Automated Trading for Smart Grids: Can It Work?
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This paper applies basic economic principles which have been developed in financial markets to a future smart grid scenario. Our method allows for autonomous bidding for electricity units to create an emerging market price for electricity. We start with replicating the popular Zero-Intelligence-Plus algorithm and setting it in a electricity supplier-consumer scenario. We identify significant weaknesses of applying this in an electricity market especially when intermittent sources of energy are present or when the supplier to consumer ratio is very small. A new algorithm (ZIP-260) is proposed which includes a measure of fairness based on minimising the deviation across all un-matched demand for a given period. This approach means that no consumer in the system is constantly experiencing an electricity supply deficit. We show and explain how market conditions can lead to collective bargaining of consumers and monopolistic behaviour of suppliers and conclude with observations on automated trading for smart grids.
KeywordsMultiagent System Smart Grid Electricity Market Demand Response Algorithmic Trading
- 1.Cliff, D.: The Flash Crash of May 6 2010: WTF? Technical report, Department of Computer Science, University of Bristol, Bristol (2010)Google Scholar
- 4.Albadi, M.H., El-Saadany, E.F.: Demand Response in Electricity Markets: An Overview. In: 2007 IEEE Power Engineering Society General Meeting, pp. 1–5 (June 2007)Google Scholar
- 5.Cliff, D.: Minimal-Intelligence Agents for Bargaining Behaviors in Market-Based Environments. Technical report, Hewlett-Packard Laboratories, Bristol (1997)Google Scholar
- 6.Ramchurn, S.D., Vytelingum, P., Rogers, A., Jennings, N.: Agent-Based Control for Decentralised Demand Side Management in the Smart Grid. In: International Conference on Autonomous Agents and Multiagent Systems AAMAS, Taiwan, pp. 2–6 (2011)Google Scholar
- 7.Mohsenian-Rad, A.H., Wong, V.W.S., Member, S., Jatskevich, J., Schober, R., Leon-garcia, A.: Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid. System 1(3), 320–331 (2010)Google Scholar
- 8.Vytelingum, P., Ramchurn, S.D., Voice, T.D., Rogers, A., Jennings, N.R.: Trading Agents for the Smart Electricity Grid. In: van der Hoek, Kaminka, Lesperance, Luck, Sen (eds.) 9th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2010), Toronto, pp. 897–904 (2010)Google Scholar
- 9.Tesfatsion, L., Judd, K.L. (eds.): Handbook of Computational Economics. North-HollandGoogle Scholar
- 11.Elexon: The Electricity Trading Arrangements: A Beginner’s Guide. Technical Report July, Elexon (2009)Google Scholar
- 12.Qian, K., Zhou, C., Li, Z., Yuan, Y.: Benefits of energy storage in power systems with high level of intermittent generation. In: 20th International Conference on Electricity Distribution. Number 0358, Prague, pp. 8–11 (2009)Google Scholar
- 13.CER: Electricity Smart Metering Customer Behaviour Trials (CBT) Findings Report. Technical report, The Commission for Energy Regulation (2011)Google Scholar
- 15.Cliff, D.: Zip60: An enhanced variant of the zip trading algorithm. In: E-Commerce Technology. In: The 3rd IEEE International Conference on the 8th IEEE International Conference on and Enterprise Computing, E-Commerce, and E-Services (June 2006)Google Scholar