Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 12/2020

11.07.2019 | Original Article

Optimal power flow with stochastic wind power and FACTS devices: a modified hybrid PSOGSA with chaotic maps approach

verfasst von: Serhat Duman, Jie Li, Lei Wu, Ugur Guvenc

Erschienen in: Neural Computing and Applications | Ausgabe 12/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Nowadays, the increasing usage of renewable energy sources (RES) in modern power systems introduces new challenges in power system planning and operation. Specifically, a high penetration of RESs introduces additional complexity into the optimal power flow (OPF) problem, which has a highly nonlinear complex structure. Under this environment, this paper discusses a modified hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) integrated with chaotic maps (CPSOGSA) to apply the composite benchmark test functions and to solve the OPF problem with stochastic wind power and flexible alternating current transmission system (FACTS) devices. Numerical studies are used to illustrate effectiveness of the proposed CPSOGSA approach against other approaches such as moth swarm algorithm, grey wolf optimizer, and whale optimization algorithm. Additionally, to demonstrate the superiority and robustness of CPSOGSA algorithm, Wilcoxon signed-rank test is applied for all case studies. Case studies indicate the potential of CPSOGSA method in effectively solving OPF problem with stochastic wind power and FACTS devices.
Vorheriger Artikel Generative adversarial fusion network for class imbalance credit scoring
Nächster Artikel Performance of evolutionary wavelet neural networks in acrobot control tasks

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Literatur
1.
Rahmani S, Amjady N (2017) A new optimal power flow approach for wind energy integrated power systems. Energy 134:349–359CrossRef
2.
Teeparthi K, Kumar DMV (2017) Multi-objective hybrid PSO-APO algorithm based security constrained optimal power flow with wind and thermal generators. Eng Sci Technol Int J 20(2):411–426
3.
Luo J, Shi L, Ni Y (2018) A solution of optimal power flow incorporating wind generation and power grid uncertainties. IEEE Access 6:19681–19690CrossRef
4.
Chen JJ, Wu QH, Zhang LL, Wu PZ (2017) Multi-objective mean–variance–skewness model for nonconvex and stochastic optimal power flow considering wind power and load uncertainties. Eur J Oper Res 263(2):719–732MathSciNetMATHCrossRef
5.
Shafiq S, Javaid N, Asif S, Ali F, Hussain C, Khurshid N (2018) An optimal power flow approach for stochastic wind and solar energy integrated power systems. In: Proceedings of the 12th international conference on complex, intelligent, and software intensive systems (CISIS-2018), pp 292–304
6.
Kouadri R, Slimani L, Bouktir T, Musirin I (2018) Optimal power flow solution for wind integrated power in presence of VSC-HVDC using ant lion optimization. Indones J Electr Eng Comput Sci 12(2):625–633CrossRef
7.
Ghasemi H, Park HS, Rabczuk T (2017) A level-set based IGA formulation for topology of flexoelectric materials. Comput Methods Appl Mech Eng 313:239–258MathSciNetMATHCrossRef
8.
Yan X, Quintana VH (1999) Improving an interior-point-based OPF dynamic adjustments of step sizes and tolerances. IEEE Trans Power Syst 14(2):709–717CrossRef
9.
Zhang S, Irving MR (1994) Enhanced Newton-Raphson algorithm for normal, controlled and optimal power flow solutions using column exchange techniques. IEE Proc Gener Transm Distrib 141(6):647–657CrossRef
10.
Monticelli A, Pereira MVF, Granville S (1987) Security-constrained optimal power flow with post-contingency corrective rescheduling. IEEE Trans Power Syst 2(1):175–180CrossRef
11.
Alsac O, Bright J, Prais M, Stott B (1990) Further developments in LP-based optimal power flow. IEEE Trans Power Syst 5(3):697–711CrossRef
12.
Capitanescu F, Glavic M, Ernst D, Wehenkel L (2007) Interior-point based algorithms for the solution of optimal power flow problems. Electr Power Syst Res 77(5–6):508–517CrossRef
13.
Capitanescu F, Glavic M, Ernst D, Wehenkel L (2006) Applications of security-constrained optimal power flows. In: Proceedings of modern electric power systems symposium (MEPS06), Wroclaw, Poland
14.
Biswas PP, Suganthan PN, Mallipeddi R, Amaratunga GAJ (2018) Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques. Eng Appl Artif Intell 68:81–100CrossRef
15.
Attia AF, El Sehiemy RA, Hasanien HM (2018) Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. Int J Electr Power Energy Syst 99:331–343CrossRef
16.
El-Fergany AA, Hasanien HM (2018) Tree-seed algorithm for solving optimal power flow problem in large-scale power systems incorporating validations and comparisons. Appl Soft Comput 64:307–316CrossRef
17.
Saha A, Das P, Chakraborty AK (2017) Water evaporation algorithm: a new metaheuristic algorithm towards the solution of optimal power flow. Eng Sci Technol Int J 20:1540–1552
18.
Mohamed AAA, Mohamed YS, El-Gaafary AAM, Hemeida AM (2017) Optimal power flow using moth swarm algorithm. Electr Power Syst Res 142:190–206CrossRef
19.
Mukherjee A, Mukherjee V (2016) Solution of optimal power flow with FACTS devices using a novel oppositional krill herd algorithm. Int J Electr Power Energy Syst 78:700–714CrossRef
20.
Prasad D, Mukherjee V (2016) A novel symbiotic organisms search algorithm for optimal power flow of power system with FACTS devices. Eng Sci Technol Int J 19:79–89
21.
Basu M (2008) Optimal power flow with FACTS devices using differential evolution. Int J Electr Power Energy Syst 30:150–156CrossRef
22.
Rao BV, Kumar GVN (2015) Optimal power flow by BAT search algorithm for generation reallocation with unified power flow controller. Int J Electr Power Energy Syst 68:81–88CrossRef
23.
Edward JB, Rajasekar N, Sathiyasekar K, Senthilnathan N, Sarjila R (2013) An enhanced bacterial foraging algorithm approach for optimal power flow problem including FACTS devices considering system loadability. ISA Trans 52(5):622–628CrossRef
24.
Ongsakul W, Bhasaputra P (2002) Optimal power flow with FACTS devices by hybrid TS/SA approach. Int J Electr Power Energy Syst 24(10):851–857CrossRef
25.
Panda A, Tripathy M (2015) Security constrained optimal power flow solution of wind-thermal generation system using modified bacteria foraging algorithm. Energy 93:816–827CrossRef
26.
Shi L, Wang C, Yao L, Ni Y, Bazargan M (2012) Optimal power flow solution incorporating wind power. IEEE Syst J 6(2):233–241CrossRef
27.
Reddy SS (2018) Multi-objective optimal power flow for a thermal-wind-solar power system. J Green Eng 7(4):451–476CrossRef
28.
Roy R, Jadhav HT (2015) Optimal power flow solution of power system incorporating stochastic wind power using Gbest guided artificial bee colony algorithm. Int J Electr Power Energy Syst 64:562–578CrossRef
29.
30.
Yousri D, AbdelAty AM, Said LA, Elwakil AS, Maundy B, Radwan AG (2019) Chaotic flower pollination and grey wolf algorithms for parameter extraction of bio-impedance models. Appl Soft Comput J 75:750–774CrossRef
31.
Ewees AA, Abd El Aziz M, Hassanien AE (2019) Chaotic multi-verse optimizer-based feature selection. Neural Comput Appl 31(4):991–1006CrossRef
32.
33.
34.
Gandomi AH, Yang XS, Talatahari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18(1):89–98MathSciNetMATHCrossRef
35.
Mirjalili S, Gandomi AH (2017) Chaotic gravitational constants for the gravitational search algorithm. Appl Soft Comput 53:407–419CrossRef
36.
Mingjun J, Huanwen T (2004) Application of chaos in simulated annealing. Chaos Solut Fractals 21(4):933–941MATHCrossRef
37.
Alatas B (2010) Chaotic harmony search algorithms. Appl Math Comput 216(9):2687–2699MATH
38.
Adarsh BR, Raghunathan T, Jayabarathi T, Yang XS (2016) Economic dispatch using chaotic bat algorithm. Energy 96:666–675CrossRef
39.
Lu P, Zhou J, Zhang H, Zhang R, Wang C (2014) Chaotic differential bee colony optimization algorithm for dynamic economic dispatch problem with valve-point effects. Int J Electr Power Energy Syst 62:130–143CrossRef
40.
Secui DC (2015) A new modified artificial bee colony algorithm for the economic dispatch problem. Energy Convers Manag 89:43–62CrossRef
41.
Shayeghi H, Ghasemi A (2014) A modified artificial bee colony based on chaos theory for solving non-convex emission/economic dispatch. Energy Convers Manag 79:344–354CrossRef
42.
Mukherjee A, Mukherjee V (2016) Chaotic krill herd algorithm for optimal reactive power dispatch considering FACTS devices. Appl Soft Comput 44:163–190CrossRef
43.
Mirjalili S, Hashim SZM (2010) A new hybrid PSOGSA algorithm for function optimization. In: International conference on computer and information application (ICCIA 2010), pp 374–377
44.
Singh RP, Mukherjee V, Ghoshal SP (2015) Particle swarm optimization with an aging leader and challengers algorithm for optimal power flow problem with FACTS devices. Int J Electr Power Energy Syst 64:1185–1196CrossRef
45.
Prasad D, Mukherjee V (2018) Solution of optimal reactive power dispatch by symbiotic organism search algorithm incorporating FACTS devices. IETE J Res 64(1):149–160CrossRef
46.
Elmitwally A, Eladl A (2016) Planning of multi-type FACTS devices in restructured power systems with wind generation. Int J Electr Power Energy Syst 77:33–42CrossRef
47.
Banu RN, Devaraj D (2008) Genetic algorithm approach for optimal power flow with FACTS devices. In: 4th International IEEE conference intelligent systems
48.
Cai LJ, Erlich I, Stamtsis G (2004) Optimal choice and allocation of FACTS devices in deregulated electricity market using genetic algorithms. In: IEEE PES power systems conference and exposition
49.
Biswas PP, Suganthan PN, Amaratunga GAJ (2017) Optimal power flow solutions incorporating stochastic wind and solar power. Energy Convers Manag 148:1194–1207CrossRef
50.
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: International conference on neural network, pp 1942–1948
51.
Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248MATHCrossRef
52.
53.
54.
Ying-Qian Z, Xing-Yuan W (2014) A symmetric image encryption algorithm based on mixed linear-nonlinear coupled map lattice. Inf Sci 273:329–351CrossRef
55.
Ying-Qian Z, Xing-Yuan W (2015) A new image encryption algorithm based on non-adjacent coupled map lattices. Appl Soft Comput 26:10–20CrossRef
56.
Chaib AE, Bouchekara HREH, Mehasni R, Abido MA (2016) Optimal power flow with emission and non-smooth cost functions using backtracking search optimization algorithm. Int J Electr Power Energy Syst 81:64–77CrossRef
57.
58.
59.
Liang JJ, Suganthan PN, Deb K (2005) Novel composition test functions for numerical global optimization. In: Proceedings 2005 IEEE swarm intelligence symposium (SIS 2005)
60.
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRef
61.
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67CrossRef
62.
Zimmerman RD, Murillo-Sanchez CE, Thomas RJ (2011) MATPOWER: steady-state operations, planning, and analysis tools for power systems research and education. IEEE Trans Power Syst 26(1):12–19CrossRef
63.
64.
Derrac J, Garcia S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1(1):3–18CrossRef
65.
Gibbons JD, Chakraborti S (2010) Nonparametric statistical inference, 5th edn. Chapman & Hall, LondonMATHCrossRef
66.
Buch H, Trivedi IN, Jangir P (2017) Moth flame optimization to solve optimal power flow with non-parametric statistical evaluation validation. Cogent Eng 4(1):1–22CrossRef
Metadaten
Titel
Optimal power flow with stochastic wind power and FACTS devices: a modified hybrid PSOGSA with chaotic maps approach
verfasst von
Serhat Duman
Jie Li
Lei Wu
Ugur Guvenc
Publikationsdatum
11.07.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 12/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04338-y

Weitere Artikel der Ausgabe 12/2020

Neural Computing and Applications 12/2020 Zur Ausgabe

Soft Computing Techniques: Applications and Challenges

A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wireless sensor networks

Original Article

Exploiting plaintext-related mechanism for secure color image encryption

Original Article

Dangerous goods detection based on transfer learning in X-ray images

Original Article

Genetic programming-assisted multi-scale optimization for multi-objective dynamic performance of laminated composites: the advantage of more elementary-level analyses

Original Article

Graph constraint-based robust latent space low-rank and sparse subspace clustering

Original Article

Ultra-low-voltage integrable electronic implementation of delayed inertial neural networks for complex dynamical behavior using multiple activation functions

Premium Partner

    Bildnachweise