle Scholar
  9. EEX. Introduction to exchange trading at EEX on Xetra and Eurex. Tech. rep., European Energy Exchange AG, January 2008.Google ScholarGoogle Scholar
  10. Hammerstrom, D. J., Ambrosio, R., Brous, J., Carlon, T. A., Chassin, D. P., DeSteese, J. G., Guttromson, R. T., Horst, G. R., Järvegren, O. M., Kajfasz, R., Katipamula, S., Kiesling, L., Le, N. T., Michie, P., Oliver, T. V., Pratt, R. G., Thompson, S., and Yao, M. Pacific northwest gridwise testbed demonstration projects: The Olympic Peninsula project. Final report, Pacific Northwest National Laboratory, Richland, Washington 99352, October 2007.Google ScholarGoogle Scholar
  11. Hatziargyriou, N., Asano, H., Iravani, R., and Marnay, C. Microgrids: An overview of ongoing research, development, and demonstration projects. Berkeley Lab Publications LBNL-62937, Lawrence Berkeley National Laboratory, July 2007.Google ScholarGoogle Scholar
  12. Hirsch, C., Hillemacher, L., Block, C., Schuller, A., and Moest, D. Simulation studies within the MEREGIO field experiment. IT - Information Technology forthcoming (2010).Google ScholarGoogle Scholar
  13. Jonker, C. M., and Treur, J. An agent architecture for multi-attribute negotiation. In International joint conference on artificial intelligence (2001), pp. 1195--1201. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Jordan, P. R., Cassell, B., Callender, L. F., and Wellman, M. P. The ad auctions game for the 2009 trading agent competition. Tech. rep., University of Michigan, Department of Computer Science and Engineering, 2009.Google ScholarGoogle Scholar
  15. Jordan, P. R., Kiekintveld, C., and Wellman, M. P. Empirical game-theoretic analysis of the TAC supply chain game. In Proc. of the Sixth Int'l Conf. on Autonomous Agents and Multi-Agent Systems (May 2007), pp. 1188--1195. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Joskow, P., and Kahn, E. A quantitative analysis of pricing behavior in California's wholesale electricity market during summer 2000. NBER Working Paper Series 8157, National Bureau of Economic Research, 2001.Google ScholarGoogle Scholar
  17. Ketter, W., Collins, J., Gini, M., Gupta, A., and Schrater, P. Detecting and forecasting economic regimes in multi-agent automated exchanges. Decision Support Systems 47, 4 (2009), 307--318. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Kiekintveld, C., Miller, J., Jordan, P. R., Callender, L. F., and Wellman, M. P. Forecasting market prices in a supply chain game. Electronic Commerce Research and Applications 8, 2 (2009), 63--77. Special Section: Supply Chain Trading Agent Research. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Meeus, L., and Belmans, R. Is the prevailing wholesale market design in Europe and North America comparable? In Power Engineering Society General Meeting, 2007. IEEE (2007), pp. 1--5.Google ScholarGoogle ScholarCross RefCross Ref
  20. Miller, J., Page, S., and LeBaron, B. Complex adaptive systems: An introduction to computational models of social life. Princeton University Press Princeton and Oxford, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Niu, J., Cai, K., Parsons, S., Gerding, E., and McBurney, P. Characterizing effective auction mechanisms: Insights from the 2007 TAC market design competition. In Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems-Volume 2 (2008), International Foundation for Autonomous Agents and Multiagent Systems, pp. 1079--1086. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. North, M., Conzelmann, G., Koritarov, V., Macal, C., Thimmapuram, P., and Veselka, T. E-laboratories: agent-based modeling of electricity markets. In 2002 American Power Conference (2002), pp. 1--19.Google ScholarGoogle Scholar
  23. Pardoe, D., and Stone, P. Adapting in agent-based markets: A study from tac scm. In Proc. of the Sixth Int'l Conf. on Autonomous Agents and Multi-Agent Systems (May 2007), pp. 677--679. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Sanchez, I. Short-term prediction of wind energy production. International Journal of Forecasting 22, 1 (January 2006), 43--56.Google ScholarGoogle ScholarCross RefCross Ref
  25. Siddiqui, A., Bartholomew, E., and Marnay, C. Empirical analysis of the spot market implications of price-elastic demand. Berkeley Lab Publications LBNL-56141, Lawrence Berkeley National Laboratory, July 2004.Google ScholarGoogle Scholar
  26. Sodomka, E., Collins, J., and Gini, M. Efficient statistical methods for evaluating trading agent performance. In Proc. of the Twenty-Second National Conference on Artificial Intelligence (2007), pp. 770--775. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Spees, K., and Lave, L. Impacts of responsive load in PJM: Load shifting and real time pricing. The Energy Journal 29, 2 (2008), 101--122.Google ScholarGoogle ScholarCross RefCross Ref
  28. Sueyoshi, T., and Tadiparthi, G. An agent-based decision support system for wholesale electricity market. Decision Support Systems 44, 2 (2008), 425--446. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Sun, J., and Tesfatsion, L. Dynamic testing of wholesale power market designs: An open-source agent-based framework. Computational Economics 30, 3 (2007), 291--327. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Tesfatsion, L. Agent-based computational economics: Growing economies from the bottom up. Artificial Life 8, 1 (2002), 55--82. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Varga, L. Z., Jennings, N. R., and Cockburn, D. Integrating intelligent systems into a cooperating community for electricity distribution management. Int. Journal of Expert Systems with Applications 7, 4 (1994), 563--579.Google ScholarGoogle ScholarCross RefCross Ref
  32. Veit, D., Weidlich, A., and Krafft, J. A. An agent-based analysis of the german electricity market with transmission capacity constraints. Energy Policy 37, 10 (2009), 4132--4144.Google ScholarGoogle ScholarCross RefCross Ref
  33. von Dollen, D. Report to NIST on the smart grid interoperability standards roadmap. Tech. Rep. SB1341-09-CN-0031, Electric Power Research Institute (EPRI), June 2009.Google ScholarGoogle Scholar
  34. Wellman, M. P., Greenwald, A., and Stone, P. Autonomous Bidding Agents. MIT Press, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  35. Zhou, Z., Chan, W., and Chow, J. Agent-based simulation of electricity markets: a survey of tools. Artificial Intelligence Review 28, 4 (2007), 305--342. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

    cover image ACM Other conferences
    ICEC '10: Proceedings of the 12th International Conference on Electronic Commerce: Roadmap for the Future of Electronic Business
    August 2010
    215 pages
    ISBN:9781450314275
    DOI:10.1145/2389376

    Copyright © 2010 ACM

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    New York, NY, United States

    Publication History

    • Published: 2 August 2010

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