Optimal scheduling of hydrothermal system considering variable nature of water transportation delay

Document Type : Research Note

Authors

1 Department of Electrical Engineering, National Institute of Technology, Agartala, Tripura, Pin-799046, India

2 Department of Electrical Engineering, National Institute of Technology, Durgapur, West Bengal, Pin-713209, India

Abstract

This paper presents solution of short-term hydrothermal scheduling problem using an algorithm called Grasshopper optimization. The objective of hydrothermal scheduling is to reduce the total cost of generation by optimizing the output of power generation of different thermal and hydro plants for a certain time interval. A non-linear relationship between hydropower generation, net head and rate of water discharge is considered here. The complicated head-sensitive water-to-power conversion and piecewise output limit is also considered. To investigate the performance of this new technique, two test systems have been considered. The simulation results are compared with some well-known optimization methods such as Genetic algorithm, Biogeography based optimization, hybrid Differential evolution with Biogeography based optimization and Grey wolf optimizer algorithm. The simulation results show the superiority of this technique as compared to other well-known optimization methods.

Keywords

References

References:
[1] Engles, L., Larson, R.E., Peschon, J. et al. “Dynamic programming applied to hydro and thermal generation scheduling”, In: IEEE tutorial course text, 76CH1107-2-PWR, IEEE, NewYork (1976).
[2] Wood, J. and Wollenberg, B.F.  “Power generation, operation, and control”, John Wiley and Sons, 2nd edition. Wiley New York (1984).
[3] Saha, T.N. and Khaparde, S.A.“An application of a direct method to the optimal scheduling of hydrothermal systems”, IEEE Trans PAS, 97 (3), pp. 977–983 (1978).
[4] Rashid AHA. and Nor, K.M.“An efficient method for optimal scheduling of fixed head hydro and thermal plants”, IEEE Trans Power Syst,6 (2), pp. 632–636 (1991).
[5] Nilsson,O.and Sjelvgren, D. “Mixed-integer programming applied to short-term planning of a hydrothermal system”, IEEE Trans Power Syst 11 (1), pp. 281–286 (1996).
[6] Wong, K.P. and Wong, Y.W. “Short-term hydrothermal scheduling Part I: simulated annealing approach”, Proc Inst Electr Eng Gen Transm Distrib.,141 (5), pp. 497–501(1994).
[7] Wong, K.P. and Wong, Y.W.“Short-term hydrothermal scheduling Part II: parallel simulated annealing approach”, Proc Inst Electr Eng Gen Transm Distrib, 141 (5), pp. 502–506 (1994).
[8] Chen PO-H. and Chang, H.C.“Genetic aided scheduling of hydraulically coupled plants in hydrothermal coordination”, IEEE Trans Power Syst, 11 (2), pp. 975–981 (1996).
[9] Orero, S.O. and Irving, M.R.“A genetic algorithm modeling framework and solution technique for short term optimal hydrothermal scheduling”, IEEE Trans PWRS, 13 (2), pp. 501–518 (1998).
[10] Gil, E., Bustos, J. and Rudnick, H.“Short-term hydro thermal generation scheduling model using a genetic algorithm”, IEEE Trans PWRS, 18 (4), pp. 1256–1264 (2003).
[11] Kumar, S. and Naresh R. “Efficient real coded genetic algorithm to solve the non-convex hydrothermal scheduling problem”, Int J Elec Power and Energ Sys, 29 (10), pp. 738-747 (2007).
[12] Hota, P.K. Chakrabarti, R. and Chattopadhyay, P.K. “Short-term hydrothermal scheduling through evolutionary programming technique”, Electr Power Syst Res, 52, pp. 189–196 (1999). 
[13] Sinha, N., Chakrabarti, R. and Chattopadhyay, P.K. “Fast evolutionary programming techniques for short-term hydrothermal scheduling” Electric Power Syst Res, 66, pp. 97–103 (2003).
[14] Mandal K.K., Basu M. and Chakraborty N. “Particle swarm optimization technique based short-term hydrothermal scheduling”, Appl Soft comput, 8, pp. 1392-1399 (2008).
[15] Wang, Y., Zhou, J., Zhou, C. et al. “ An improved self-adaptive PSO technique for short-term hydrothermal scheduling”, Expert Sys with Appl, 39, pp. 2288-2295 (2012).
[16] Cavazzini, G., Pavesi, G. and Ardizzon, G. “A novel two-swarm based PSO search strategy for optimal short-term hydro-thermal generation scheduling”, Energ Conv and Mang, 164, pp. 460-481 (2018).
[17] Mandal, K.K. and Chakraborty, N. “Differential evolution technique-based short-term economic generation scheduling of hydrothermal systems”, Electr Power Syst Res, 78 (11) pp. 1972–1979 (2008).
[18] Sivansubramani, S. and swarup K.S. “Hybrid DE-SQP algorithm for non-convex short term hydrothermal scheduling problem”, Energ Conv and Mang, 52, pp. 757-761 (2011).
[19] Basu M. “Improved differential evolution for short-term hydrothermal scheduling”, Elec Power & Energ, 58, pp. 91-100 (2014).
[20] Basu M. “Hopfield neural networks for optimal scheduling of fixed head hydrothermal power systems”, Electr Power Syst Res, 64 (1), pp 11–15 (2003).
[21] Roy, P.K. “Teaching learning based optimization for short term hydrothermal scheduling problem considering valve point effect and prohibited discharge constraint”, Int J Elec Power and Energ Sys, 53, pp. 10–19 (2013).
[22] Roy, P.K., Paul, C. and Sultana, S. “Oppositional teaching learning based optimization approach for combined heat and power dispatch”, Int J Elec Power and Energ Sys, 57, pp. 392–403 (2014).
[23] Nguyen, T.T., Vo, D.N. and Ruong, A.V. “Cuckoo search algorithm for short-term hydrothermal scheduling”, Appl Energy, 132, pp. 276–287 (2014).
[24] Zhou, J., Liao, X., Ouyang, S. et al. “Multi-objective artificial bee colony algorithm for short-term scheduling of hydrothermal system”, Electr Power Energy Syst, 55, pp 542-553 (2014).
[25] Das, S. and Bhattacharya, A. “Symbiotic organisms search algorithm for short-term hydrothermal scheduling”, Ain Shams Engg J, 9 (4), pp. 499-516 (2016).
[26] Roy S., Rani S., Bhattacharjee K. et al. “Chemical reaction based optimization implemented to solve short-term hydrothermal generation scheduling problems”, 3rd International Conference on Electrical Energy Systems (ICEES 2016), SSN college of Engineering, Tamilnadu, India, pp. 79-84 (2016)
[27] Sutradhar S., Dev Choudhury N.B. and Sinha N. “Grey wolf optimizer for short-term hydrothermal scheduling problems”, Michael Faraday IET International Sumit 2015, Kolkata, India (2015).
[28] Bhattacharjee K., Bhattacharya A. and nee Dey S.H. “Real coded chemical reaction based optimization for short-term hydrothermal scheduling”, Appl. Soft Comput., 24, pp. 962-976 (2014).
[29] Roy P.K., Pradhan M. and Paul T. “Krill herd algorithm applied to short-term hydrothermal scheduling problem”, Ain Shams Engg. Jr.9,  pp. 31-43 (2018).
[30] Swain R.K., Barisal A. K., Hota P.K. et al. “ Short-term hydrothermal scheduling using clonal selection algorithm”, Int. J. Elec. Power & Energ. Sys., 33 (3) pp. 647-656 (2011).
[31] Balachander T., Jeyanthy P.A. and Devaraj D. “ Short term hydrothermal scheduling using flower pollination algorithm”, 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), Srivilliputhur, India (2017).
[32] Das S., Bhattacharya A. and Das S. “Solution of short-term hydrothermal scheduling using sine cosine algorithm”, Soft Comput., 22 (19), pp. 6409-6427 (2018).
[33] Dubey H.M., Pandit M. and Panigrahi B.K. “Ant lion optimization for short-term wind integrated hydrothermal power generation scheduling”, Int. J. Elec. Power & Energ. Sys, 83, pp. 158-174 (2016).
 [34] Das S., Bhattacharya A., Chakraborty A.K. et al. “Fixed head short-term hydrothermal scheduling using whale optimization algorithm considering the uncertainty of solar power”, 2017 Ninth International Conference on Advanced Computing (ICoAC), Chennai, India (2017).
[35] Nguyen, T.T. and Vo, D.N. “Modified cuckoo search algorithm for short-term hydrothermal scheduling”, Electr Power Energy Syst, 65 pp. 271–281 (2015).
[36] Das, S., Bhattacharya, A. and Chakraborty A.K. “Solution of short-term hydrothermal scheduling problem using quasi-reflected symbiotic organisms search algorithm considering multi-fuel cost characteristics of thermal generator”, Arab J Sci and Engg, 43, pp. 2931-2960 (2018).
[37] Das, S., Bhattacharya, A. and Chakraborty, A.K. “Quasi-reflected ions motion optimization algorithm for short term hydrothermal scheduling”, Neural Comput & Applic, 29, pp. 123-149 (2018).
[38] Narang, N. “Short-term hydrothermal generation scheduling using improved predator influenced civilized swarm optimization technique”, Appl Soft Comput, 58, pp. 207-224 (2017).
[39] Fang, N., Zhou, J., Zhang, R. et al. “ A hybrid of real coded genetic algorithm and artificial fish swarm algorithm for short-term optimal hydrothermal scheduling”, Int J Elec Power and Energ Sys, 62, pp. 617-629 (2014).
[40] Bhattacharjee, K., Bhattacharya, A. and nee Dey S.H. “Oppositional real coded chemical reaction based optimization to solve short-term hydrothermal scheduling problems”, Int J Elec Power and Energ Sys, 63, pp. 145-157 (2014).
[41] Zhang, J., Lin, S. and Qin, W. “A modified chaotic differential evolution algorithm for short-term optimal hydrothermal scheduling”, Int J Elec Power and Energ Sys, 65, pp. 159-168 (2015).
[42] Rasoul, Z.A. and Mohammadi-Ivatloo B. “Short-term hydrothermal generation scheduling by a modified dynamic neighborhood learning based particle swarm optimization”, Int J Elec Power and Energ Sys, 67, pp. 350-367 (2015).
[43] Roy, P.K. “Hybrid chemical reaction optimization approach for combined economic emission short-term hydrothermal scheduling”, Elec Power Comp and Sys, 42 (15), pp. 1647-1660 (2014).
[44] Nadakuditi, G. Sharma, V. and Naresh, R. “Application of non-dominated sorting gravitational search algorithm with disruption operator for stochastic short-term hydrothermal scheduling”, IET Gener. Transm. Distrib, 10 (4), pp. 862-872 (2016).
[45] Goutham, N.K., Sharma, V. and Naresh R. “Hybridized Gravitational search algorithm for short-term hydrothermal scheduling”, IETE Jr of Res, 62 (4), pp. 468-478 (2016).
[46] Feng, Z.K., Niu, W.J., Zhou, Z. et al. “Scheduling of short-term hydrothermal energy system by parallel multi-objective differential evolution”, Appl Soft Comput, 61, pp. 58-71 (2017).
[47] Zhang, J., Tang, Q., Chen, Y. et al. “A hybrid particle swarm optimization with small population size to solve the optimal short-term hydro-thermal unit commitment problem”, Energy, 109, pp. 765-780 (2016).
[48] Basu, M. “Quasi-oppositional group search optimization for hydrothermal power system”, Int J of Elec Power and Energ Sys, 81, pp. 324-335 (2016).
[49] Feng Z.-K.m, Niu W.-J., Zhou J.-Z. et al. “Parallel multi-objective genetic algorithm for short-term economic environmental hydrothermal scheduling”, Energies, 10 pp. 1-22 (2017).
[50] Heris M.N., Babaei A.F., Ivatloo B.M. et al. “Improved harmony search algorithm for the solution of non-linear non-convex short-term hydrothermal scheduling”, Energy, 151, pp. 226-237 (2018).
[51] Nguyen T.T., Vo D.N. and Dinh B.H. “An effectively adaptive selective cuckoo search algorithm for solving three complicated short-term hydrothermal scheduling problems”, Energy, 155, pp. 930-956 (2018).
[52] Wu Y., Wu Y. and Liu X. “Couple-based particle swarm optimization for short-term hydrothermal scheduling”, Appl. Soft Comput., 74, pp. 440-450 (2019).
[53]Y. H.K., Liu L., Su L. et al. “ Short-term hydropower generation scheduling using an improved cloud adaptive quantum-inspired binary social spider optimization algorithm”, Water Resources management, 33 (7), pp. 2357-2379 (2019).
[54] Gosh, S., Kaur M., Bhullar S. et al. “Hybrid ABC-BAT for solving short-term hydrothermal scheduling problems”, Energies 12,  pp. 1-15 (2019).
[55] Saremi, S., Mirjalili, S. and Lewsis, A. “Grasshopper optimization algorithm: Theory and application”, Advan in Engg Soft, 105, pp. 30-47 (2017).
[56] Ge, X.L., Zhang, Li-Zi, Shu, J. et al. “Short-term hydropower optimal scheduling considering the optimization of water time delay”, Elect Power Syst Res,110, pp 188–197 (2014).
[57] Thakur, S., Chanwit B., and Weerakon O. "Optimal hydrothermal generation scheduling using self-organizing hierarchical PSO", IEEE PES General Meeting, 2010.
[58] Roy P.K., Sur A. and Pradhan D.K. "Optimal short-term hydro-thermal scheduling using quasi-oppositional teaching learning based optimization", Engineering Applications of Artificial Intelligence, 26, pp. 2516-2524 (2013).
 [59] Derac, j., Garcia, S., Molina, D. et al. “A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms”, Swarm and evolutionary computation. 1, pp 3-18 (2011).
[60] Shenkin D. J. “Hand book of parametric and no parametric statistical procedures”, 4th ED., Chapman & Hall/CRC (2006).
Scientia Iranica
Volume 29, Issue 2
Transactions on Computer Science & Engineering and Electrical Engineering (D)
March and April 2022
Pages 749-770

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History

  • Receive Date: 08 February 2019
  • Revise Date: 08 July 2019
  • Accept Date: 07 December 2020

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