Short Term Optimal Hydro-Thermal Scheduling of the Transmission System Equipped with Pumped Storage in the Competitive Environment

  • Mohammadreza Daneshvar Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
  • Behnam Mohammadi-ivatloo Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
  • Somayeh Asadi Department of Architectural Engineering, Pennsylvania State University, 104 Engineering Unit A, University Park, PA 16802, USA.
  • Sadjad Galvani Department of Power Engineering, Faculty of Electrical and Computer Engineering, Urmia University, Iran.
Keywords: Short Term Optimal Scheduling, Wind-Hydro-Thermal Power Plants, Clean Energy Production, Competitive Energy Market, Transmission System, Uncertainty Modeling

Abstract

The considerable development of the electricity market subjects in recent years has provided a complex and more competitive environment for the participants. Each participant in this environment adopts a special strategy to maximize its profit or minimize its energy costs considering the significant constraints. In this paper, a short term optimal scheduling of thermal units, hydropower units, wind turbines, and pumped storage units has been proposed based on the energy market guidelines. The main objective of this research is to minimize the thermal energy production costs considering the uncertainty parameters along with the maximum utilization of clean energy production in the system. In order to evaluate the research goals, IEEE 5-bus standard test system is selected as the case study, which is equipped with both conventional and clean energy resources. In addition, probabilistic behaviors related to energy demand and wind production have been considered. Results proved the effectiveness of this model in minimizing the energy cost of thermal units.

References

[1] J. Qu, W. Shi, K. Luo, C. Feng, and J. Mou, "Day-ahead Generation Scheduling Method for New Energy and Hydro Power System," in 2018 International Conference on Power System Technology (POWERCON), 2018, pp. 1899-1902: IEEE.
[2] Q. Zhang, M. Wangg, X. Wang, and S. Tian, "Mid-long term optimal dispatching method of power system with large-scale wind-photovoltaic-hydro power generation," in Energy Internet and Energy System Integration (EI2), 2017 IEEE Conference on, 2017, pp. 1-6: IEEE.
[3] Q. Wang, X. Luo, N. Gong, and H. Ma, "Day-Ahead Optimal Dispatching of Wind-Solar-Hydro-Thermal Combined Power System with Pumped-Storage Hydropower Integration," in 2018 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), 2018, pp. 430-434: IEEE.
[4] Z. Han, T. Cheng, Y. Zhou, and P. Zhang, "Multi-objective optimal scheduling for hydro-thermal-wind power system," in TENCON 2015-2015 IEEE Region 10 Conference, 2015, pp. 1-5: IEEE.
[5] Y. Zhang, J. Le, X. Liao, F. Zheng, K. Liu, and X. An, "Multi-objective hydro-thermal-wind coordination scheduling integrated with large-scale electric vehicles using IMOPSO," Renewable energy, vol. 128, pp. 91-107, 2018.
[6] R. S. Patwal and N. Narang, "Crisscross PSO algorithm for multi-objective generation scheduling of pumped storage hydrothermal system incorporating solar units," Energy Conversion and Management, vol. 169, pp. 238-254, 2018.
[7] C. Li, W. Wang, and D. Chen, "Multi-objective complementary scheduling of hydro-thermal-RE power system via a multi-objective hybrid grey wolf optimizer," Energy, vol. 171, pp. 241-255, 2019.
[8] W. Zhang, R. Li, S. Song, C. Yin, and Y. Li, "The influences of an improved pumped-wind-hydro-thermal optimization strategy in power system and research on different optimal proportion in different large regional grid," in Control And Decision Conference (CCDC), 2017 29th Chinese, 2017, pp. 6932-6936: IEEE.
[9] J. Jian, S. Pan, and L. Yang, "Solution for short-term hydrothermal scheduling with a logarithmic size mixed-integer linear programming formulation," Energy, vol. 171, pp. 770-784, 2019.
[10] A. Ihsan, M. Jeppesen, and M. J. Brear, "Impact of demand response on the optimal, techno-economic performance of a hybrid, renewable energy power plant," Applied Energy, vol. 238, pp. 972-984, 2019.
[11] H. Chen, J. Wang, and Y. Zhang, "Economic dispatch of hydro-thermal power system with large-scale wind power penetration," in Power and Energy Engineering Conference (APPEEC), 2012 Asia-Pacific, 2012, pp. 1-4: IEEE.
[12] B. Zhou, G. Geng, and Q. Jiang, "Hydro-thermal-wind coordination in day-ahead unit commitment," IEEE Transactions on Power Systems, vol. 31, no. 6, pp. 4626-4637, 2016.
[13] N. Shi, S. Zhou, X. Su, R. Yang, and X. Zhu, "Unit commitment and multi-objective optimal dispatch model for wind-hydro-thermal power system with pumped storage," in Power Electronics and Motion Control Conference (IPEMC-ECCE Asia), 2016 IEEE 8th International, 2016, pp. 1489-1495: IEEE.
[14] A. Anantharaman, S. Sharan, K. Naveen, and M. Selvan, "Hydro-Thermal-Wind Coordination for Short Term Unit Commitment Using Lambda-Gamma Iteration and Particle Swarm Optimization," in 2017 14th IEEE India Council International Conference (INDICON), 2017, pp. 1-6: IEEE.
[15] S. Rebennack, B. Flach, M. V. Pereira, and P. M. Pardalos, "Stochastic Hydro-Thermal Scheduling Under ${\rm CO} _ {2} $ Emissions Constraints," IEEE Transactions on Power Systems, vol. 27, no. 1, pp. 58-68, 2012.
[16] M. Daneshvar, M. Pesaran, and B. Mohammadi-ivatloo, "Transactive energy integration in future smart rural network electrification," Journal of Cleaner Production, vol. 190, pp. 645-654, 2018.
[17] M. D. McKay, R. J. Beckman, and W. J. Conover, "Comparison of three methods for selecting values of input variables in the analysis of output from a computer code," Technometrics, vol. 21, no. 2, pp. 239-245, 1979.
[18] K. Bruninx, E. Delarue, and W. D’haeseleer, "A practical approach on scenario generation & reduction algorithms based on probability distance measures–the case of wind power forecast errors," WP EN2014-15, 2014.
[19] W. L. de Oliveira, C. Sagastizábal, D. D. J. Penna, M. E. P. Maceira, and J. M. Damázio, "Optimal scenario tree reduction for stochastic streamflows in power generation planning problems," Optimisation Methods & Software, vol. 25, no. 6, pp. 917-936, 2010.
[20] A. Jain, R. Balasubramanian, S. Tripathy, and Y. Kawazoe, "Topological observability analysis using heuristic rule based expert system," in Power Engineering Society General Meeting, 2006. IEEE, 2006, p. 6 pp.: IEEE.
[21] J. M. Morales, A. J. Conejo, H. Madsen, P. Pinson, and M. Zugno, Integrating renewables in electricity markets: operational problems. Springer Science & Business Media, 2013.
[22] Y. Wang, J. Zhou, L. Mo, R. Zhang, and Y. Zhang, "Short-term hydrothermal generation scheduling using differential real-coded quantum-inspired evolutionary algorithm," Energy, vol. 44, no. 1, pp. 657-671, 2012.
[23] M. R. Bussieck and A. Drud, "SBB: A new solver for mixed integer nonlinear programming," Talk, OR, 2001.
[24] I. E. Grossmann, J. Viswanathan, A. Vecchietti, R. Raman, and E. Kalvelagen, "GAMS/DICOPT: A discrete continuous optimization package," GAMS Corporation Inc, vol. 37, p. 55, 2002.
[25] (2019, JAN 29). “GAMS Home Page.” [Online]. Available: https://www.gams.com/
Published
2020-03-01
How to Cite
Daneshvar, M., Mohammadi-ivatloo, B., Asadi, S., & Galvani, S. (2020). Short Term Optimal Hydro-Thermal Scheduling of the Transmission System Equipped with Pumped Storage in the Competitive Environment. Majlesi Journal of Electrical Engineering, 14(1), 77-84. Retrieved from http://mjee.org/index/index.php/ee/article/view/3305
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Articles