Short-term Hydro-Thermal-Wind-Solar Power Scheduling: A Case Study of Kanyakumari Region of India

*Sunimerjit Kaur scopus  -  Department of Electrical Engineering, I.K. Gujral Punjab Technical University, Kapurthala 144603, Punjab, India
Yadwinder Singh Brar orcid scopus  -  Department of Electrical Engineering, I.K. Gujral Punjab Technical University, Kapurthala 144603, Punjab, India
Jaspreet Singh Dhillon orcid scopus  -  Department of Electrical & Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, Sangrur 148106, Punjab, India
Received: 6 Jan 2021; Revised: 15 Mar 2021; Accepted: 26 Mar 2021; Published: 1 Aug 2021; Available online: 10 Apr 2021.
DOI: https://doi.org/10.14710/ijred.2021.35558 View
Supplementary Data
Subject Supplementary Material
Type Supplementary Material
  Download (213KB)    Indexing metadata
Open Access Copyright (c) 2021 The Authors. Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract

In this paper, an advanced modus operandi named the -constrained simplex method (ACSM) is deployed to resolve a real-time hydro-thermal-wind-solar power scheduling problem. ACSM is an updated articulation of the Nonlinear Simplex Method (SM) of Nelder and Mead. It has been designed after interbreeding an ordinary SM with some other methods like-evolutionary method, α-constrained method, etc. To develop this technique three alterations in the SM are adopted (i) -level differentiation, (ii) mutations of the worst point, and (iii) the incorporation of multi-simplexes. A real-time multi-objective hydro-thermal-wind-solar power scheduling problem is established and optimized for the Kanyakumari (Tamil Nadu, India) for the 18th of September of 2020. Four contrary constraints are contemplated for this case study (i)fuel cost and employing cost of wind and solar power system, (ii) NOx emission, (iii) SO2 emission, and (iv) CO2 emission. The fidelity of the projected practice is trailed upon two test systems. The first test system is hinged upon twenty-four-hour power scheduling of a pure thermal power system. The values of total fuel cost,emission, emission, and emission are attained as 4707.19$/day, 59325.23 kg/day, 207672.70 kg/day, and 561369.20 kg/day, respectively. In the second test system, two thermal generators are reintegrated with renewable energy resources (RER) based power system (hydro, wind, and solar system) for the same power demands. The hydro, wind, and solar data are probed with the Glimn-Kirchmayer model, Weibull Distribution Density Factor, and Normal Distribution model, respectively. The outturns using ACSM are contrasted with the SM and evolutionary method(EM). For this real-time hydro-thermal-wind-solar power scheduling problem the values of fuel cost,  emission,  emission, and  emission are shortened to 1626.41 $/day, 24262.24 kg/day, 71753.80 kg/day, and 196748.20 kg/day, respectively for the specified interval using ACSM and with SM, these values are calculated as 1626.57 $/day, 24264.67 kg/day, 71760.98 kg/day, 196767.68 kg/day, respectively. The results for the same are obtained as 1626.74 $/day, 24267.10 kg/day, 71768.15 kg/day, 196787.55 kg/day, respectively, by using EM. The values of the operating cost of the solar system, wind system, total system transmission losses, and computational time of test system-2 with ACSM, SM, and EM are evaluated as 8438.76 $/day, 19017.42 $/day, 476.69 MW/day & 15.6 seconds; 8439.61 $/day, 19019.33 $/day, 476.74 MW/day and 16.8 sec; and 8447.20 $/day, 19036.43 $/day, 477.17 MW/day and 17.3 sec, respectively. The solutions portray the sovereignty of ACSM over the other two methods in the entire process.

Note: This article has supplementary file(s).

Keywords: Boundary-mutations; Multi-simplexes; Constrained-optimization; Uncertainty Cost; Hydro-Thermal-Wind-Solar Power Scheduling

Article Metrics:

  1. Ansari, M.M., Guo, C., Shaikh, M., Chopra N., Yang, B., Pan J., Zhu, Y., & Huang, X. (2020). Considering the uncertainty of hydrothermal wind and solar-based DG. Alexandra Engineering Journal, 59(6), 4211-4236; https://doi.org/10.1016/j.aej.2020.07.026
  2. Biswas, P.P., Suganthan, P.N., & Amaratunga, G.A.J. (2017). Optimal power flow solutions incorporating stochastic wind and solar power. Energy Conversion and Management, 148, 1194-1207; https://doi.org/10.1016/j.enconman.2017.06.071
  3. 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
  4. 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, 114943; https://doi.org/10.1016/j.apenergy.2020.114943
  5. 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
  6. Das S., Bhattacharya, A., Chakraborty, A.K. (2018). Fixed head short-term hydrothermal scheduling in presence of solar and wind power. Energy Strategy Reviews, 22, 47-60; https://doi.org/10.1016/j.esr.2018.08.001
  7. Dasgupta, K., Roy, P.K., & Mukherjee, V. (2020). Power flow based hydro-thermal-wind scheduling of hybrid power system using sine cosine algorithm. Electric Power Systems Research, 178, 106018; https://doi.org/10.1016/j.epsr.2019.106018
  8. Dhillon, J.S., Parti, S.C., & Kothari, D.P. (2002). Fuzzy decision-making in stochastic multiobjective short-term hydrothermal scheduling. IEE Proceedings - Generation, Transmission and Distribution, 149(2), 191-200; https://doi.org/10.1049/ip-gtd:20020176
  9. Dubey, H.M., Pandit, M., & Panigrahi, B.K. (2015). 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
  10. Dukkipati, S., Sankar, V., & Varma, P.S. (2019). Forecasting of solar irradiance using probability distributions for a PV system: a case study. International Journal of Renewable Energy Research, 9(2)
  11. Electricity Sector in India. In Wikipedia. [Online] Available: https://en.wikipedia.org/wiki/Electricity_sector_in_India#:~:text=The%20national%20electric%20grid%20in,of%20India's%20total%20installed%20capacity. Accessed on 1 December 2020
  12. El-Hawary, M.E., & Ravindranath, K.M. (1988). Optimal operation of variable head hydro-thermal systems using the Glimn-Kirchmayer model and the Newton-Raphson method. Electric Power Systems Research, 14(1), 11-22; https://doi.org/10.1016/0378-7796(88)90043-0
  13. 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
  14. 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
  15. Ji, B., Zhang, B., Yu, S.S., Zhang, D., & Yuan, X. (2021). An enhanced borg algorithmic framework for solving the hydro-thermal-wind co-scheduling problem. Energy, 218, 119512; https://doi.org/10.1016/j.energy.2020.119512
  16. Kaur, S, Brar, Y.S., & Dhillon, J.S. (2020a). Multi-objective power scheduling of wind-thermal integrated system by using α-constrained simplex method. International Conference on Smart Grid and Clean Energy Technologies (ICSGCE), Kuching, Malaysia, pp. 112-119; https://doi.org/10.1109/ICSGCE49177.2020.9275653
  17. Kaur, S., Brar, Y.S., & Dhillon, J.S. (2020b). Solar-thermal power scheduling by inserting α-constrained method to nonlinear simplex method with mutations. International Conference on Smart Grid and Clean Energy Technologies (ICSGCE), Kuching, Malaysia, pp. 27-34; https://doi.org/10.1109/ICSGCE49177.2020.9275629
  18. Kaur, S, Brar, Y.S., & Dhillon, J.S. (2021). Optimal scheduling of solar-wind-thermal-integrated system using α-constrained simplex method. International Journal of Renewable Energy Development, 10(1), 47-59; https://doi.org/10.14710/ijred.2021.32245
  19. 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; https://doi.org/10.1109/NPSC.2018.8771822
  20. Kothari, D.P., & Dhillon, J.S. (2011). Power Systems Optimization. 2nd ed. PHI, New Delhi, India
  21. Kumar, M.B.H., Balasubramaniyan, S., Padmanaban, S., & Holm-Nielsen, J.B. (2019). Wind energy potential assessment by Weibull parameter estimation using multiverse optimization method: a case study of Tirumala region in India. Energies, 12(11), 2158; https://doi.org/10.3390/en12112158
  22. Li, F., & Kuri, B. (2005). Generation scheduling in a system with wind power. 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific, Aug 18, Dalian, China, 1-6; https://doi.org/10.1109/TDC.2005.1547157
  23. Liaquat, S., Fakhar, M.S., Kashif, S.A.R., Rasool, A., Saleem, O., Zia, M.F., & Padmanaban, S. (2021).Application of dynamically search space squeezed modified firefly algorithm to a novel short term economic dispatch of multi-generation systems. IEEE Access, 9, 1918-1939; https://doi.org/10.1109/ACCESS.2020.3046910
  24. List of Countries by Electricity Production. In Wikipedia. [Online] Available: https://en.wikipedia.org/wiki/List_of_countries_by_electricity_production. Accessed on 1 December 2020
  25. Ma, T., Yuan, T., Sun, Y., Chen, N., Liu, X., & Gao, B. (2017). Power generation scheduling for wind-solar-thermal power long distance consumption based on game-theory. 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Honolulu, HI, 1296-1300; https://doi.org/10.1109/CYBER.2017.8446256
  26. Ministry of New and Renewable Energy, Government of India. Annual Report 2019-20. Available: https://mnre.gov.in/. Accessed on 27 October, 2020
  27. 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
  28. 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
  29. Muppandal Wind Farm. In Wikipedia. [Online] Available: https://en.wikipedia.org/wiki/Muppandal_Wind_Farm. Accessed on 20 December 2020
  30. Nagababu, G., Bavishi, D., Kachhwaha, S.S., & Savsani, V. (2015). Evaluation of wind resource in selected locations in Gujarat. Energy Procedia, 79, 212-219; https://doi.org/10.1016/j.egypro.2015.11.467
  31. Narang, N., Dhillon, J.S., & Kothari, D.P. (2014). Scheduling short-term hydrothermal generation using predator prey optimization technique. Applied Soft Computing, 21, 298-308; https://doi.org/10.1016/j.asoc.2014.03.029
  32. Naversen, C.Ø., Helseth, A., Li, B., Parvania, M., Farahmand, H., & Catalāo, J.P.S. (2020). Hydrothermal scheduling in the continuous-time framework. Electric Power Systems Research, 189, 106787; https://doi.org/10.1016/j.epsr.2020.106787
  33. NCERT India. India - Size and Location. 2020-21. [Online] Available: https://ncert.nic.in/textbook/pdf/iess101.pdf. Accessed on 10 September, 2020
  34. 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
  35. 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
  36. Rahimi, M., Ardakani, F.J., Ardakani, A.J. (2021). Optimal stochastic scheduling of electrical and thermal renewable and non-renewable resources in virtual power plant. International Journal of Electrical Power & Energy Systems, 127, 106658; https://doi.org/10.1016/j.ijepes.2020.106658
  37. 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
  38. 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
  39. 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
  40. Renewable Energy in India. In Wikipedia. [Online] Available: https://en.wikipedia.org/wiki/Renewable_energy_in_India#:~:text=As%20of%2027%20November%202020,GW%20out%20of%20373%20GW).&text=According%20to%202027%20blueprint%2C%20India,%E2%80%9Cother%20zero%20emission%E2%80%9D%20sources. Accessed on 10 December 2020
  41. Saxena, B.K., & Rao, K.V.S. (2015). Comparison of Weibull parameters computation methods and analytical estimation of wind turbine capacity factor using polynomial power curve model: case study of a wind farm. Renewables: Wind, Water, and Solar,2, 3; https://doi.org/10.1186/s40807-014-0003-8
  42. 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
  43. Singal, R.K. (2009). Non-Conventional Energy Resources, 2nd ed., S. K. Kataria& Sons, New Delhi, India. ISBN-8188458821
  44. Sukkiramathi, K., & Seshaiah, C.V. (2020). Analysis of wind power potential by the three-parameter Weibull distribution to install a wind turbine. Energy Exploration & Exploitation, 38(1), 158-174; https://doi.org/10.1177%2F0144598719871628
  45. 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), 437-451; https://doi.org/10.1109/TEVC.2005.850256
  46. Tamil Nadu Generation and Distribution Corporation Limited. www.tangedco.gov.in
  47. 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
  48. Vaderobli, A., Parikh, D., & Diwekar, 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

Last update: 2021-05-12 09:19:18

No citation recorded.

Last update: 2021-05-12 09:19:18

No citation recorded.