July  2015, 11(3): 985-998. doi: 10.3934/jimo.2015.11.985

Electricity day-ahead markets: Computation of Nash equilibria

1. 

Faculdade de Ciências, Universidade do Porto and INESC TEC, Rua do Campo Alegre, 4169-007 Porto, Portugal, Portugal

2. 

Faculdade de Engenharia, Universidade do Porto and INESC TEC, Rua Dr. Roberto Frias, 4200 - 465 Porto, Portugal

Received  May 2012 Revised  June 2014 Published  October 2014

In a restructured electricity sector, day-ahead markets can be modeled as a game where some players - the producers - submit their proposals. To analyze the companies' behavior we have used the concept of Nash equilibrium as a solution in these multi-agent interaction problems. In this paper, we present new and crucial adaptations of two well-known mechanisms, the adjustment process and the relaxation algorithm, in order to achieve the goal of computing Nash equilibria. The advantages of these approaches are highlighted and compared with those available in the literature.
Citation: Margarida Carvalho, João Pedro Pedroso, João Saraiva. Electricity day-ahead markets: Computation of Nash equilibria. Journal of Industrial & Management Optimization, 2015, 11 (3) : 985-998. doi: 10.3934/jimo.2015.11.985
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show all references

References:
[1]

Power Systems, IEEE Transactions on, 22 (2007), 2152-2160. doi: 10.1109/TPWRS.2007.907445.  Google Scholar

[2]

Power Systems, IEEE Transactions on, 22 (2007), 1804-1818. doi: 10.1109/TPWRS.2007.907536.  Google Scholar

[3]

Power Systems, IEEE Transactions on, 25 (2010), 1978-1986. doi: 10.1109/TPWRS.2010.2049034.  Google Scholar

[4]

Power Systems, IEEE Transactions on, 21 (2006), 629-638. doi: 10.1109/TPWRS.2006.873127.  Google Scholar

[5]

In The Proceedings of the VII ALIO/EURO Workshop on Applied Combinatorial Optimization,, pages 153-156, Porto, Portugal, May 2011. Google Scholar

[6]

TOP, 9 (2001), 1-54. doi: 10.1007/BF02579062.  Google Scholar

[7]

Power Systems, IEEE Transactions on, 19 (2004), 195-206. doi: 10.1109/TPWRS.2003.820692.  Google Scholar

[8]

4OR: A Quarterly Journal of Operations Research, 5 (2007), 173-210. doi: 10.1007/s10288-007-0054-4.  Google Scholar

[9]

MIT Press, Cambridge, MA, 5th edition, 1996.  Google Scholar

[10]

Master's thesis, Faculdade de Engenharia da Universidade do Porto, Portugal, 2005. Google Scholar

[11]

Power Systems, IEEE Transactions on, 25 (2010), 722-728. doi: 10.1109/TPWRS.2009.2037153.  Google Scholar

[12]

Power Systems, IEEE Transactions on, 15 (2000), 638-645. doi: 10.1109/59.867153.  Google Scholar

[13]

Computational Economics, 5 (2006), 24644. URL http://mpra.ub.uni-muenchen.de/1119/. Google Scholar

[14]

Power Engineering Review, IEEE, 22 (2002), p55. doi: 10.1109/MPER.2002.4311811.  Google Scholar

[15]

Power Systems, IEEE Transactions on, 20 (2005), 180-188. doi: 10.1109/TPWRS.2004.840397.  Google Scholar

[16]

Power Systems, IEEE Transactions on, 26 (2011), 1744-1752. doi: 10.1109/TPWRS.2010.2098425.  Google Scholar

[17]

Electric Power Systems Research, 81 (2011), 329-339. doi: 10.1016/j.epsr.2010.09.008.  Google Scholar

[18]

FEUPedições, 2002. Google Scholar

[19]

Evolutionary Computation, IEEE Transactions on, 8 (2004), 305-315. doi: 10.1109/TEVC.2004.832862.  Google Scholar

[20]

Journal of Global Optimization, 39 (2007), 197-219. doi: 10.1007/s10898-007-9133-5.  Google Scholar

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