[1] M. Shahidehpour, H. Yamin, Z. Li, Market overview in electric power systems, Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management, (2002) 1-20.
[2] L. Goel, Q. Wu, P. Wang, Reliability enhancement and nodal price volatility reduction of restructured power systems with stochastic demand side load shift, in: Power Engineering Society General Meeting, 2007. IEEE, IEEE, 2007, pp. 1-8.
[3] H.Y. Yamin, S.M. Shahidehpour, Risk and profit in self-scheduling for GenCos, IEEE Transactions on Power Systems, 19(4) (2004) 2104-2106.
[4] J. Wang, S. Kennedy, J. Kirtley, A new wholesale bidding mechanism for enhanced demand response in smart grids, in: Innovative Smart Grid Technologies (ISGT), 2010, IEEE, 2010, pp. 1-8.
[5] S.J. Rassenti, V.L. Smith, B.J. Wilson, Controlling market power and price spikes in electricity networks: Demand-side bidding, Proceedings of the National Academy of Sciences, 100(5) (2003) 2998-3003.
[6] T. Li, M. Shahidehpour, Strategic bidding of transmission-constrained GENCOs with incomplete information, IEEE Transactions on power Systems, 20(1) (2005) 437-447.
[7] C. Su, D. Kirschen, Direct participation of demand-side in a pool-based electricity market, POWER SYSTEM TECHNOLOGY-BEIJING-, 31(20) (2007) 7.
[8] R.H. Patrick, F.A. Wolak, Real-time pricing and demand side participation in restructured electricity markets, Electricity Pricing in Transition, (2002) 345-356.
[9] R.L. Earle, Demand elasticity in the California power exchange day-ahead market, The Electricity Journal, 13(8) (2000) 59-65.
[10] C.-L. Su, D. Kirschen, Quantifying the effect of demand response on electricity markets, IEEE Transactions on Power Systems, 24(3) (2009) 1199-1207.
[11] A. David, Y. Li, Effect of inter-temporal factors on the real time pricing of electricity, IEEE transactions on power systems, 8(1) (1993) 44-52.
[12] E.M. Larsen, P. Pinson, F. Leimgruber, F. Judex, Demand response evaluation and forecasting — Methods and results from the EcoGrid EU experiment, Sustainable Energy, Grids and Networks, 10 (2017) 75-83.
[13] L. Goel, Q. Wu, P. Wang, Nodal price volatility reduction and reliability enhancement of restructured power systems considering demand–price elasticity, Electric Power Systems Research, 78(10) (2008) 1655-1663.
[14] A. David, Y.-C. Lee, Dynamic tariffs: theory of utility-consumer interaction, IEEE Transactions on Power Systems, 4(3) (1989) 904-911.
[15] R. Fernández-Blanco, Y. Dvorkin, M.A. Ortega-Vazquez, Probabilistic security-constrained unit commitment with generation and transmission contingencies, IEEE Transactions on Power Systems, 32(1) (2017) 228-239.
[16] M.H. Hemmatpour, M. Mohammadian, A.-A. Gharaveisi, Simple and efficient method for steady-state voltage stability analysis of islanded microgrids with considering wind turbine generation and frequency deviation, IET Generation, Transmission & Distribution, 10(7) (2016) 1691-1702.
[17] G. Kariniotakis, I.H. Waldl, I. Marti, G. Giebel, T.S. Nielsen, J. Tambke, J. Usaola, F. Dierich, A. Bocquet, S. Virlot, Next generation forecasting tools for the optimal management of wind generation, in: Probabilistic Methods Applied to Power Systems, 2006. PMAPS 2006. International Conference on, IEEE, 2006, pp. 1-6.
[18] M. Gibescu, B. Ummels, W. Kling, Statistical wind speed interpolation for simulating aggregated wind energy production under system studies, in: Probabilistic Methods Applied to Power Systems, 2006. PMAPS 2006. International Conference on, IEEE, 2006, pp. 1-7.
[19] J. Li, F. Liu, Z. Li, S. Mei, G. He, Impacts and benefits of UPFC to wind power integration in unit commitment, Renewable Energy, 116 (2018) 570-583.
[20] N. Gong, X. Luo, D. Chen, Bi-level two-stage stochastic SCUC for ISO day-ahead scheduling considering uncertain wind power and demand response, The Journal of Engineering, 2017(13) (2017) 2549-2554.
[21] B.D.H. Kiran, M.S. Kumari, Demand response and pumped hydro storage scheduling for balancing wind power uncertainties: A probabilistic unit commitment approach, International Journal of Electrical Power & Energy Systems, 81 (2016) 114-122.
[22] S. Abedi, M. He, D. Obadina, Congestion Risk-Aware Unit Commitment with Significant Wind Power Generation, IEEE Transactions on Power Systems, (2018).
[23] P. Xiong, C. Singh, A Distributional Interpretation of Uncertainty Sets in Unit Commitment under Uncertain Wind Power, IEEE Transactions on Sustainable Energy, (2018).
[24] P. Glasserman, Monte Carlo methods in financial engineering, Springer Science & Business Media, 2013.
[25] J. Dupačová, N. Gröwe-Kuska, W. Römisch, Scenario reduction in stochastic programming, Mathematical programming, 95(3) (2003) 493-511.
[26] N. Growe-Kuska, H. Heitsch, W. Romisch, Scenario reduction and scenario tree construction for power management problems, in: Power tech conference proceedings, 2003 IEEE Bologna, IEEE, 2003, pp. 7 pp. Vol. 3.
[27] E. Zarei, M.H. Hemmatpour, M. Mohammadian, The Effects of Demand Response on Security-Constrained Unit Commitment, Scientia Iranica, (2017) -.
[28] A. Khodaei, M. Shahidehpour, S. Bahramirad, SCUC with hourly demand response considering intertemporal load characteristics, IEEE Transactions on Smart Grid, 2(3) (2011) 564-571.
[29] J. Wang, M. Shahidehpour, Z. Li, Security-constrained unit commitment with volatile wind power generation, IEEE Transactions on Power Systems, 23(3) (2008) 1319-1327.