DOI: https://doi.org/10.14710/ijred.2021.32245

Optimal Scheduling of Solar-Wind-Thermal Integrated System Using α-Constrained Simplex Method

1Department of Electrical Engineering, I.K. Gujral Punjab Technical University, Kapurthala 144603, Punjab, India

2Department of Electrical & Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, Sangrur 148106, Punjab, India

Received: 6 Aug 2020; Revised: 20 Sep 2020; Accepted: 30 Sep 2020; Available online: 5 Oct 2020; Published: 1 Feb 2021.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2021 The Authors. Published by CBIORE
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
In this paper, multi-objective economic-environmental solar-wind-thermal power scheduling model was developed and it was optimized for five test systems. First test system was based upon a purely thermal power generating system and its problem was formulated to satisfy three conflicting objectives: (i) fuel cost, (ii)  emission, and (iii)  emission. The second, third and fourth test systems were comprised of optimal scheduling of integrated solar-thermal, wind-thermal and solar-wind-thermal power systems, respectively. Uncertainty costs were also considered in the renewable power based systems. These four test systems were examined for five power demands i.e. 200 MW, 225 MW, 250 MW, 275 MW, & 300 MW. Fifth test system was also deployed upon a renewable-thermal power scheduling. The effects of variation in number of thermal generators on fuel cost and  emission were perceived, for a power demand of 400 MW. The values of fuel cost (4067.98 Rs/h) and  emission (2,441.05 kg/h) reduced to 3,232.94 Rs/h and 1,939.30 kg/h, respectively, when number of thermal generators were reduced from four to two. The -constrained simplex method (ACSM) was used for simulation and the results were compared with simplex method (SM). The results clearly depict the dominance of ACSM over SM in almost all the fields.
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Keywords: Integrated system; Fuel cost; Pollutant emission; Wind farms; Solar units; α- constrained simplex method

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Section: Original Research Article
Language : EN
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  1. Brar, Y.S., Dhillon, J.S., & Kothari, D.P. (2005). Fuzzy satisfying multi-objective generation scheduling based on simplex weightage pattern search. International Journal of Electrical Power & Energy Systems, 27(7), 518-527; https://doi.org/10.1016/j.ijepes.2005.06.002
  2. Correa-Jullian, C., Droguett, E.L., & Cardemil, J.M. (2020). Operation scheduling in a solar thermal system: A reinforcement learning - based framework. Applied Energy, 268, 11943; https://doi.org/10.1016/j.apenergy.2020.114943
  3. Damodaran, S.K., & Kumar, T.K.S. (2018). Hydro-thermal-wind generation scheduling considering economic and environmental factors using heuristic algorithms. Energies, 11(2), 353; https://doi.org/10.3390/en11020353
  4. Deb, K. (2005). Optimization for Engineering Design: Algorithms and Examples, 2nd ed. PHI, New Delhi, India; ISBN-978-81-203-4678-9
  5. Dhillon, J.S., Parti, S.C., & Kothari, D.P. (2002). Fuzzy decision making in stochastic multi-objective short term hydrothermal scheduling. IEE Proceedings - Generation, Transmission and Distribution, 149(2), 191-200; http://dx.doi.org/10.1049/ip-gtd:20020176
  6. Dubey, H.M., Pandit, M., & Panigrahi, B.K. (2015a). Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch. Renewable Energy, 83, 188-202; https://doi.org/10.1016/j.renene.2015.04.034
  7. Dubey, H.M., Pandit, M., & Panigrahi, B.K. (2015b). A biologically inspired modified flower pollination algorithm for solving economic dispatch problems in modern power systems. Cognitive Computation, 7, 594-608; https://doi.org/10.1007/s12559-015-9324-1
  8. Gangwar, A.K., & Chishti, F. (2014). Hybrid simplex method for economic load dispatch. Advanced Research in Electrical and Electronic Engineering, 1(3), 51-57
  9. He, Z., Zhou, J., Sun, N., Jia, B., & Qin, H. (2019). Integrated scheduling of hydro, thermal and wind power with spinning reserve. Energy Procedia, 158, 6302-6308; https://doi.org/10.1016/j.egypro.2019.01.409
  10. Hetzer, J., Yu, D.C., & Bhattarai, K. (2008). An economic dispatch model incorporating wind power. IEEE Transactions on Energy Conversion, 23(2), 603-611; https://doi.org/10.1109/TEC.2007.914171
  11. Kayalvizhi, S., & Kumar, D.M.V. (2018). Stochastic optimal power flow in presence of wind generations using harmony search algorithm, Proceedings of the 20th National Power Systems Conference, Dec. 14-16.NIT Tiruchirappalli, India; http://doi.org/10.1109/NPSC.2018.8771822
  12. Kothari, D.P., & Dhillon, J.S. (2011). Power System Optimization. 2nd ed. PHI, New Delhi, India
  13. Li, F., & Kuri, B. (2005). Generation scheduling in a system with wind power. 2005 IEEE/PES Transmission and Distribution Conference & Exhibition: Asia and Pacific, August 18, 2005, Dalian China,1-6; http://doi.org/10.1109/TDC.2005.1547157
  14. Mayo, W.E. (1995). Programming with Fortran 77. McGraw-Hill
  15. Mohamed, M., Youssef, A., Ebeed, M., & Kamel, S. (2019). Hybrid optimization technique for short term wind-solar-hydrothermal generation scheduling. IEEE Conference on Power Electronics and Renewable Energy (CPERE), Aswan City, Egypt, pp. 212-216; https://doi.org/10.1109/CPERE45374.2019.8980237
  16. Mondal, S., Bhattacharya, A., & Halder, S. (2012). Solution of cost constrained emission dispatch problems considering wind power generation using gravitational search algorithm. IEEE-International Conference on Advances in Engineering, Science and Management (ICAESM-2012), Nagapattinam, Tamil Nadu, India, 169-174
  17. Mondal, S., Bhattacharya, A., & Dey, S.H.N. (2013). Multi- objective economic emission load dispatch solution using gravitational search algorithm and considering wind power penetration. International Journal of Electrical Power and Energy Systems, 44(1), 282-292; https://doi.org/10.1016/j.ijepes.2012.06.049
  18. Nguyen, T.T., Pham, L.H., Mohammadi, F., & Kien, L.C. (2020). Optimal scheduling of large scale wind-hydro-thermal systems with fixed-head short-term model. Applied Sciences, 10(8), 2964; https://doi.org/10.3390/app10082964
  19. Panda, A., & Tripathy, M. (2016). Solution of wind integrated thermal generation system for environmental optimal power flow using hybrid algorithm, Journal of Electrical Systems and Information Technology, 3(2), 151-160; https://doi.org/10.1016/j.jesit.2016.01.004
  20. Reddy, S.S. (2017a). Optimal scheduling of wind-thermal power system using clustered adaptive teaching learning based optimization. Electrical Engineering, 99, 535-550; https://doi.org/10.1007/s00202-016-0382-5
  21. Reddy, S.S. (2017b). Optimization of renewable energy resources in hybrid energy systems. Journal of Green Engineering, 7(1&2), 43-60; https://doi.org/10.13052/jge1904-4720.7123
  22. Reddy, S.S., Abhyankar, A.R., Bijwe, P.R. (2011). Multi-objective day ahead real power market clearing with voltage dependent load models. International Journal of Emerging Electric Power Systems, 12(4); https://doi.org/10.2202/1553-779X.2727
  23. Reddy, S.S., Abhyankar, A.R., & Bijwe, P.R. (2015). Co-optimization of energy and demand-side reserves in day-ahead electricity markets. International Journal of Emerging Electric Power Systems, 16(2), 195-206; https://doi.org/10.1515/ijeeps-2014-0020
  24. Reddy, S.S., Park, J.Y., & Jung, C.M. (2016). Optimal operation of microgrid using hybrid differential evolution and harmony search algorithm. Frontiers in Energy, 10(3), 355-362; https://doi.org/10.1007/s11708-016-0414-x
  25. Saxena, N., & Ganguli, S. (2015). Solar and wind power estimation and economic load dispatch using firefly algorithm. Procedia Computer Science, 70, 688-700; https://doi.org/10.1016/j.procs.2015.10.106
  26. Singal, R.K. (2009). Non-Conventional Energy Resources, 2nd ed., S.K. Kataria & Sons, New Delhi India. ISBN-8188458821
  27. Sinha, N., Chakrabarti, R., & Chattopadhyay, P.K. (2003). Evolutionary programming techniques for economic load dispatch. IEEE Transactions on Evolutionary Computation, 7(1), 83-93; http://doi.org/10.1109/TEVC.2002.806788
  28. Takahama, T., & Sakai, S. (2005). Constrained optimization by applying the /spl alpha/ constrained method to the nonlinear simplex method with mutations. IEEE Transactions on Evolutionary Computation, 9(5), 437-450; http://doi.org/10.1109/TEVC.2005.850256
  29. Tan, S., Wang, X., & Jiang, C. (2019). Optimal scheduling of hydro-PV-Wind hybrid system considering CHP and BESS coordination. Applied Sciences, 9(5) 892; https://doi.org/10.3390/app9050892
  30. Tyagi, N., Dubey, H.M., & Pandit, M. (2016). Economic load dispatch of wind-solar-thermal system using backtracking search algorithm. International Journal of Engineering, Science and Technology, 8(4), 16-27; http://dx.doi.org/10.4314/ijest.v8i4.3
  31. Vaderobli, A., Parikh, D., & Diwerkar, U. (2020). Optimization under uncertainty to reduce the cost of energy for parabolic trough solar power plants for different weather conditions. Energies, 13(12), 3131; https://doi.org/10.3390/en13123131
  32. Wood, A.J., & Wollenberg, B.F. (2006). Power Generation, Operation, and Control, 2nd ed., Wiley, India. ISBN 978-81-265-0838-9

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