An Agent-based Modeling for Price-responsive Demand Simulation

Hongyan Liu, Jüri Vain

2013

Abstract

With the ongoing deployment of smart grids, price-responsive demand is playing an increasingly important role in the paradigm shifting of electricity markets. Taking a multi-agent system modeling approach, this paper presents a conceptual platform for discovering dynamic pricing solutions that reflect the varying cost of electricity in the wholesale market as well as the level of demand participation, especially regarding household customers and small and medium sized businesses. At first, an agent-based meta-model representing various concepts, relations, and structure of agents is constructed. Then a domain model can be instantiated based upon the meta-model. Finally, a simulation experiment is developed for use case demonstration and model validation. The simulation is for the supplier to obtain the profit-maximizing demand curve which has such a shape that it follows the spot price curve in inverse ratio. The result suggests that this multi-agent-based construct could contribute to 1) estimating the impacts of various time-varying tariff options on peak-period energy use through simulation, before any experimental pilots can be carried out; 2) modeling the electricity retail market evolving interactions in a systematic manner; 3) inducing innovative simulation configurations.

References

  1. Albadi, M. H. and El-Saadany, E. F., 2007. Demand Response in Electricity Markets: An Overview. In: Proceedings of IEEE PES General Meeting (GM' 07), Tampa, FL, USA, June 24-28, 2007, pp. 1-5.
  2. Bagnall, A., Smith, G., 2005. A multi-agent model of the UK market in electricity generation. IEEE Trans. on Evolutionary Computation 9 (5), 522-536.
  3. Belonogova, N., Kaipia, T., Lassila, J., Partanen, J., 2011. Demand Response: Conflict Between Distribution System Operators and Retailer. In: Proceedings of 21st International Conference on Electricity Distribution (CIRED 2011), p 1085, June 6-9, Frankfurt, Germany.
  4. Bengtsson, J., Yi, W., 2004. Timed Automata: Semantics, Algorithms and Tools. Lecture Notes on Concurrency and Petri Nets. W. Reisig and G. Rozenberg (eds.), LNCS 3098, Springer-Verlag, 2004, pp. 87-124.
  5. Bower, J., Bunn, D.W., 2000. Model-based comparisons of pool and bilateral markets for electricity. Energy Journal 21 (3), 1-29.
  6. Bower, J., Bunn, D.W., Wattendrup, C., 2001. A modelbased analysis of strategic consolidation in the german electricity industry. Energy Policy 29 (12), 987-1005.
  7. Cau, T. D. H., Anderson, E. J., 2002. A co-evolutionary approach to modelling the behaviour of participants in competitive electricity markets. IEEE Power Engineering Society Summer Meeting 3, 1534-1540.
  8. CEER Advice on the take-off of a demand response electricity market with smart meters, Ref: C11-RMF36-03 (December 2011).
  9. Ehlen, M. A., Scholand, A. J., Stamber, K. L., 2007. The effects of residential real-time pricing contracts on transco loads, pricing, and profitability: Simulations using the N-ABLE™ agent-based model. Energy Economics 29 (2), Elsevier, March 2007, pp. 211-227.
  10. Epstein, J. M., Axtell, R. L., 1996. Growing Artificial Societies: Social Science from the Bottom Up. The MIT Press.
  11. Holland, J. H.,Miller, J. H., 1991. Artificial adaptive agents in economic theory. American Economic Review 81 (2), 365-370.
  12. Hämäläinen, R. P., Mäntysaari, J., Ruusunen, J., and Pineau, P. O., 2000. Cooperative consumers in a deregulated electricity market - dynamic consumption strategies and price coordination. Energy 25 (9), Elsevier, September 2000, pp. 857-875.
  13. Koesrindartoto, D., Sun, J., Tesfatsion, L., 2005. An Agent-Based Computational Laboratory for Testing the Economic Reliability of Wholesale Power Market Desings. Proceedings of the IEEE Power Engineering Society General Meeting, vol. 3, pp. 2818-2823.
  14. Müller, M., Sensfuß, F., and Wietschel, M., 2007. Simulation of current pricing-tendencies in the German electricity market for private consumption. Energy Policy 35 (8), Elsevier, August 2007, pp.4283- 4294.
  15. Nicolaisen, J., Smith, M., Petrov, V., Tesfatsion, L., 2000. Concentration and capacity effects on electricity market power. Proc. 2000 Congress on Evolutionary Computation, vol. 2. La Jolla, USA, pp. 1041-1047.
  16. Nicolaisen, J., Petrov, V., Tesfatsion, L., 2001. Market power and efficiency in a computational electricity market with discriminatory double-auction pricing. IEEE Trans. on Evolutionary Computation 5 (5), 504- 523.
  17. Petrov, V., Sheblé, G., 2001. Building electric power auctions with improved Roth-Erev reinforced learning. Proc. of the North American Power Symposium, Texas, USA.
  18. Richter, C.W., Sheblé, G.B., 1998. Genetic algorithm evolution of utility bidding strategies for the competitive marketplace. IEEE Transactions on Power Systems 13(1) (1), 256-261.
  19. Roop, J. M., Fathelrahman, E., 2003. Modeling electricity contract choice: an agent-based approach. In: Proceedings of the ACEEE Summer Study Meeting, Rye Brook, New York.
  20. Russell, S., Norvig, P., 2003. Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, pp 46 - 54.
  21. Schuler, R. E., 2012. Planning, Markets and Investment in the Electric Supply Industry. In: Proceedings of 45th Hawaii International Conference on System Sciences (HICSS 2012), Maui, Hawaii, USA, January 4-7, 2012, pp. 1923-1930.
  22. Sensfuß, F.; Ragwitz, M.; Genoese, M.; Möst, D., 2007. Agent-based simulation of electricity markets: a literature review. Working paper sustainability and innovation No. S5/2007
  23. Sun, J., Tesfatsion, L., 2007. Dynamic testing of wholesale power market designs: an open-source agent-based framework. Computational Economics 30 (3), 291-327.
  24. Weidlich, A. and Veit, D., 2008. A critical survey of agent-based wholesale electricity market models. Energy Economics (30), Elsevier, 2008, pp. 1728-1759.
  25. Wooldridge, M., Jennings, N. R., 1995. Intelligent agents: theory and practice. Knowledge Engineering Review 10 (2), 115-152.
  26. Zhou, Z., Zhao, F., and Wang, J. 2011 Agent-Based Electricity Market Simulation With Demand Response From Commercial Buildings. IEEE Transactions on Smart Grid, vol.2, no.4, pp.580-588.
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Paper Citation


in Harvard Style

Liu H. and Vain J. (2013). An Agent-based Modeling for Price-responsive Demand Simulation . In Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8565-59-4, pages 436-443. DOI: 10.5220/0004417504360443


in Bibtex Style

@conference{iceis13,
author={Hongyan Liu and Jüri Vain},
title={An Agent-based Modeling for Price-responsive Demand Simulation},
booktitle={Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2013},
pages={436-443},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004417504360443},
isbn={978-989-8565-59-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - An Agent-based Modeling for Price-responsive Demand Simulation
SN - 978-989-8565-59-4
AU - Liu H.
AU - Vain J.
PY - 2013
SP - 436
EP - 443
DO - 10.5220/0004417504360443