Smart meter aware domestic energy trading agents

The domestic energy market is changing with the increasing availability of energy micro-generating facilities. On the long run, households will have the possibility to trade energy for purchasing to and for selling from a number of different actors. We model such a futuristic scenario using software agents. In this paper we illustrate an implementation including the interfacing with a physical Smart Meter and provide initial simulation results. Given the high autonomy of the actors in the domestic market and the complex set of behaviors, the agent approach proves to be effective for both modeling and simulating purposes.

[1]  Keith B. Hall,et al.  Fair and Efficient Solutions to the Santa Fe Bar Problem , 1910 .

[2]  P. Klemperer,et al.  Supply Function Equilibria in Oligopoly under Uncertainty , 1989 .

[3]  Yves Demazeau,et al.  From Analysis to Deployment: A Multi-agent Platform Survey , 2000, ESAW.

[4]  Amory B. Lovins,et al.  Small is profitable: The Hidden economic benefits of making electrical resources the right size , 2003 .

[5]  Ali Naci Celik,et al.  Energy output estimation for small-scale wind power generators using Weibull-representative wind data , 2003 .

[6]  Pavel Vrba JAVA-Based Agent Platform Evaluation , 2003, HoloMAS.

[7]  Pawel Kaczmarek,et al.  Testing the efficiency of JADE agent platform , 2004, Third International Symposium on Parallel and Distributed Computing/Third International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Networks.

[8]  P. Crompton,et al.  Energy consumption in China: past trends and future directions , 2004 .

[9]  Declan Butler,et al.  Power to the people , 2004, Nature.

[10]  P. Bodger,et al.  Forecasting electricity consumption in New Zealand using economic and demographic variables , 2005 .

[11]  Pawel Kaczmarek,et al.  Efficiency of JADE agent platform , 2005, Sci. Program..

[12]  J. K. Kok,et al.  PowerMatcher: multiagent control in the electricity infrastructure , 2005, AAMAS '05.

[13]  C. Marnay,et al.  Microgrids in the evolving electricity generation and delivery infrastructure , 2006, 2006 IEEE Power Engineering Society General Meeting.

[14]  S. Ilarri,et al.  Comparison and Performance Evaluation of Mobile Agent Platforms , 2007, Third International Conference on Autonomic and Autonomous Systems (ICAS'07).

[15]  Ken Nagasaka,et al.  Toward Designing Value Supportive Infrastructure for Electricity Trading , 2007, The 9th IEEE International Conference on E-Commerce Technology and The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services (CEC-EEE 2007).

[16]  Paul L. Joskow,et al.  Lessons Learned From Electricity Market Liberalization , 2008 .

[17]  D. Whitehead,et al.  The El Farol Bar Problem Revisited: Reinforcement Learning in a Potential Game , 2008 .

[18]  Cameron W. Potter,et al.  Building a smarter smart grid through better renewable energy information , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[19]  Barrie Murray,et al.  Power Markets and Economics: Energy Costs, Trading, Emissions , 2009 .

[20]  Rafael Cossent,et al.  Towards a future with large penetration of distributed generation: Is the current regulation of electricity distribution ready? Regulatory recommendations under a European perspective , 2009 .

[21]  Nicola Capodieci,et al.  P2P energy exchange agent platform featuring a game theory related learning negotiation algorithm , 2011 .