Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Evolutionary Intelligence
Published in: Evolutionary Intelligence 1/2022

25-11-2020 | Research Paper

A quantum class topper optimization algorithm to solve combined emission economic dispatch problem

Authors: Abhishek Srivastava, Dushmanta Kumar Das, Pradeep Kumar Gupta

Published in: Evolutionary Intelligence | Issue 1/2022

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

A combined emission and economic dispatch problem is referred as a complex multi-objective problem related to power system. In this article, an hybridized version of class topper optimization known as quantum class topper optimization is proposed to solve this problem. In quantum class topper optimization, quantum mechanism is used to enhance the searching ability of the proposed algorithm. The exploration, exploitation and convergence behavior of quantum class topper optimization is validated using benchmark functions. Later, four test cases on combined emission and economic dispatch are used to test the effectiveness of quantum class topper optimization. These four tests prove that the proposed optimization algorithm is effective to solve this real time optimization problem.
previous article A multi-objective approach for solving transmission expansion planning problem considering wind power uncertainty
next article Protection scheme for hybrid transmission system using fuzzy inference system and microcontroller

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Wood AJ, Wollenberg BF, Sheblé GB (2013) Power generation, operation, and control. John Wiley & Sons, New Jersey
2.
Lee K, Park Y, Ortiz J (1984) Fuel-cost minimisation for both real-and reactive-power dispatches. In: IEE proceedings C (Generation, transmission and distribution), vol 131, No 3. IET, pp 85–93
3.
Chen C-L, Wang S-C (1993) Branch-and-bound scheduling for thermal generating units. IEEE Trans Energy Convers 8(2):184–189CrossRef
4.
Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872CrossRef
5.
Shadravan S, Naji H, Bardsiri VK (2019) The sailfish optimizer: a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Eng Appl Artif Intell 80:20–34CrossRef
6.
Shehab M, Abualigah L, Al Hamad H, Alabool H, Alshinwan M, Khasawneh AM (2019) Moth-flame optimization algorithm: variants and applications. Neural Comput Appl 32:9859–9884CrossRef
7.
8.
Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, BerlinCrossRef
9.
Park J-B, Lee K-S, Shin J-R, Lee KY (2005) A particle swarm optimization for economic dispatch with nonsmooth cost functions. IEEE Trans Power Syst 20(1):34–42CrossRef
10.
Jayabarathi T, Jayaprakash K, Jeyakumar D, Raghunathan T (2005) Evolutionary programming techniques for different kinds of economic dispatch problems. Electr Power Syst Res 73(2):169–176CrossRef
11.
Wang Y, Li B, Weise T (2010) Estimation of distribution and differential evolution cooperation for large scale economic load dispatch optimization of power systems. Inf Sci 180(12):2405–2420CrossRef
12.
Kamboj VK, Bath S, Dhillon J (2016) Solution of non-convex economic load dispatch problem using grey wolf optimizer. Neural Comput Appl 27(5):1301–1316CrossRef
13.
Banerjee S, Maity D, Chanda CK (2015) Teaching learning based optimization for economic load dispatch problem considering valve point loading effect. Int J Electr Power Energy Syst 73:456–464CrossRef
14.
Bulbul SMA, Pradhan M, Roy PK, Pal T (2018) Opposition-based krill herd algorithm applied to economic load dispatch problem. Ain Shams Eng J 9(3):423–440CrossRef
15.
Kansal V, Dhillon J (2020) Emended salp swarm algorithm for multiobjective electric power dispatch problem. Appl Soft Comput 90:106172CrossRef
16.
Song Y, Wang G, Wang P, Johns A (1997) Environmental/economic dispatch using fuzzy logic controlled genetic algorithms. IEE Proc Gener Transm Distrib 144(4):377–382CrossRef
17.
Roy PK, Ghoshal S, Thakur S (2010) Combined economic and emission dispatch problems using biogeography-based optimization. Electr Eng 92(4–5):173–184CrossRef
18.
Güvenç U, Sönmez Y, Duman S, Yörükeren N (2012) Combined economic and emission dispatch solution using gravitational search algorithm. Sci Iran 19(6):1754–1762CrossRef
19.
Jayabarathi T, Bahl P, Ohri H, Yazdani A, Ramesh V (2012) A hybrid bfa-pso algorithm for economic dispatch with valve-point effects. Front Energy 6(2):155–163CrossRef
20.
Kavousi-Fard A, Khosravi A (2016) An intelligent \(\theta\)-modified bat algorithm to solve the non-convex economic dispatch problem considering practical constraints. Int J Electr Power Energy Syst 82:189–196CrossRef
21.
Liang H, Liu Y, Li F, Shen Y (2018) A multiobjective hybrid bat algorithm for combined economic/emission dispatch. Int J Electr Power Energy Syst 101:103–115CrossRef
22.
Khatsu S, Srivastava A, Das DK (2019) An adaptive phasor particle swarm optimization to solve economic load dispatch and combined emission economic load dispatch problem. In: 2019 international conference on electrical, electronics and computer engineering (UPCON). IEEE, pp 1–5
23.
Goudarzi A, Li Y, Xiang J (2020) A hybrid non-linear time-varying double-weighted particle swarm optimization for solving non-convex combined environmental economic dispatch problem. Appl Soft Comput 86:105894CrossRef
24.
25.
Kulkarni DKPS, Kothari AG (2000) Combined economic and emission dispatch using improved backpropagation neural network. Electr Mach Power Syst 28(1):31–44CrossRef
26.
27.
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67CrossRef
28.
Gherbi YA, Bouzeboudja H, Gherbi FZ (2016) The combined economic environmental dispatch using new hybrid metaheuristic. Energy 115:468–477CrossRef
29.
Abdelaziz A, Ali E, Elazim SA (2016) Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems. Energy 101:506–518CrossRef
30.
Elattar EE (2019) Environmental economic dispatch with heat optimization in the presence of renewable energy based on modified shuffle frog leaping algorithm. Energy 171:256–269CrossRef
31.
Mandal K, Mandal S, Bhattacharya B, Chakraborty N (2015) Non-convex emission constrained economic dispatch using a new self-adaptive particle swarm optimization technique. Appl Soft Comput 28:188–195CrossRef
32.
Kennedy J, Eberhart R (1995) Particle swarm optimization (pso). In: Proceedings of the IEEE international conference on neural networks, Perth, Australia, pp 1942–1948
33.
Metadata
Title
A quantum class topper optimization algorithm to solve combined emission economic dispatch problem
Authors
Abhishek Srivastava
Dushmanta Kumar Das
Pradeep Kumar Gupta
Publication date
25-11-2020
Publisher
Springer Berlin Heidelberg
Published in
Evolutionary Intelligence / Issue 1/2022
Print ISSN: 1864-5909
Electronic ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-020-00526-1

Other articles of this Issue 1/2022

Evolutionary Intelligence 1/2022 Go to the issue

Research Paper

Rank-based univariate feature selection methods on machine learning classifiers for code smell detection

Research Paper

A hybrid bio-inspired computing approach for buzz detection in social media

Research Paper

Particle swarm optimization for solving a scan-matching problem based on the normal distributions transform

Research Paper

A comparative approach to multiobjective optimization of AISC thin-walled members

Research Paper

A multi-objective approach for solving transmission expansion planning problem considering wind power uncertainty

Research Paper

Niching comprehensive learning gravitational search algorithm for multimodal optimization problems

Premium Partner

    Image Credits