مدیریت بهینه انرژی ریزشبکه‌ها در بازار خرده‌فروشی بر پایه روش تئوری بازی غیر همکارانه با در نظر گرفتن عدم قطعیت

[1]     M. Shahidehpour, H. Yamin, and Z. Li, Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management, Wiley-IEEE Press, vol. 9. 2002.

[2]     L. Tesfatsion, Agent-based Computational Economics: A constructive approach to economic theory, Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics, p. 55, 2005.

[3]     D. Srinivasan, K.T. Chen, C. Wu, and C. L. Ah, “Evolving buyer’s bidding strategies using game-theoretic co-evolutionary algorithm,” International Conference on Intelligent Systems Applications to Power Systems, ISAP, 2007.

[4]     D. Srinivasan, and L.T. Trung, “Co-evolutionary bidding strategies for buyers in electricity power markets,” IEEE Congress of Evolutionary Computation, CEC2011, pp. 2519–2526, 2011.

[5]     E. Bompard, Y.C. Ma, R. Napoli, G. Gross, and T. Guler, “Comparative analysis of game theory models for assessing the performances of network constrained electricity markets,” IET Generation, Transmission & Distribution, vol. 4. p. 386, 2010.

[6]     H.Y. Wu, and S. L. Hu, “A coalition structure generation algorithm based on partition cardinality structure,” Proceedings of IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS2010, vol. 3, pp. 75–78, 2010.

[7]     S. Ketchpel, “Forming coalitions in the face of uncertain rewards,” Proceedings of the Twelfth National Conference on Artificial Intelligence, vol. 1, pp. 414–419, 1994.

[8]     M. Klusch, and A. Gerber, “Dynamic coalition formation among rational agents,” IEEE Intell. Syst. their Appl., vol. 17, pp. 42–47, 2002.

[9]     S. Beer, “A formal model for agent-based coalition formation in electricity markets,” Innovative Smart Grid Technologies Europe (ISGT EUROPE), 2013 4th IEEE/PES, pp. 1–5, 2013.

[10]  M. Marzband, M. Ghadimi, A. Sumper, and J.L. Domínguez-García, “Experimental validation of a real-time energy management system using multi-period gravitational search algorithm for microgrids in islanded mode,” Appl. Energy, vol. 128, pp. 164–174, 2014.

[11]  M. Marzband, A. Sumper, J.L. Domínguez-García, and R. Gumara-Ferret, “Experimental validation of a real time energy management system for microgrids in islanded mode using a local day-ahead electricity market and MINLP,” Energy Convers. Manag., vol. 76, pp. 314–322, 2013.

[12]  M. Marzband, A. Sumper, A. Ruiz-álvarez, J.L. Domínguez-García, and B. Tomoiagâ, “Experimental evaluation of a real time energy management system for stand-alone microgrids in day-ahead markets,” Appl. Energy, vol. 106, pp. 365–376, 2013.

[13]  W. Su, and A. Q. Huang, “A game theoretic framework for a next-generation retail electricity market with high penetration of distributed residential electricity suppliers,” Appl. Energy, vol. 119, pp. 341–350, 2014.

[14]  National Household Travel Survey, [Online]. Available at:  http://nhts.ornl.gov/.

[15]  A.R. Daniel, and A.A. Chen, “Stochastic simulation and forecasting of hourly average wind speed sequences in Jamaica,” Solar Energy, vol. 46. pp. 1–11, 1991.

[16]  B.S. Borowy, and Z.M. Salameh, “Optimum photovoltaic array size for a hybrid wind/PV system,” IEEE Trans. Energy Convers., vol. 9, pp. 482–488, 1994.

[17]  G. Tina, S. Gagliano, and S. Raiti, “Hybrid solar/wind power system probabilistic modelling for long-term performance assessment,” Sol. Energy, vol. 80, pp. 578–588, 2006.

[18]  S. Berridge, and J. Krawczyk, “Relaxation algorithm in finding nash equilibria.” Society for Computational Economics, Computing in Economics and Finance, http://ideas.repec.org/p/sce/scecf7/159.html, 1997.

[19]  A. Soroudi, R. Caire, N. Hadjsaid, and M. Ehsan, “Probabilistic dynamic multi-objective model for renewable and non-renewable distributed generation planning,” IET Generation, Transmission & Distribution, vol. 5, no. 11. p. 1173, 2011.

[20]  B. Alizadeh, and S. Jadid, “Uncertainty handling in power system expansion planning under a robust multi-objective framework,” IET Generation, Transmission & Distribution, vol. 8, no. 12, pp. 2012–2026, 2014.

[21]  B. Mohammadi-Ivatloo, H. Zareipour, N. Amjady, and M. Ehsan, “Application of information-gap decision theory to risk-constrained self-scheduling of GenCos,” IEEE Trans. Power Syst., vol. 28, no. 2, pp. 1093–1102, 2013.

[22]  A. Soroudi, M. Ehsan, R. Caire, and N. Hadjsaid, “Possibilistic evaluation of distributed generations impacts on distribution networks,” IEEE Trans. Power Syst., vol. 26, no. 4, pp. 2293–2301, 2011.

[23]  A. Soroudi, “Possibilistic-scenario model for DG impact assessment on distribution networks in an uncertain environment,” IEEE Trans. Power Syst., vol. 27, no. 3, pp. 1283–1293, 2012.

[24]  H. Yu, C.Y. Chung, and K.P. Wong, “Robust transmission network expansion planning method with Taguchi’s orthogonal array testing,” IEEE Trans. Power Syst., vol. 26, no. 3, pp. 1573–1580, 2011.

[25]  R. Chatthaworn, and S. Chaitusaney, “Transmission network expansion planning considering renewable energy target with Taguchi’s orthogonal array testing,” IEEE Trans. Electr. Electron. Eng., vol. 9, no. 6, pp. 588–599, 2014.