Optimal power flow model incorporating wind, solar, and bundled solar-thermal power in the restructured Indian power system

References

• Abido, M. A. (2009). Multi objective particle swarm for environmental/economic dispatch problem. Electric Power Systems Research 79 (2009):1105–1113. doi:10.1016/j.epsr.2009.02.005
   Google Scholar
• Bhattacharya, A., and P. K. Roy. 2012. Solution of multi-objective optimal power flow using gravitational search algorithm. IET Generation, Transmission & Distribution 6 (8):751–63. doi:10.1049/iet-gtd.2011.0593.
   Web of Science ®Google Scholar
• Bird, L., J. Cochran, and X. Wang. (2014). Technical Report on Wind and Solar Energy Curtailment: Experience and Practices in the United States, National Renewable Energy Laboratory (NREL). http://www.nrel.gov/docs/fy14osti/60983.pdf ( accessed October 28, 2016).
   Google Scholar
• Castronuovo, E. D., J. Martínez-Crespo, and J. Usaola. 2007. Optimal controllability of wind generators in a delegated dispatch. Electric Power Systems Research 77 (10):1442–48. doi:10.1016/j.epsr.2006.10.015.
   Web of Science ®Google Scholar
• CERC. (2015a). Central electricity regulatory commission (deviation settlement mechanism and related matters) (second amendment) regulations. http://www.cercind.gov.in/2015/regulation/Noti7.pdf. Accessed October 28, 2016.
   Google Scholar
• CERC. (2015b). Framework on forecasting, scheduling and imbalance handling for variable renewable energy sources (wind and solar): Statement of reasons issued by CERC. http://www.cercind.gov.in/2015/regulation/SOR7.pdf. ( accessed October 28, 2016).
   Google Scholar
• Chen, C. L., T. Y. Lee, and R. M. Jan. 2006. Optimal wind-thermal coordination dispatch in isolated power systems with large integration of wind capacity. Energy Conversion and Management 47:3456–72. doi:10.1016/j.enconman.2005.12.016.
   Web of Science ®Google Scholar
• Costa, A. L., and A. S. Costa. 2007. Energy and ancillary service dispatch through dynamic optimal power flow. Electric Power System Research 77 (8):1047–55. doi:10.1016/j.epsr.2006.09.003.
   Web of Science ®Google Scholar
• Dobson, Y., S. Greene, R. Rajaraman, and C. L. DeMarco. 2001. Electric Power Transfer Capability: Concepts, Applications, Sensitivity and Uncertainty, 01–34. PSERC Publication. http://www.pserc.cornell.edu/tcc/tutorial/TCC_Tutorial.pdf. (Accessed October 28, 2016).
   Google Scholar
• ElDesouky, A. A. 2013. Security and stochastic economic dispatch of power system including wind and solar resources with environmental consideration. International Journal of Renewable Energy Research 3 (4):951–58.
   Google Scholar
• GOI, MNRE. (2015). Guidelines for Selection of 3000 MW Grid – Connected Solar PV Power Projects under Batch-II, “State Specific Bundling Scheme”-MNRE, GOI. http://ireeed.gov.in/policydetails?id=444# ( accessed October 28, 2016).
   Google Scholar
• Hetzer, J., D. C. Yu, and K. Bhattarai. 2008. An economic dispatch model incorporating wind power. IEEE Transactions on Energy Conversion 23 (2):603–11. doi:10.1109/TEC.2007.914171.
   Web of Science ®Google Scholar
• Jabr, R. A., and B. C. Pal. 2009. Intermittent wind generation in optimal power flow dispa tching. IET Generation, Transmission & Distribution 3 (1):66–74. doi:10.1049/iet-gtd:20080273.
   Web of Science ®Google Scholar
• Kamalinia, S., and M. Shahidehpour. 2010. Generation expansion planning in wind-thermal power systems. IET Generation, Transmission & Distribution 4:940–51. doi:10.1049/iet-gtd.2009.0695.
   Web of Science ®Google Scholar
• Li, F., and B. Kuri. (2005). Generation scheduling in a system with wind power. IEEE/PES Transmission and Distribution Conference and Exhibition, Asia and Pacific, Dalian, China, August 14–18. doi:10.1109/TDC.2005.1547157.
   Google Scholar
• Luque, A., and S. Hegedus. 2003. Handbook of Photovoltaic Science and Engineering. Hoboken, NJ: Wiley.
   Google Scholar
• Makarov, Y., J. Blatchford, H. Alarian, K. DeMarse, M. O’Hara, M. Scholz, S. Jercich, J. Vidov, E. Leuze, R. Abernathy, D. Hawkins, and J. Zack. (2011). Incorporation of Wind Power Resources into the California Energy Market. CAISO Corporation, USA. http://www.caiso.com/documents/incorporation-windpowerresourcesintocaliforniaenergymarket.pdf ( accessed October 28, 2016).
   Google Scholar
• Miranda, V., and P. S. Hang. 2005. Economic dispatch model with fuzzy wind constraints and attitudes of dispatchers. IEEE Transactions on Power Systems 20 (4):2143–45. doi:10.1109/TPWRS.2005.857930.
   Web of Science ®Google Scholar
• Niknam, T., M. R. Narimani, J. Aghaei, S. Tabatabaei, and M. Nayeripour. 2011. Modified honey bee mating optimisation to solve dynamic optimal power flow considering generator constraints. IET Generation, Transmission & Distribution 5 (10):989–1002. doi:10.1049/iet-gtd.2011.0055.
   Web of Science ®Google Scholar
• Oren, S., D. Callaway, J. Mathieu, A. Papavasiliou, T. Mount, M. Zhang, and R. Thomas (2012). White paper on “Renewable Energy Integration and the Impact of Carbon Regulation on the Electric Grid,” Cornell University, George Gross, 12–13. PSERC Publication https://pserc.wisc.edu/documents/publications/papers/fgwhitepapers/Oren_PSERC_Future_Grid_TA3_May_2012.pdf. (Accessed October 28, 2016).
   Google Scholar
• Riffonneau, Y. S., B. F. Barruel, and S. Ploix. 2011. Optimal power flow management for grid connected PV systems with batteries. IEEE Transactions on Sustainable Energy 2 (3):309–20. doi:10.1109/TSTE.2011.2114901.
   Web of Science ®Google Scholar
• Senthilkumar, V., and M. R. Mohan. 2009. Optimal short term hydro thermal scheduling using decomposition approach and GA based OPF. Journal of Electrical Systems 5 (2):P8.
   Google Scholar
• Shi, L., C. Wang, L. Yao, Y. Ni, and M. Bazargan. 2012. Optimal power flow solution incorporating wind power. IEEE Systems Journal 6:233–41. doi:10.1109/JSYST.2011.2162896.
   Web of Science ®Google Scholar
• Skoplaki, E., and J. A. Palyvos. 2009. On the temperature dependence of photovoltaic module electrical performance: a review of efficiency/power correlations. Solar Energy 83 (5):614–24. doi:10.1016/j.solener.2008.10.008.
   Web of Science ®Google Scholar
• Stott, B., and O. Alsaç. (2012). White paper on optimal power flow- basic requirement for real life problems and solutions http://www.ieee.hr/_download/repository/Stott-Alsac-OPF-White-Paper.pdf ( accessed October 28, 2016).
   Google Scholar
• TANGEDCO. (2015). Tamil Nadu Generation and Distribution Corporation, Statistics at a glance 2014-15. http://www.tangedco.gov.in/stat11-12.php ( accessed November 04, 2016).
   Google Scholar
• Tuohy, A., and M. O’Malley. 2009. Impact of Pumped Storage on Power Systems with Increasing Wind Penetration, 26–30. Calgary, AB, Canada: IEEE Power & Energy Society General Meeting.
   Google Scholar
• Wang, C., Z. Lu, and Y. Qiao. 2013. A consideration of the wind power benefits in day-ahead scheduling of wind-coal intensive power systems. IEEE Transactions on Power Systems 28 (1):236–45. doi:10.1109/TPWRS.2012.2205280.
   Web of Science ®Google Scholar
• Wu, J., A. Botterud, A. Mills, Z. Zhou, B.-M. Hodge, and M. Heaney. 2015. Integrating solar PV (photovoltaics) in utility system operations: Analytical framework and Arizona case study. Energy 85 (1):1–9. doi:10.1016/j.energy.2015.02.043.
   Web of Science ®Google Scholar
• Yang, X. S. 2012. Flower pollination algorithm for global optimization, unconventional computation and natural computation. In Lecture Notes in Computer Science, ed. J. Durand-Lose, and N. Jonoska, vol. 7445, 240–49. Berlin, Heidelberg: Springer.
   Google Scholar
• Zhang, H., F. Gao, J. Wu, K. Liu, and X. Liu. 2012. Optimal bidding strategies for wind power producers in the day-ahead electricity market. Energies 5 (11):4804–23. doi:10.3390/en5114804.
   Web of Science ®Google Scholar