Skip to main content
SpringerLink
Log in

Multi-objective generation scheduling of integrated energy system using hybrid optimization technique

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper formulates multi-objective (MO) generation scheduling problem of an integrated energy system (IES). The integrated energy system comprises combined heat and power (CHP), hydroelectric, thermal and heat units. The interdependency of heat and power in CHP units, water transport delay among various multi-chain hydroelectric units and valve point loading effect of thermal units makes the generation scheduling problem a complex, constrained, discontinuous, non-differentiable and multimodal optimization problem. The main motive of this research work is to concurrently reduce the overall generation cost and pollutant emissions emitted by various generating units. The generation scheduling problem is treated as a MO problem owing to the contradictory nature of these objectives. Thus, a very proficient hybrid optimization approach, i.e., quantum-based cuckoo search algorithm (QCSA) with mutation operators, is proposed for searching for the optimal generation schedule of the MO-integrated energy system generation scheduling (IESGS) problem. The proposed technique significantly improves the solutions obtained by QCSA by utilizing three mutation operators, i.e., Cauchy, Gaussian, and opposition-based mutation. The proposed strategy has been implemented to MO-hydroelectric-thermal (HT) generation scheduling and MO-IESGS problems to demonstrate the efficacy of the proposed method. The cardinal priority method is utilized for finding the most suitable non-dominated solution for both problems. The obtained results are comparatively better than the published results. The proposed approach’s robustness compared to the QCSA has been further verified using the t-test. Based on comparisons and statistical analysis, the proposed technique is a promising approach for handling complex, multi-dimensional and non-convex generation scheduling optimization problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

Data used to perform the research are included in the article. Besides, additional data information is available in Supplement Material.

References

  1. Li Y, Wang J, Zhao D, Li G, Chen C (2018) A two-stage approach for combined heat and power economic emission dispatch: combining multi-objective optimization with integrated decision making. Energy 162:237–254

    Google Scholar 

  2. Fan H, Wang C, Liu L, Li X (2022) Review of Uncertainty Modeling for Optimal Operation of Integrated Energy System. Front Energy Res 9(January):1–17

    Google Scholar 

  3. Zakaria A, Ismail FB, Lipu MSH, Hannan MA (2020) Uncertainty models for stochastic optimization in renewable energy applications. Renew Energy 145:1543–1571

    Google Scholar 

  4. S. Flesca, F. Scala, E. Vocaturo, and F. Zumpano, “On forecasting non-renewable energy production with uncertainty quantification: A case study of the Italian energy market,” Expert Syst. Appl., vol. 200, no. March, p. 116936, 2022.

  5. Guo J, Wu L, Mu Y (2023) An optimized grey model for predicting non-renewable energy consumption in China. Heliyon 9(6):e17037

    Google Scholar 

  6. Pravin PS, Wang X (2022) Stochastic optimization approaches in solving energy scheduling problems under uncertainty. IFAC-PapersOnLine 55(7):815–820

    Google Scholar 

  7. Nilsson O, Sjelvgren D (1996) Mixed-Integer programming applied to short-term planning of a hydro-thermal system. IEEE Trans Power Syst 11(1):281–286

    Google Scholar 

  8. Wi S, An RI, Chang S, Fong I, Peter BL (1990) Hydroelectric generation scheduling with an effective differential dynamic programming. IEEE Trans Power Syst 5(3):737–743

    Google Scholar 

  9. Wood AJ, Wollenberg BF (1996) Power Generation, Operation and Control. John Wiley & Sons, New York

    Google Scholar 

  10. Mandal KK, Chakraborty N (2008) Differential evolution technique-based short-term economic generation scheduling of hydrothermal systems. Electr Power Syst Res 78(11):1972–1979

    Google Scholar 

  11. Kumar VS, Mohan MR (2011) A genetic algorithm solution to the optimal short-term hydrothermal scheduling. Int J Electr Power Energy Syst 33(4):827–835

    Google Scholar 

  12. Farhat IA, El-Hawary ME (2009) Optimization methods applied for solving the short-term hydrothermal coordination problem. Electr Power Syst Res 79(9):1308–1320

    Google Scholar 

  13. Mandal KK, Chakraborty N (2011) Short-term combined economic emission scheduling of hydrothermal systems with cascaded reservoirs using particle swarm optimization technique. Appl Soft Comput J 11(1):1295–1302

    Google Scholar 

  14. J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proceedings, IEEE Int. Conf., pp. 1942–1948, 1995.

  15. Narang N, Dhillon JS, Kothari DP (2014) Scheduling short-term hydrothermal generation using predator prey optimization technique. Appl Soft Comput J 21:298–308

    Google Scholar 

  16. Nguyen TT, Vo DN, Truong AV (2014) Cuckoo search algorithm for short-term hydrothermal scheduling. Appl Energy 132:276–287

    Google Scholar 

  17. N. J. Cheung, X. M. Ding, and H. Bin Shen, “A nonhomogeneous cuckoo search algorithm based on quantum mechanism for real parameter optimization,” IEEE Trans. Cybern., vol. 47, no. 2, pp. 391–402, 2017.

  18. K. Thirumal, V. P. Sakthivel, and P. D. Sathya, “Solution for short-term generation scheduling of cascaded hydrothermal system with turbulent water flow optimization,” Expert Syst. Appl., vol. 213, no. PA, p. 118967, 2023.

  19. Zeng X, Thaeer A, Manoj N, Subramaniam U, Almakhles DJ (2021) A grasshopper optimization algorithm for optimal short-term hydrothermal scheduling. Energy Rep 7:314–323

    Google Scholar 

  20. T. Guo and Mark I.Henwood and M. V. Ooijen, “An algorithm for combined heat and power economic dispatch,” IEEE Trans. Power Syst., vol. 11, no. 4, pp. 1778–1784, 1996.

  21. Yin H, Wu F, Meng X, Lin Y, Fan J, Meng A (2020) Crisscross optimization based short-term hydrothermal generation scheduling with cascaded reservoirs. Energy 203:117822

    Google Scholar 

  22. Rooijers FJ, van Amerongen RAM (1994) Static economic dispatch for co-generation systems. IEEE Trans Power Syst 9(3):1392–1398

    Google Scholar 

  23. Vasebi A, Fesanghary M, Bathaee SMT (2007) Combined heat and power economic dispatch by harmony search algorithm. Int J Electr Power Energy Syst 29(10):713–719

    Google Scholar 

  24. Nazari-Heris M, Babaei AF, Mohammadi-Ivatloo B, Asadi S (2018) Improved harmony search algorithm for the solution of non-linear non-convex short-term hydrothermal scheduling. Energy 151:226–237

    Google Scholar 

  25. Basu M (2011) Bee colony optimization for combined heat and power economic dispatch. Expert Syst Appl 38(11):13527–13531

    Google Scholar 

  26. Murugan R, Mohan MR, Asir CC, Sundari PD, Arunachalam S (2018) Hybridizing bat algorithm with artificial bee colony for combined heat and power economic dispatch. Appl Soft Comput J 72:189–217

    Google Scholar 

  27. Jayakumar N, Subramanian S, Ganesan S, Elanchezhian EB (2016) Grey wolf optimization for combined heat and power dispatch with cogeneration systems. Int J Electr Power Energy Syst 74:252–264

    Google Scholar 

  28. Gupta S, Deep K (2019) A novel random walk grey wolf optimizer. Swarm Evol Comput 44:101–112

    Google Scholar 

  29. Basu M (2016) Group search optimization for combined heat and power economic dispatch. Int J Electr Power Energy Syst 78:138–147

    Google Scholar 

  30. Hagh MT, Teimourzadeh S, Alipour M, Aliasghary P (2014) Improved group search optimization method for solving CHPED in large scale power systems. Energy Convers Manag 80:446–456

    Google Scholar 

  31. Q. Yang, P. Liu, J. Zhang, and N. Dong, “Combined heat and power economic dispatch using an adaptive cuckoo search with differential evolution mutation,” Appl. Energy, vol. 307, no. October 2021, p. 118057, 2022.

  32. X. S. Yang and S. Deb, “Cuckoo search via Levy flights,” World Congr. Nat. Biol. Inspired Comput. (NABIC 2009) - Proceedings, IEEE Publ. USA, pp. 210–214, 2009.

  33. Murali GB, Deepak B, Biswal B, Mohanta GB, Rout A (2018) Robotic optimal assembly sequence using improved cuckoo search algorithm. Procedia Comput Sci 133:323–330

    Google Scholar 

  34. Majumder A, Laha D, Suganthan PN (2018) A hybrid cuckoo search algorithm in parallel batch processing machines with unequal job ready times. Comput Ind Eng 124(June):65–76

    Google Scholar 

  35. Cheng J, Wang L, Jiang Q, Cao Z, Xiong Y (2018) Cuckoo search algorithm with dynamic feedback information. Futur Gener Comput Syst 89:317–334

    Google Scholar 

  36. Agrawal RK, Kaur B, Sharma S (2020) Quantum based whale optimization algorithm for wrapper feature selection. Appl Soft Comput J 89:106092

    Google Scholar 

  37. Layeb A (2013) A hybrid quantum inspired harmony search algorithm for 0–1 optimization problems. J Comput Appl Math 253:14–25

    MathSciNet  Google Scholar 

  38. Z. Xin-gang, L. Ji, M. Jin, and Z. Ying, “An improved quantum particle swarm optimization algorithm for environmental economic dispatch,” Expert Syst. Appl., p. 113370, 2020.

  39. Vijay RK, Nanda SJ (2019) A quantum grey wolf optimizer based declustering model for analysis of earthquake catalogs in an ergodic framework. J Comput Sci 36:101019

    Google Scholar 

  40. J. Liu, W. Xu, and J. Sun, “Quantum-behaved particle swarm optimization with mutation operator,” Proceddings 17th IEEE Int. Conf. Tools with Artif. Intell., 2005.

  41. Wang H, Liu Y, Zeng S, Li H, Li C (2007) Opposition-based particle swarm algorithm with cauchy mutation. EEE Congr Evol Comput 1:4750–4756

    Google Scholar 

  42. Patwal RS, Narang N, Garg H (2018) A novel TVAC-PSO based mutation strategies algorithm for generation scheduling of pumped storage hydrothermal system incorporating solar units. Energy 142:822–837

    Google Scholar 

  43. Zhang X, Yuen SY (2015) A directional mutation operator for differential evolution algorithms. Appl Soft Comput J 30:529–548

    Google Scholar 

  44. Zhou Y, Li X, Gao L (2013) A Differential evolution algorithm with intersect mutation operator. Appl Soft Comput 13:390–401

    Google Scholar 

  45. Yi W, Gao L, Li X, Zhou Y (2015) A new differential evolution algorithm with a hybrid mutation operator and self-adapting control parameters for global optimization problems. Appl Intell 42(4):642–660

    Google Scholar 

  46. Kaur A, Narang N (2019) Optimum generation scheduling of coordinated power system using hybrid optimization technique. Electr Eng 101(2):379–408

    Google Scholar 

  47. Basu M (2004) An interactive fuzzy satisfying method based on evolutionary programming technique for multiobjective short-term hydrothermal scheduling. Electr Power Syst Res 69:277–285

    Google Scholar 

  48. Mandal KK, Chakraborty N (2009) Short-term combined economic emission scheduling of hydrothermal power systems with cascaded reservoirs using differential evolution. Energy Convers Manag 50:97–104

    Google Scholar 

  49. Basu M (2011) Artificial immune system for fixed head hydrothermal power system. Energy 36(1):606–612

    Google Scholar 

  50. Basu M (2005) A simulated annealing-based goal-attainment method for economic emission load dispatch of fixed head hydrothermal power systems. Int J Electr Power Energy Syst 27(2):147–153

    Google Scholar 

  51. Feng Z, Niu W, Cheng C (2017) Multi-objective quantum-behaved particle swarm optimization for economic environmental hydrothermal energy system scheduling. Energy 131:165–178

    Google Scholar 

  52. Sundaram A (2020) Multiobjective multi-verse optimization algorithm to solve combined economic, heat and power emission dispatch problems. Appl Soft Comput J 91:106195

    Google Scholar 

  53. Wang L, Singh C (2008) Stochastic combined heat and power dispatch based on multi-objective particle swarm optimization. Int J Electr Power Energy Syst 30(3):226–234

    Google Scholar 

  54. Shi B, Yan LX, Wu W (2013) Multi-objective optimization for combined heat and power economic dispatch with power transmission loss and emission reduction. Energy 56:135–143

    Google Scholar 

  55. Basu M (2013) Combined heat and power economic emission dispatch using nondominated sorting genetic algorithm-II. Int J Electr Power Energy Syst 53(1):135–141

    Google Scholar 

  56. Zhou J, Liao X, Ouyang S, Zhang R, Zhang Y (2014) Multi-objective artificial bee colony algorithm for short-term scheduling of hydrothermal system. Int J Electr Power Energy Syst 55:542–553

    Google Scholar 

  57. Basu M (2019) Multi-area dynamic economic emission dispatch of hydro-wind-thermal power system. Renew Energy Focus 28:11–35

    Google Scholar 

  58. Li C, Zhou J, Lu P, Wang C (2015) Short-term economic environmental hydrothermal scheduling using improved multi-objective gravitational search algorithm. Energy Convers Manag 89:127–136

    Google Scholar 

  59. Lu Y, Zhou J, Qin H, Wang Y, Zhang Y (2011) A hybrid multi-objective cultural algorithm for short-term environmental/economic hydrothermal scheduling. Energy Convers Manag 52(5):2121–2134

    Google Scholar 

  60. Kumar A, Dhillon JS (2022) Environmentally sound short-term hydrothermal generation scheduling using intensified water cycle approach. Appl Soft Comput 127:109327

    Google Scholar 

  61. G. L. Decker and A. D. Brooks, “Valve point loading of turbines,” Trans. Am. Inst. Electr. Eng. Part III Power Appar. Syst., vol. 77, no. 3, pp. 481–484, 1958.

  62. Walters DC, Sheble GB (1993) Generatic algorithm solution of economic dispatch with valve point loading. IEEE Trans Power Syst 8(3):1325–1332

    Google Scholar 

  63. Vertis AS, Eisenberg L (1973) An approach to emissions minimization in power dispatch. J franklin inst 296:443–449

    Google Scholar 

  64. P. S. Nagendra Rao, “Combined heat and power economic dispatch: A direct solution,” Electr Power Compon Syst, vol. 34, no. 9, pp. 1043–1056, 2006.

  65. Patwal RS, Narang N (2018) Crisscross PSO algorithm for multi-objective generation scheduling of pumped storage hydrothermal system incorporating solar units. Energy Convers Manag 169(May):238–254

    Google Scholar 

  66. Boushaki SI, Kamel N, Bendjeghaba O (2018) A new quantum chaotic cuckoo search algorithm for data clustering. Expert Syst Appl 96:358–372

    Google Scholar 

  67. Andrews PS (2006) An investigation into mutation operators for particle swarm optimization. In: 2006 IEEE International Conference Evolutionary Computation, pp 1044–1051

  68. Jordehi AR (2015) Enhanced leader PSO (ELPSO): A new PSO variant for solving global optimisation problems. Appl Soft Comput J 26:401–417

    Google Scholar 

  69. Basu M (2010) Economic environmental dispatch of hydrothermal power system. Int J Electr Power Energy Syst 32(6):711–720

    Google Scholar 

  70. Basu M (2008) Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II. Int J Electr Power Energy Syst 30(2):140–149

    Google Scholar 

  71. Elaiw AM, Xia X, Shehata AM (2013) Combined heat and power dynamic economic dispatch with emission limitations using hybrid DE-SQP method. Abstr. Appl Anal 13–15

  72. Narang N, Dhillon JS, Kothari DP (2014) Weight pattern evaluation for multiobjective hydrothermal generation scheduling using hybrid search technique. Int J Electr Power Energy Syst 62:665–678

    Google Scholar 

  73. Sun C, Lu S (2010) Short-term combined economic emission hydrothermal scheduling using improved quantum-behaved particle swarm optimization. Expert Syst Appl 37(6):4232–4241

    Google Scholar 

  74. Lu S, Sun C (2011) Quadratic approximation based differential evolution with valuable trade off approach for bi-objective short-term hydrothermal scheduling. Expert Syst Appl 38(11):13950–13960

    Google Scholar 

  75. Lakshminarasimman L, Subramanian S (2006) Short-term scheduling of hydrothermal power system with cascaded reservoirs by using modified differential evolution. IEE Proc Gener Transm Distrib 153(6):693–700

    Google Scholar 

Download references

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

  1. Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India

    Arunpreet Kaur & Nitin Narang

Authors
  1. Arunpreet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nitin Narang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AK: Conceptualization, Methodology, Software, Writing – original draft. NN: Supervision, Writing—review and editing.

Corresponding author

Correspondence to Arunpreet Kaur.

Ethics declarations

Conflict of interest

The authors declare no competing interests. The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 32 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaur, A., Narang, N. Multi-objective generation scheduling of integrated energy system using hybrid optimization technique. Neural Comput & Applic 36, 1215–1236 (2024). https://doi.org/10.1007/s00521-023-09091-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-09091-x

Keywords

Search

Navigation