Uncertainty Management in Short-Term Self-Scheduling Unit Commitment Using Harris Hawks Optimization Algorithm

Document Type : Research paper


1 Khuzestan Regional Electric Company (KZREC), Ahvaz, Iran

2 Department of Electrical Engineering,Institue for Higher Eduction, ACECR, Ahvaz, Iran


The present study focuses on the harris hawks optimizer. harris hawks optimization (HHO) is introduced based on population and nature patterns. The  HHO algorithm imitates harris hawks attacking behavior and includes two phases called exploration and exploitation, which can be modeled with three strategies, 1) discovering the prey, 2) surprising attack, and 3) prey attack. The main purpose of using this type of algorithm is to optimally solve the short-term hydro-thermal self-scheduling (STHTSS) problem with wind power(WP), photovoltaic (PV), small  hydro (SH) and pumped hydro storage (PHS) powr plants while considering uncertainties such as energy prices, ancillary services prices, etc, in the energy market. It will be shown how energy generation companies can use this algorithm and other algorithms and innovative methods that will be introduced in the future to achieve profit maximum with careful scheduling. It is worth mentioning that in this study, the effect of the presence and absence of two important factors, namely valve load cost (VLC) effect  and prohibited  operating  zones (POZs) (with linear modeling) that can affect the profit of units (power plants) has been pointed out. Finally, as shown in this study, several tests perfomed on the IEEE118-bus system validate the precision and credibility of the harris hawks optimization algorithm.


  1. j. Wood, B. F. Wollenberg, Power Generation Operation and Control, thirded. john Wiley & Sons Ltd.: 2013, NewYork, USA.
  2. Sharafi Masouleh, F. Salehi, F. Raeisi, M. Saleh, A. Brahman, A. Ahmadi, “Mixed-integer programming of stochastic hydro self-scheduling problem in joint energy and reserves markets,” Electric Power Compon. Syst. vol. 44, no. 7, pp. 752–762, 2016.
  3. Esmaeily, A. Ahmadi, F. Raeisi, M. R. Ahmadi, A. Esmaeel Nezhad, M. R. Janghorbani, “Evaluating the effectiveness of mixed-integer linear programming for day-A head hydrothermal self-scheduling considering price uncertainty and forced outage rate,” Energy, vol. 122, pp. 182–193, 2017.
  4. Bisanovic, M. Hajro, M. Dlakic, “Hydro-thermal selfscheduling problem in a day-ahead electricity market,” Electr. Power Syst. Res., vol. 78, no. 9, pp. 1579–1596, 2008.
  5. Giuntoli, PoliD, “A Novel Mixed- integer Linear Algorithm to generate unit commitment and dispatching scenarios for reliability test grids,” Int. Rev. Electr. Eng. (IREE), vol. 6, no. 4 pp. 1971–1982, 2011.
  6. P. Zeng, J. Wang, A. L. Liu, “Stochastic optimization for unit commitment –A review,” IEEE Trans. Power Syst., vol. 30, no. 4, 2014.
  7. Gavrilas,V. Stahie, “Cascade hydropower plants optimization with honey bee mating optimization algorithm,” Int. Rev. Electr. Eng. (IREE), vol. 6, no. 5, 2011.
  8. J. Conejo, J. M. Arroyo, J. Contreras, and F. A. Villamor, “Self-scheduling of a hydro producer in a pool-based electricity market,” IEEE Trans. Power Syst., vol. 17, no. 4, pp. 1265–1272, 2002.
  9. Karami, H. A. Shayanfar, J. Aghaei, and A. Ahmadi, “Scenario-based security constrained hydro-therm coordination with volatile wind power generation,” Renewable Sustainable Energy., vol 28, pp. 726–737, 2013.
  10. Aghaei, A. Ahmadi, H. A. Shayanfar, and Rabiee, “A mixed-integer programming of generalized hydro-thermal self-scheduling of generating units,” Electr. Eng., vol. 95, no. 2, pp. 109–125, 2013.
  11. Connolly, H. Lund, B. Mathiesen, M. Leahy, “A review of computer tools for analyzing the integration of renewable energy into various energy systems,” Appl. Energy., vol. 87, no. 4, pp. 1059–82, 2010.
  12. M. Foley, P. G. Leahy, K. Li, E. J. McKeogh, A. P. Morrison, “Along term analysis of pumped hydro storage to firm wind power,” Appl. Energy., vol. 137, pp. 638–648, 2015.
  13. Ilak, I. Raj_sl, S. Krajcar, M. Delimar, “The impact of a wind variable generation on the hydro generation water shadow price,” Appl. Energy., vol. 154, no. 15, pp. 197–208, 2015.
  14. Jianzhong, L. Peng, L. Yuanzheng, W. Chao, Y. Liu, M. Li, “Short - term hydro-thermal-wind complementary scheduling considering uncertainty of wind power using an enhanced multi-objective bee colony optimization algorithm,” Energy Convers. Manage.., vol. 123, pp. 116–129, 2016.
  15. P. S. Catalão, H.M. I. Pousinho, J. Contreras, “Optimal hydro scheduling and offering strategies considering price uncertainty and risk management,” Energy, vol. 37, no. 1, pp. 237–244, 2012.
  16. Wu, M. Shahidehpour, T. Li, “GENCO’s risk-based maintenance outage scheduling,” IEEE Trans. Power Syst., vol. 23, no. 1, pp. 127–136, 2008.
  17. Wu, M. Shahidehpour, Z. Li, “GENCO’s risk-constrained hydro-thermal scheduling,” IEEE Trans. Power Syst., vol. 23, no. 4, pp. 1847–1858, 2008.
  18. L. Tseng, W. Zhu, “Optimal self-scheduling and bidding strategy of a thermal unit subject to ramp constraints and price uncertainty,” IET Gener. Transm. Distrib., vol. 4, no. 2, pp. 125–137, 2010.
  19. Lindblom, “Energy in Sweden facts and figures,” The Swedish Energy Agency, 2010.
  20. Moghimi, A. Ahmadi, A. Aghaei, M. Najafi, “Risk constrained self-scheduling of hydro-wind units for shortterm electricity markets considering intermittency and uncertainty,”Renewable Sustainable Energy Rev., vol. 16, pp. 4734–4743, 2012.
  21. Meng, H. G. Wang, Z. Y. Dong, K. P. Wong, “Quantum inspired particle swarm optimization for valve point economic load dispatch,” IEEE Trans. Power Syst., vol. 25, no. 1, pp. 215–222, 2010.
  22. Li, M. Shahidehpour, “Dynamic ramping in unit commitment,” IEEE Trans. Power Syst., vol. 22, no. 3, 1379–1381, 2007.
  23. Karami,    H. A. Shayanfar, J. Aghaei, A. Ahmadi, “Mixed-integer programming of security-constrained daily hydro-thermal generation scheduling (SCDHGS),” Sci. Iran., vol. 20, no. 6, pp. 2036–2050, 2013.
  24. Ahmadi, M. Charwand, J. Aghaei, “Risk-constrained optimal strategy for retailer forward contract portfolio,” Int. J. Elect. Power Energy Syst., vol. 53, pp. 704–713, 2013.
  25. Wei, Z. Hongxuan, D. Yu, W. Yiting, D. ling, X. Ming, “Short-term optimal operation of hydro-wind-solar hybrid system with Improved generative adversarial networks,” Appl. Energy., vol. 250, pp. 389–403, 2019.
  26. R. Behnamfar, H. Barati, M. Karami, “Stochastic shortterm hydro-thermal scheduling based on mixed integer programming (MIP) with volatile wind power generation,” J. Oper. Autom. Power Eng., vol. 8, no. 3, pp. 195–208, 2020.
  27. Díaz, J. Coto, J. Gómez-Aleixandre, “Optimal operation value of combined wind power and energy storage in multi-stage electricity markets,” Appl. Energy, vol. 235, pp. 1153–1168, 2019.
  28. Akbari, R. Hooshmand, M. Gholipour, M. Parastegari, “Stochastic programming-based optimal bidding of compressed air energy storage with wind and thermal generation units in energy and reserve market,” Energy, vol. 171, pp. 535–546, 2019.
  29. Xu, F. Wang, C. Lv, Q. Huang, H. Xie, “Economicenvironmental equilibrium Based optimal scheduling strategy towards wind – solar -thermal power generation system under limited resources,” Appl. Energy, vol. 231, pp. 355–371, 2018
  30. M. Zabetian -Hosseini, M. Oloomi-Buygi, “How does large-scale wind power generation affect energy and reserve prices,” J. Oper. Autom. Power Eng., vol. 6, no. 2, pp. 169–182, 2018
  31. Panda,M. Umakanta,A. Kathleen B, “Optimizing hybrid power systems with compressed air energy storage,” Energy, vol. 205, p. 117962, 2020.
  32. Shahzad Javed, T. Ma, J. Jurasz, M. Yasir Amin, “Solar-wind-pumped hydro energy storage systems: review and future perspective,” Renewable Energy, 2019.
  33. Guo, Y. He, H. Pei, S. Wu, “The multi-objective capacity optimization of wind-photovoltaic-thermal energy storage hybrid power system with electric heater,” Solar Energy, vol. 195, pp. 138–149, 2020.
  34. Akbari, R. A. Hooshmand, M. Gholipour, M. Parastegari, “Stochastic programming-based optimal bidding of compressed air energy storage with wind and thermal generation units in energy and reserve markets,” Energy, vol. 171, pp. 535–546, 2019.
  35. Das, A. Bhattacharya, A. K. Chakraborty, “Fixed head short-term hydro-thermal scheduling in presence of solar and wind power,” Energy Strategy Rev., vol. 22, pp. 47–60, 2018.
  36. E. Nazaria, M. M. Ardehali, “Optimal bidding strategy for a GENCO in day-ahead energy and spinning reserve markets with considerations for coordinated wind-pumped storage thermal system and CO2 emission,” Energy Strategy, vol. 26, p. 100405, 2019.
  37. S. Patwal, N. Narang, “Optimal generation scheduling of pumped storage hydro-thermal system with wind energy sources,” Appl. Soft Comput. J., vol. 93, p. 106345, 2020.
  38. Wang, C. Li, X. Liao, H. Qin, “Study on unit commitment problem considering pumped storage and renewable energy via a novel binary artificial sheep algorithm,” Appl. Energy, vol. 187, pp. 612–626, 2017.
  39. G. Damousis, A. G. Bakirtzis, P. S. Dokopolous, “A solution to the unit-commitment problem using integer coded genetic algorithm,” IEEE Trans. Power Syst., vol. 19, no. 1, pp. 198–205, 2003
  40. Nilsson, and D. Sjelvgren, “Hydro unit start-up costs and their impact on the short-term scheduling strategies of swedish power producers,” IEEE Trans. Power Syst., vol. 12, no. 1, pp. 38–44, 1997.
  41. Daneshi, A. L. Choobbari, M. Shahidehpour,and Z. Li, “Mixed-integer programming method to solve security constrained unit commitment with restricted operating zone limits,” IEEE Int. Con. on EIT, pp.187–92, 2008.
  42. R. AlRashidi, and M. E. El-Hawary, “Hybrid particle swarm optimization approach for solving the discrete OPF problem considering the valve loading effects,” IEEE Trans. Power Syst., vol. 22, no. 4, pp. 2030–2038, 2007.
  43. Li, M. Shahidehpour, “Price-based unit commitment: a case of lagrangian relaxation versus mixed-integer programming,” IEEE Trans. Power Syst., vol. 20, no. 4, pp. 2015–2025, 2005.
  44. M. Arroyo, A. J. Conejo, “Optimal response of a thermal unit to an electricity spot market,” IEEE Trans. Power Syst., vol. 15, no. 13, 1098–1104, 2000.
  45. http: //motor.ece.iit.edu /data/PBUCdata.pdf. Also market price is from http://motor. ece.iit.edu/data/PBUCdata.pdf.
  46. http://motor.ece.iit.edu/data/118bus_abreu. xls.
  47. G. Brown, R. W. Katz, A. H. Murphy, “Timeseries models to simulateand forecast wind speed and wind power,” J. Appl. Meteorol.., vol. 23, pp. 1184–1195, 1984.
  48. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, “Harris hawks optimization: algorithm and applications,” Future Gener. Comput. Syst., vol. 97, pp. 849–872, 2019.
  49. Yuan, H. Tian, Y. Yuan, Y. Huang, R. M. Ikram, “An extended NSGA-III for solution of multi-objective hydrothermal-wind scheduling considering wind power cost,” Energy Convers. Manage., vol. 96, pp. 568–578, 2015.
  50. Jianzhong, L. Peng, L.Yuanzheng, W. Chao, Y. Liu, M. Li, “Short-term hydro-thermal-wind complementary scheduling considering uncertaintyof wind power using an enhanced multiobjective bee colony optimization algorithm,” Energy Convers. Manage., vol. 123, pp. 116–129, 2016
  51. Wijesinghe, and L. L. Lai, “Small hydro power plant analysis and development,” in Proc. of the 4thInt. Conf. DRPT., Weihai, China, 2011, pp. 25–30.
  52. P. Biswas, P. N. Suganthan, and G. A. Amaratunga, “Optimal power flow solutions incorporating stochastic wind and solar power,” Energy Convers. Manage., vol. 148, pp. 1194–1207, 2017.
  53. García-González, R. M. R. de la Muela, L. M. Santos, and A. M González, “Stochastic joint optimization of wind Generation and pumped-storage units in an electricity market,” IEEE Trans. power syst., vol. 23, no. 2, 2008.
  54. Montero, A. Bello, and J. Reneses, “A review on the unit commitment problem, approaches, techniques and resolution methods,” Energies, vol. 15, no. 4, p. 1296, 2022.
  55. Anbazhagi, K. Asokan and R. AshokKumar, “A mutual approach for profit-based unit commitment in deregulated power system integrated with renewable energy sources,” Trans. Inst. Meas. Control, vol. 43, no. 5, pp. 1102–1116, 2021.
  56. Chaima, J. Lian, C. Ma, Y. Zhang, S. Kavwenje, “Complementary optimization of hydropower with pumped hydro storage–photovoltaic plant for all-day peak electricity demand in Malawi,” Energies, vol. 14, no. 16, p. 4948, 2021.
  57. Yang Li, J. Li, H. Chen, M. Jin, H. Ren, “Enhanced Harris hawks’ optimization with multi- strategy for global optimization tasks,” Expert Syst. Appl., vol. 185, p.115499, 2021
  58. Davoodi, S. Balaei-Sani, B. Mohammadi-Ivatloo, and M. Abapour, “Flexible continuous-time Modeling for multiobjective day-ahead scheduling of CHP units,” Sustainability, vol. 13, no. 9, p. 5058, 2021
  59. Da, M. De, K. K. Mandal, “Multi-objective optimization of hybrid renewable energy system by using novel autonomic soft computing techniques,” Comput. Electr. Eng., vol. 94, p. 107350, 2021.
  60. Mohamed, A. R. Youssef, S. Kamel, M. Ebeed, E.E. Elattar, Optimal Scheduling of Hydro–Thermal– Wind–Photovoltaic Generation Using Lightning Attachment Procedure Optimizer. Sustainability 2021, 13, 8846.
  61. S. Vasiyullah1, S. G. Bharathidasan, “Profit based unit commitment of thermal units with renewable energy and electric vehicles in power market,” J. Electr. Eng. Technol., vol. 16, no. 1, pp. 115–29, 2020.
  62. Salman, M. Kusaf, “Short-term unit commitment by using machine learning to cover the uncertainty of wind power forecasting,” Sustainability, vol. 13, no. 24, p. 13609, 2021.
  63. Shokouhandeh, M. Ahmadi Kamarposhti, I. Colak, K. Eguchi, “Unit commitment for power generation systems based on prices in smart grid environment considering uncertainty,” Sustainability, vol. 13, no. 18, p. 10219, 2021.
  64. Amjady, H. Nasiri-Rad, “Non-convex economic dispatch with AC constraints by a new real coded genetic Algorithm,” IEEE Trans. Power syst., vol. 24, no. 3, 1489–1502, 2009.
  65. R. AlRashidi, M.E. El-Hawary, “Hybrid particle swarm optimization approach for solving the discrete OPF problem considering the valve loading effects,” IEEE Trans. Power syst., vol. 22, no. 4, 2030–2038, 2007.
  66. Meng, H. G. Wang, Z.Y. Dong, K.P. Wong, “Quantuminspired particle swarm optimization for valve-point economic load dispatch,” IEEE Trans. Power syst., vol. 25, no. 1, 215–222, 2010.