AUTHORS: Suman Sutradhar, Nalin B. Dev. Choudhury, Nidul Sinha

Download as PDF

ABSTRACT: This paper addresses the modelling of transmission constraints using full AC load flow model in the context of Hydrothermal Scheduling (HTS) under deregulated environment including multiple objectives, day basis profit and emission. In practical, DC load flow or Optimal Power Flow (OPF) are used in HTS problem as being state of the art but both these techniques has severe cons like DC load flow Model being too erroneous and OPF consumes too much time especially for a deregulated system where time is in short supply. In addition to this, satisfying ramping limitation is highly complicated when using OPF to solve large transmission system. For this purpose in this research AC load flow model is adopted over DC load flow or OPF model and power distribution among the units of GENCOs (Generating Companies) is performed by unit commitment. As AC load flow model being considered, the intricacies regarding Slack bus is also needed to be handled. The concept of slack bus is born for logical representation of power system by virtually injecting the mathematical unbalances of the system model through that bus. But for a large system where the unbalances exceed slack bus boundary the overall concept tends toward impossibility. Moreover the concept lacks practicality for a problem where multiple inter-related time-intervals is involved as in case of hydrothermal scheduling. In this research the concept of slack bus is extended for more practical depiction of a HTS model in deregulated environment. As the complexity of the problem increases greatly for involving such convolution, a hybridization between Artificial Bee Colony and Grey Wolf Optimization algorithms, i.e. hybrid ABC/GWO algorithm (hABC/GWO) is proposed which merges the superior exploitation technique of GWO with highly diversified exploration technique of ABC to provide sufficient diversity in the search space of HTS in order to counter additional complexity and to enhance the speed for solving the HTS problem efficiently

KEYWORDS: Deregulation, Optimization, hybridization, AC load flow modelling, Hydro-Thermal Scheduling, Slack Bus

REFERENCES:

[1] A. J. Wood and B. F. Wollenberg, Power Generation, Operation and Control, John Wiley and Sons, 2012.

[2] S. Rebennack, B. Flach, M. Pereira and P. Pardolas, Stochastic Hydro-thermal Scheduling under CO2 Emission constraints, IEEE Transaction on Power System, Vol.27, 2012, pp. 58-68.

[3] G. G. Oliveira and S. Soares, A Second-Order Network Flow Algorithm for Hydrothermal Scheduling, IEEE Transaction on Power System, Vol.10, 1995, pp. 1635-1641.

[4] S. C. Orero and M. R. Irving, A genetic algorithm modeling framework and solution technique for short term optimal hydrothermal scheduling, IEEE Transaction on Power System, Vol.13, 1998, pp. 501-518.

[5] N. Sinha, R. Chakrabarti and P. K. Chattopadhyay, Fast evolutionary programming techniques for short-term hydrothermal scheduling, IEEE Transaction on Power System, Vol. 18, 2003, pp. 214-219.

[6] L. Lakshminarasimman and S. Subramanian, Short-term scheduling of hydrothermal power system with cascaded reservoirs by using modified differential evolution, IEE Proceedings - Generation, Transmission and Distribution, VOl.18, 2006, pp. 693-700.

[7] A. Pattanaik, M. P. Satpathy and S. C. Mishra, Dry sliding wear behavior of epoxy fly ash composite with Taguchi optimization, International Journal Engineering Science and Technology, 2015.

[8] T. G. Werner and J. F. Verstege, An evolution strategy for short-term operation planning of hydrothermal power systems, IEEE Transaction on Power System, Vol.14, 1999, pp. 1362-1368.

[9] R. Kelman, N. L. Augusto, M. V. Barrso and F. Pereira, Market power assessment and mitigation in hydrothermal systems, IEEE Transaction on Power System, Vol. 16, No. 3, 2001, pp. 354-359.

[10] A. Ahmadi, J. Aghaei, H. A. Shayanfar and A. Rabiee, “Mixed integer programming of multiobjective hydro-thermal self-scheduling,” Applied Soft Computing, Vol. 12, 2012, pp. 2137-2147.

[11] I. A. Farhat and M. E. El-Hawary, Optimization methods applied for solving the short-term hydrothermal coordination problem, Electric Power Systems Research, 2009, pp. 1308-1320.

[12] B. G. Gorenstin, N. M. Campodonic, J. P. Costa and M. V. F. Pereifa, Stochastic Optimization Of A Hydro-Thermal System Including Network Constraints, Power Industry Computer Application Conference, 1991. Conference Proceedings, pp. 127-133, 1991.

[13] P. Kundur, Power system stability and control, New York: McGraw-hill, 1994.

[14] A. Muwaffaq, Derivation of UPFC DC load flow model with examples of its use in restructured power systems, IEEE Transaction on Power Systems, Vol. 18, No. 3, 2003, pp. 1173-1180.

[15] L. S. A. Martinas, A. T. Azevedo and S. Soares, Nonlinear Medium-Term HydroThermal Scheduling With Transmission Constraints, IEEE Transaction on Power Systems, Vol. 29, No. 4, 2014, pp. 1623-1633.

[16] D. Karaboga and B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of global optimization, Vol. 39, No. 3, 2007, pp. 459-471.

[17] M. Seyedali, S. M. Mirjalili and A. Lewis, Grey wolf optimizer, Advances in Engineering Software, Vol. 69, 2014, pp. 46-61.

[18] S. Sutradhar, N. Sinha and N. B. D. Chooudhury, Grey Wolf Optimizer for Short Term Hydrothermal Scheduling Problems, IET, Michael Faraday House, 2015.

[19] R. A. Ponrajah, R, J. Witherspoon and F. D. Galiana, Systems to optimise conversion efficiencies at Ontario Hydro's hydroelectric plants, IEEE Transaction on Power System, Vol. 13, No. 3, 2002, pp. 1044-1050.

[20] A. J. Conejo, J. M. Arroyo and J. Contreras, Self-Scheduling of a Hydro Producer in a PoolBased Electricity Market, IEEE Transaction on Power System, Vol. 17, No. 4, 2002, pp. 1265- 1271.

[21] N. Amjady and A. Shirzadi, Unit commitment using a new integer coded genetic algorithm, European Transactions on Electrical Power, Vol. 19, No. 8, 2009, pp. 1161-1176.

[22] http:/ /motor.ece.iit.edu/ data/ 118_ nonsmooth.xls

[23] V. K. Jadaon, N. Gupta, K. R. Niazi and A. Swarnkar, Dynamically controlled particle swarm optimization for large‐scale nonconvex economic dispatch problems, International Transactions on Electrical Energy Systems, 2014.

[24] S. Tong, M. Kleinberg and K. Miu, A distributed slack bus model and its impact on distribution system application techniques, IEEE Circuit and System, Vol. 5, 2005, pp. 4743 – 4746.

[25] M. A. Abido, J. M. Bakhashwain, Optimal VAR dispatch using a multiobjective evolutionary algorithm, International Journal of Electrical Power & Energy Systems, Vol.27, 2005, pp.13-20.