Skip to main content
SpringerLink
Log in

Self-adaptive differential evolution with Gaussian–Cauchy mutation for large-scale CHP economic dispatch problem

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

Abstract

With the widespread application of co-generation units, the combined heat and power economic dispatch (CHPED) has become an important issue in the power system operation. Existing research work mostly focuses on small- or medium-scale CHPED problem, and there is very little research work on large-scale CHPED problems. Considering the characteristics of high-dimensional variables and huge search space in large-scale CHPED problem, it brings great challenge to the existing algorithms. In this paper, an improved differential evolution algorithm, called self-adaptive differential evolution with Gaussian–Cauchy mutation (SDEGCM), is proposed to solve the large-scale CHPED problem. In SDEGCM, in order to improve the performance, two strategies namely Gaussian–Cauchy mutation and parameter self-adaptation are introduced. Moreover, a constraint repair technique is used in SDEGCM to deal with complex operating constraints. The SDEGCM is applied to solve three large-scale CHPED problems with 48, 84 and 96 units, and compared with three well-established differential evolution and other methods in the literature. It is found that the proposed SDEGCM has advantages in terms of solution accuracy and stability for the large-scale CHPED problem.

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

Similar content being viewed by others

Abbreviations

ABC:

Artificial bee colony

BA:

Bat algorithm

BLPSO:

Biogeography-based learning particle swarm optimization

CHP:

Combined heat and power

CHPED:

Combined heat and power economic dispatch

COA:

Cuckoo optimization algorithm

CSA:

Cuckoo search algorithm

CSA-BA-ABC:

Hybridizing bat algorithm and artificial bee colony with chaotic based self-adaptive search

CSO:

Crisscross optimization algorithm

DE:

Differential evolution

DEGM:

Differential evolution with Gaussian mutation

ECSA:

Effective cuckoo search algorithm

FA:

Firefly algorithm

FSRPSO:

Hybrid firefly and self-regulating particle swarm optimization

GA:

Genetic algorithm

GCM:

Gaussian–Cauchy mutation strategy

GSA:

Gravitational search algorithm

GSO:

Group search optimization

HS:

Harmony search

HTS:

Heat transfer search

IABC:

Improved artificial bee colony

IGA-NCM:

Improved genetic algorithm using novel crossover and mutation

JADE:

Adaptive differential evolution with optional external archive

KKO:

Kho-kho optimization

LHS:

Latin hypercube sampling

MADS:

Mesh adaptive direct search algorithm

MPHS:

Multi-player harmony search method

PSA:

Parameter self-adaptive strategy

PSO:

Particle swarm optimization

RCGA-CRWM:

Real-coded genetic algorithm with random walk-based mutation

RCGA-IMM:

Real-coded genetic algorithm with improved Muhlenbein mutation

SABBO:

Biogeography-based optimization with simulated annealing

SaDE:

Self-adaptive differential evolution

SDEGCM:

Self-adaptive differential evolution with Gauss and Cauchy mutation

SFO:

Sailfish optimization

SFS:

Stochastic fractal search

SRPSO:

Self-regulating particle swarm optimization

TCSO:

Social cognitive optimization with tent map

TVAC-GSA-PSO:

Hybrid gravitational search algorithm-particle swarm optimization with time varying acceleration coefficients

TVAC-PSO:

Particle swarm optimization with time varying acceleration coefficients

WOA:

Whale optimization algorithm

References

  1. Abdolmohammadi HR, Kazemi A (2013) A benders decomposition approach for a combined heat and power economic dispatch. Energy Convers Manag 71:21–31

    Article  Google Scholar 

  2. Keirstead J, Samsatli N, Shah N, Weber C (2012) The impact of chp (combined heat and power) planning restrictions on the efficiency of urban energy systems. Energy 41:93–103

    Article  Google Scholar 

  3. Nazari-Heris M, Abapour S, Mohammadi-Ivatloo B (2017) Optimal economic dispatch of fc-chp based heat and power micro-grids. Appl Thermal Eng 114:756–769

    Article  Google Scholar 

  4. Mellal MA, Williams EJ (2015) Cuckoo optimization algorithm with penalty function for combined heat and power economic dispatch problem. Energy 93:1711–1718

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. 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 

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

    Article  Google Scholar 

  8. 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 

  9. 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 

  10. Murugan R, Mohan M, Rajan CCA, Sundari PD, Arunachalam S (2018) Hybridizing bat algorithm with artificial bee colony for combined heat and power economic dispatch. Appl Soft Comput 72:189–217

    Article  Google Scholar 

  11. 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 

  12. Storn R, Price K (1995) De-a simple and efficient adaptive scheme for global optimization over continuous space. Techn Rep 25:95–102

    Google Scholar 

  13. Chen X, Du W, Qian F (2014) Multi-objective differential evolution with ranking-based mutation operator and its application in chemical process optimization. Chemometr Intell Lab Syst 136:85–96

    Article  Google Scholar 

  14. Gao S, Wang K, Tao S, Jin T, Dai H, Cheng J (2021) A state-of-the-art differential evolution algorithm for parameter estimation of solar photovoltaic models. Energy Convers Manag 230:113784

    Article  Google Scholar 

  15. Zhao W, Ma A, Ji J, Chen X, Yao T (2019) Multiobjective optimization of a double-side linear vernier pm motor using response surface method and differential evolution. IEEE Trans Ind Electron 67:80–90

    Article  Google Scholar 

  16. Basu M (2010) Combined heat and power economic dispatch by using differential evolution. Electr Power Compon Syst 38:996–1004

    Article  Google Scholar 

  17. Hosseini SSS, Jafarnejad A, Behrooz AH, Gandomi AH (2011) Combined heat and power economic dispatch by mesh adaptive direct search algorithm. Expert Syst Appl 38:6556–6564

    Article  Google Scholar 

  18. Yazdani A, Jayabarathi T, Ramesh V, Raghunathan T (2013) Combined heat and power economic dispatch problem using firefly algorithm. Front Energy 7:133–139

    Article  Google Scholar 

  19. Sun J, Li Y (2019) Social cognitive optimization with tent map for combined heat and power economic dispatch. Int Trans Electr Energy Syst 29:e2660

    Article  Google Scholar 

  20. Gu H, Zhu H, Chen P, Si F (2019) Improved hybrid biogeography-based algorithm for combined heat and power economic dispatch with feasible operating region and energy saving potential. Electr Power Compon Syst 47:1677–1690

    Article  Google Scholar 

  21. 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 154:493–504

    Article  Google Scholar 

  22. Chen X, Li K, Xu B, Yang Z (2020) Biogeography-based learning particle swarm optimization for combined heat and power economic dispatch problem. Knowl-Based Syst 208:106463

    Article  Google Scholar 

  23. Alomoush MI (2020) Optimal combined heat and power economic dispatch using stochastic fractal search algorithm. J Modern Power Syst Clean Energy 8:276–286

    Article  Google Scholar 

  24. Rabiee A, Jamadi M, Mohammadi-Ivatloo B, Ahmadian A (2020) Optimal non-convex combined heat and power economic dispatch via improved artificial bee colony algorithm. Processes 8:1036

    Article  Google Scholar 

  25. Haghrah A, Nekoui M, Nazari-Heris M, Mohammadi-ivatloo B (2020) An improved real-coded genetic algorithm with random walk based mutation for solving combined heat and power economic dispatch. J Ambient Intell Human Comput 12(8):8561–8584

    Article  Google Scholar 

  26. Srivastava A, Das DK (2020a) A sailfish optimization technique to solve combined heat and power economic dispatch problem. In: IEEE Students Conference on Engineering & Systems, pp 1–6

  27. 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

    Article  Google Scholar 

  28. Nasir M, Sadollah A, Aydilek İB, Ara AL, Nabavi-Niaki SA (2021) A combination of fa and srpso algorithm for combined heat and power economic dispatch. Appl Soft Comput 102:107088

    Article  Google Scholar 

  29. Victoire TAA, Jeyakumar AE (2005) Reserve constrained dynamic dispatch of units with valve-point effects. IEEE Trans Power Syst 20:1273–1282

    Article  Google Scholar 

  30. Price KV (2013) Differential evolution. In: Handbook of optimization. Springer, pp 187–214

  31. Ge Y-F, Yu W-J, Lin Y, Gong Y-J, Zhan Z-H, Chen W-N, Zhang J (2017) Distributed differential evolution based on adaptive mergence and split for large-scale optimization. IEEE Trans Cybern 48:2166–2180

    Article  Google Scholar 

  32. Deng L-B, Zhang L-L, Fu N, Sun H-L, Qiao L-Y (2020) Erg-de: an elites regeneration framework for differential evolution. Inf Sci 539:81–103

    Article  MathSciNet  MATH  Google Scholar 

  33. Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evolut Comput 10:646–657

    Article  Google Scholar 

  34. Jena C, Basu M, Panigrahi C (2016) Differential evolution with gaussian mutation for combined heat and power economic dispatch. Soft Comput 20:681–688

    Article  Google Scholar 

  35. Qin AK, Huang VL, Suganthan PN (2008) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13:398–417

    Article  Google Scholar 

  36. Zhang J, Sanderson AC (2009) Jade: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13:945–958

    Article  Google Scholar 

  37. Meng A, Mei P, Yin H, Peng X, Guo Z (2015) Crisscross optimization algorithm for solving combined heat and power economic dispatch problem. Energy Convers Manag 105:1303–1317

    Article  Google Scholar 

  38. Beigvand SD, Abdi H, La Scala M (2016) Combined heat and power economic dispatch problem using gravitational search algorithm. Electr Power Syst Res 133:160–172

    Article  Google Scholar 

  39. Nguyen TT, Vo DN, Dinh BH (2016) Cuckoo search algorithm for combined heat and power economic dispatch. Int J Electr Power Energy Syst 81:204–214

    Article  Google Scholar 

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

    Article  Google Scholar 

  41. 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 10:81–96

    Article  Google Scholar 

  42. Beigvand SD, Abdi H, La Scala M (2017) Hybrid gravitational search algorithm-particle swarm optimization with time varying acceleration coefficients for large scale chped problem. Energy 126:841–853

    Article  Google Scholar 

  43. 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

    Article  Google Scholar 

  44. Nazari-Heris M, Mehdinejad M, Mohammadi-Ivatloo B, Babamalek-Gharehpetian G (2019) Combined heat and power economic dispatch problem solution by implementation of whale optimization method. Neural Comput Appl 31:421–436

    Article  Google Scholar 

  45. Huang Z, Gao Z, Qi L, Duan H (2019) A heterogeneous evolving cuckoo search algorithm for solving large-scale combined heat and power economic dispatch problems. IEEE Access 7:111287–111301

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China

    Xu Chen & Anning Shen

Authors
  1. Xu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anning Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xu Chen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Availability of data and material

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

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

Chen, X., Shen, A. Self-adaptive differential evolution with Gaussian–Cauchy mutation for large-scale CHP economic dispatch problem. Neural Comput & Applic 34, 11769–11787 (2022). https://doi.org/10.1007/s00521-022-07068-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07068-w

Keywords

Search

Navigation