Skip to main content
Log in

Novel Hybrid Algorithm Based on Combined Particle Swarm Optimization and Imperialist Competitive Algorithm for Non-Convex CHPED Solution

  • Original Article
  • Published:
Journal of Electrical Engineering & Technology Aims and scope Submit manuscript

Abstract

This paper presents a novel hybrid approach by integrating the imperialist competitive algorithm (ICA) with particle swarm optimization (PSO) method to deal with the combined heat and power economic dispatch (CHPED) problem with the bounded feasible operating region. Unlike many previous methods, this approach takes the valve-point effects explicitly into account as an absolute sinusoidal term in the conventional polynomial cost function. The efficiency and feasibility of the hybrid scheme are evaluated on three small (with three different scenarios), medium and large test systems. The simulation results suggest the superior performance of the proposed hybrid algorithm in finding optimum solutions compared to other existing methods.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

i :

The index of conventional thermal units.

j :

The index of cogeneration units.

k :

The index of heat-only units

\({P}_{i}^{p}\) :

The power output of the ith conventional thermal unit.

\({P}_{j}^{c},{H}_{j}^{c}\) :

The power and thermal output of the jth cogeneration unit

\({H}_{k}^{h}\) :

The thermal output of the kth heat-only unit

\({N}_{p}\) :

The number of conventional thermal units

\({N}_{c}\) :

The number of cogeneration units

\({N}_{h}\) :

The number of heat-only units

\({\alpha }_{i},{\beta }_{i}\;and\;{ \gamma }_{i}\) :

The cost coefficients of the ith conventional thermal unit.

\({\lambda }_{i}\;and\;{\rho }_{i}\) :

The cost coefficients representing valve point effects of the ith conventional thermal unit.

\({a}_{j},{b}_{j},{c}_{j},{d}_{j}, {e}_{j},\;and\;{f}_{j}\) :

The cost coefficients of the jth cogeneration unit.

\({a}_{k},{b}_{k}\;and\;{c}_{k}\) :

The cost coefficients of the kth heat-only unit.

\({P}_{\text{demand}}\) :

The system power demand

\({H}_{\text{demand}}\) :

The system thermal demand

\({{P}_{i}^{p,max}, P}_{i}^{p,min}\) :

The upper and lower limit of power output of the ith conventional power-only unit.

\({{H}_{k}^{h,max}, H}_{k}^{h,min}\) :

The upper and lower limit of power output of the kth heat-only unit

\({C}_{i}({P}_{i}^{p})\) :

The fuel cost of the conventional thermal unit i

\({C}_{j}({P}_{j}^{c},{H}_{j}^{c})\) :

The cost function of the cogeneration unit j

\({C}_{k}({H}_{k}^{h})\) :

The cost of the heat-only unit k

References

  1. Zhang L, Gao T, Cai G, Hai KL (2022) Research on electric vehicle charging safety warning model based on back propagation neural network optimized by improved gray wolf algorithm. J Energy Storage 1(49):104092

    Article  Google Scholar 

  2. Mou J, Duan P, Gao L, Liu X, Li J (2022) An effective hybrid collaborative algorithm for energy-efficient distributed permutation flow-shop inverse scheduling. Futur Gener Comput Syst 1(128):521–537

    Article  Google Scholar 

  3. Kusiak A, Zhang Z, Verma A (2013) Prediction, operations, and condition monitoring in wind energy. Energy 60:1–2

    Article  Google Scholar 

  4. Zhong C, Li H, Zhou Y, Lv Y, Chen J, Li Y (2022) Virtual synchronous generator of PV generation without energy storage for frequency support in autonomous microgrid. Int J Electr Power Energy Syst 1(134):107343

    Article  Google Scholar 

  5. Wang Y, Zou R, Liu F, Zhang L, Liu Q (2021) A review of wind speed and wind power forecasting with deep neural networks. Appl Energy 15(304):117766

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Subbaraj P, Rengaraj R, Salivahanan S (2009) Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm. Appl Energy 86:915–921

    Article  Google Scholar 

  8. Hosseini SSS, Gandomi AH. Discussion on “Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm, by P. Subbaraj et al., Applied Energy 86 (2009) 915–921

  9. Hosseini SSS, Gandomi AH. Discussion on “Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm, by P. Subbaraj et al., Appl Energy 2010; 87:1459.

  10. Wong KP, Algie C (2002) Evolutionary programming approach for combined heat and power dispatch. Electr Power Syst Res 61:227–232

    Article  Google Scholar 

  11. Niknam T, Azizipanah-Abarghooee R, Roosta A, Amiri B (2012) A new multi-objective reserve constrained combined heat and power dynamic economic emission dispatch. Energy 42:530–545

    Article  Google Scholar 

  12. Rong A, Lahdelma R (2007) An efficient envelope-based Branch and Bound algorithm for non-convex combined heat and power production planning. Eur J Oper Res 183:412–431

    Article  MATH  Google Scholar 

  13. Makkonen S, Lahdelma R (2006) Non-convex power plant modelling in energy optimisation. Eur J Oper Res 171:1113–1126

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  15. Huang S-H, Lin P-C (2013) A harmony-genetic based heuristic approach toward economic dispatching combined heat and power. Int J Electr Power Energy Syst 53:482–487

    Article  Google Scholar 

  16. Mohammadi-Ivatloo B, Moradi-Dalvand M, Rabiee A (2013) Combined heat and power economic dispatch problem solution using particle swarm optimization with time varying acceleration coefficients. Electr Power Syst Res 95:9–18

    Article  Google Scholar 

  17. Wang L, Singh C (2006) Stochastic combined heat and power dispatch based on multi-objective particle swarm optimization 2006 IEEE power Eng Soc Gen Meet, IEEE 8 pp

  18. Roy PK, Paul C, Sultana S (2014) Oppositional teaching learning based optimization approach for combined heat and power dispatch. Int J Electr Power Energy Syst 57:392–403

    Article  Google Scholar 

  19. Salgado F, Pedrero P (2008) Short-term operation planning on cogeneration systems: a survey. Electr Power Syst Res 78:835–848

    Article  Google Scholar 

  20. Zou D, Li S, Kong X, Ouyang H, Li Z (2019) Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy. Appl Energy 237:646–670

    Article  Google Scholar 

  21. Nguyen TT, Nguyen TT, Vo DN (2018) An effective cuckoo search algorithm for large-scale combined heat and power economic dispatch problem. Neural Comput Appl 30:3545–3564. https://doi.org/10.1007/s00521-017-2941-8

    Article  Google Scholar 

  22. Alomoush MI (2020) Optimal combined heat and power economic dispatch using stochastic fractal search algorithm J Mod Power Syst Clean Energy 8: 276-286 https://doi.org/10.35833/MPCE.2018.000753.

  23. Nazari-heris M, Mohammadi-ivatloo B, Asadi S, Geem ZW (2019) Large-scale combined heat and power economic dispatch using a novel multi-player harmony search method. Appl Therm Eng. https://doi.org/10.1016/j.applthermaleng.2019.03.095

    Article  Google Scholar 

  24. Goudarzi A, Zhang C, Fahad S, Mahdi AJ (2021) A hybrid sequential approach for solving environmentally constrained optimal scheduling in co-generation systems. Energy Rep 7:3460–3479. https://doi.org/10.1016/j.egyr.2021.05.078

    Article  Google Scholar 

  25. Nasir M, Sadollah A, Aydilek İB, Lashkar Ara A, Nabavi-Niaki SA (2021) A combination of FA and SRPSO algorithm for combined heat and power economic dispatch. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2021.107088

    Article  Google Scholar 

  26. Srivastava A, Das DK (2020) A new Kho-Kho optimization algorithm: an application to solve combined emission economic dispatch and combined heat and power economic dispatch problem. Eng Appl Artif Intell 94:103763. https://doi.org/10.1016/j.engappai.2020.103763

    Article  Google Scholar 

  27. Shaheen AM, Ginidi AR, El-Sehiemy RA, Ghoneim SSM (2020) Economic power and heat dispatch in cogeneration energy systems using manta ray foraging optimizer. IEEE Access 8:208281–208295. https://doi.org/10.1109/ACCESS.2020.3038740

    Article  Google Scholar 

  28. Ginidi AR, Elsayed AM, Shaheen AM, Elattar EE, El-Sehiemy RA (2021) A novel heap-based optimizer for scheduling of large-scale combined heat and power economic dispatch. IEEE Access 9:83695–83708. https://doi.org/10.1109/access.2021.3087449

    Article  Google Scholar 

  29. Cao B, Zhao J, Liu X, Arabas J, Tanveer M, Singh AK, Lv Z (2022) Multiobjective evolution of the explainable fuzzy rough neural network with gene expression programming IEEE Transactions Fuzzy Syst

  30. Meng F, Yang S, Wang J, Xia L, Liu H (2022) Creating knowledge graph of electric power equipment faults based on BERT–BiLSTM–CRF model. J Electric Eng Technol 4:1

    Google Scholar 

  31. Mehdinejad M, Mohammadi-Ivatloo B, Dadashzadeh-Bonab R (2017) Energy production cost minimization in a combined heat and power generation systems using cuckoo optimization algorithm. Energy Effic. https://doi.org/10.1007/s12053-016-9439-6

    Article  Google Scholar 

  32. Ramachandran M, Mirjalili S, Nazari-Heris M, Parvathysankar DS, Sundaram A, Gnanakkan CARC (2022) A hybrid grasshopper optimization algorithm and Harris Hawks optimizer for combined heat and power economic dispatch problem. Eng Appl Artif Intell 111:104753

    Article  Google Scholar 

  33. Kennedy J, Eberhart R (1995) Particle swarm optimization Proc. ICNN’95-international Conf neural networks, vol. 4, IEEE pp 1942–8

  34. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition 2007 IEEE Congr Evol Comput, IEEE, pp 4661–7

  35. Ghodrati A, Malakooti M V, Soleimani M (2012) A hybrid ICA/PSO algorithm by adding independent countries for large scale global optimization Asian Conf Intell Inf Database Syst, Springer; 2012, pp 99–108

  36. Khorram E, Jaberipour M (2011) Harmony search algorithm for solving combined heat and power economic dispatch problems. Energy Convers Manag 52:1550–1554

    Article  Google Scholar 

  37. Mohammadi-Ivatloo B, Rabiee A, Soroudi A, Ehsan M (2012) Iteration PSO with time varying acceleration coefficients for solving non-convex economic dispatch problems. Int J Electr Power Energy Syst 42:508–516

    Article  Google Scholar 

  38. Meng K, Wang HG, Dong Z, Wong KP (2009) Quantum-inspired particle swarm optimization for valve-point economic load dispatch. IEEE Trans Power Syst 25:215–222

    Article  Google Scholar 

Download references

Acknowledgements

This work was financially supported by the general project of natural science research in Colleges and universities of Jiangsu Province (Project Nos. 21KJD10004, 16KJD580001), the general project of philosophy and social sciences research in Colleges and universities in Jiangsu Province (Project No. 2021SJA1642), the collaborative education project of industry university cooperation of the Ministry of Education (Grant No. 202101056007), the project of Jiangsu Institute of Educational Technology (Grant No. 2021JSETKT064) and the Jiangsu Industry University Research Cooperation Project (Grant Nos. BY2020545, BY2020547), the Nantong Science and technology projects(Grant Nos. JCZ21059, JC2018148, GY12015024.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hashem Imani Marani.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Y., Gao, J., Gu, H. et al. Novel Hybrid Algorithm Based on Combined Particle Swarm Optimization and Imperialist Competitive Algorithm for Non-Convex CHPED Solution. J. Electr. Eng. Technol. 18, 1–13 (2023). https://doi.org/10.1007/s42835-022-01143-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42835-022-01143-x

Keywords

Navigation