Ahmed Tiguercha, Ahmed Amine Ladjici, and Mohamed Boudour


  1. [1] A.A. Ladjici, A. Tiguercha, and M. Boudour, Nash equilibriumin a two-settlement electricity market using competitive CEAs,International Journal of Electrical Power & Energy Systems,57, 2014, 148–155.
  2. [2] A. Tiguercha, A. Ladjici, and M. Boudour, Competitive co-evolutionary approach to stochastic modeling in deregulatedelectricity market, Energy Conference (ENERGYCON), 2014IEEE International, Dubrovnik, Croatia, IEEE, 2014, 514–519.
  3. [3] A. Tiguercha, A. Ladjici, and M. Boudour, Suppliers’ optimal biding strategies in day-ahead electricity market usingcompetitive coevolutionary algorithms, 3rd International Conference on Systems and Control, Algiers, Algeria, IEEE, 2013,821–826.
  4. [4] Q.P. Zheng, J. Wang, and A.L. Liu, Stochastic optimizationfor unit commitment–A review, IEEE Transactions on PowerSystems, 30(4), 2015, 1913–1924.
  5. [5] T. Pinto, G. Santos, Z.A. Vale, I. Pra¸ca, F. Lopes, andH. Algarvio, Realistic multi-agent simulation of competitiveelectricity markets, in DEXA Workshops, Munich, Germany,2014, 109–113.
  6. [6] M. Jenabi, S.M. T.F. Ghomi, and Y. Smeers, Bi-level gameapproaches for coordination of generation and transmissionexpansion planning within a market environment, IEEE Trans-actions on Power Systems, 28(3), 2013, 2639–2650.
  7. [7] S.P. Karthikeyan, I.J. Raglend, and D.P. Kothari, A review onmarket power in deregulated electricity market, InternationalJournal of Electrical Power & Energy Systems, 48, 2013,139–147.
  8. [8] M.R. Hesamzadeh and D.R. Biggar, Computation of extremal-Nash equilibria in a wholesale power market using a single-stage MILP, IEEE Transactions on Power Systems, 27(3),2012, 1706–1707.
  9. [9] E.V. BECk, On optimal bidding strategy modeling in thecontext of a liberalized electricity market, Ph.D. Dissertation,´Ecole Polytechnique F´ed´erale de Lausanne, Switzerland, 2007.
  10. [10] A. Ladjici and M. Boudour, Nash–Cournot equilibrium of aderegulated electricity market using competitive coevolutionaryalgorithms, Electric Power Systems Research, 81(4), 2011,958–966.
  11. [11] M.M. Tripathi, A. Pandey, A. Verma, K. Upadhyay, andD. Chandra, Nash commitment for energy trading in liberalizedpower market: A state of art review, International Review ofElectrical Engineering (IREE), 11(4), 2016, 381–390.
  12. [12] M.R. Salehizadeh, A. Rahimi-Kian, and K. Hausken, A leader–follower game on congestion management in power systems(Cham, Switzerland: Springer International Publishing, 2015),ch. 4.
  13. [13] S. Wogrin, J. Barqu´ın, and E. Centeno, Capacity expansionequilibria in liberalized electricity markets: An EPEC approach, IEEE Transactions on Power Systems, 28(2), 2013,1531–1539.
  14. [14] L. Guo, G.-H. Lin, D. Zhang, and D. Zhu, An MPEC reformulation of an EPEC model for electricity markets, OperationsResearch Letters, 43(3), 2015, 262–267.
  15. [15] B.F. Hobbs, C.B. Metzler, and J. Pang, Strategic gaminganalysis for electric power systems: An MPEC approach, IEEETransactions on Power Systems, 15(2), 2000, 638–645.
  16. [16] J. Yao, I. Adler, and S.S. Oren, Modeling and computing two-settlement oligopolistic equilibrium in a congested electricitynetwork, Operations Research, 56(1), 2008, 34–47.
  17. [17] L. Tesfatsion and K.L. Judd, Handbook of computational economics: Agent-based computational economics. (Amsterdam:Elsevier, 2006), vol. 2.
  18. [18] H. Li and L. Tesfatsion, Development of open source softwarefor power market research: The AMES test bed, The Journalof Energy Markets, 2(2), 2009, 111.
  19. [19] A. Minoia, D. Ernst, M. Dicorato, M. Trovato, and M. Ilic,Reference transmission network: A game theory approach,IEEE Transactions on Power Systems, 21(1), 2006, 249–259.
  20. [20] V. Nannen, Evolutionary agent-based policy analysis in dynamic environments. V. Nannen, Vrije Universiteit, Amster-dam, 2009, no. 2009.
  21. [21] M. Shafie-khah and J.P. Catal˜ao, A stochastic multi-layeragent-based model to study electricity market participantsbehavior, IEEE Transactions on Power Systems, 30(2), 2015,867–881.
  22. [22] S. Cincotti and G. Gallo, The Genoa artificial power-exchange,International Conference on Agents and Artificial Intelligence,Berlin, Germany, Springer, 2012, 348–363.
  23. [23] A. Tiguercha, A. Ladjici, M. Boudour, and M. Hazli, Dayahead a electricity market analysis through a neuroevolutionalgorithm, 2016 IEEE International Energy Conference (EN-ERGYCON), Leuven, Belguim, IEEE, 2016, 1–6.
  24. [24] T.L. Turocy, Texas A&M university, Bernhard von Stengel,London School of Economics? Game Theory? CDAM ResearchReport (October 2001), 2001.
  25. [25] R. Ferrero, S. Shahidehpour, and V. Ramesh, Transactionanalysis in deregulated power systems using game theory, IEEETransactions on Power Systems, 12(3), 1997, 1340–1347.
  26. [26] A.G. Pereira and A. Petry, Data assimilation using neuroevolution of augmenting topologies, The 2012 International JointConference on Neural Networks (IJCNN), Brisbane, Australia,IEEE, 2012, 1–6.
  27. [27] R.P. Wiegand, An analysis of cooperative coevolutionary algorithms, Ph.D. Dissertation, CiteSeerX, 2003.
  28. [28] A. Ladjici, M. Boudour, and A. Tiguercha, Day-ahead electricity market equilibrium calculation using competitive coevolution approach, JES Special Issue, 1, 2009, 67–72.

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