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 6/2020

06.03.2019 | Deep Learning for Big Data Analytics

Adjustable loads control and stochastic stability analysis for multi-energy generation system based on Markov model

verfasst von: Suwei Zhai, Yonghui Sun, Hantao Cui, Yinlong Hu, Zhihua Li

Erschienen in: Neural Computing and Applications | Ausgabe 6/2020

Einloggen

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

search-config
loading …

Abstract

This paper mainly addresses the issue of the power generation fluctuations due to the stochastic characteristics of the renewable energies, which threaten the stability of the power grid. First, the multi-energy generation system with integration of wind and photovoltaic power is modeled in the framework of Markov model to describe the dynamic changes. Second, to weaken the active power fluctuation of renewable energies generation and improve the utilization of renewable energies, the flexible loads are added for local consumption and an adjustable load control strategy is proposed to guarantee the output power continuous stability of the renewable energies generation. Then, the stochastic stability is analyzed by using Markov stability theory. Finally, the simulation results and analysis are provided to illustrate the effectiveness of the proposed schemes.
Vorheriger Artikel Special issue on deep learning-based neural information processing for big data analytics
Nächster Artikel An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments

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.
Wei S, Zhou Y, Huang Y (2017) Synchronous motor generator pair to enhance small signal and transient stability of power system with high penetration of renewable energy. IEEE Access 5:11505–11512
2.
Bapadopoulos P, Milanovic J (2017) Probabilistic framework for transient stability assessment of power systems with high penetration of renewable generation. IEEE Trans Power Syst 32(4):3078–3088
3.
Saravia H, Painemal H, Mauricio J (2017) Flywheel energy storage model, control and location for improving stability: The Chilean case. IEEE Trans Power Syst 32(4):3111–3119
4.
Mohammad F et al (2018) Review of energy storage system technologies in microgrid applications: issues and challenges. IEEE Access 6:35143–35164
5.
Yuan B, Zhou M, Li G, Zhang X (2015) Stochastic small signal stability of power systems with wind power generation. IEEE Trans Power Syst 30(4):1680–1689
6.
Liu S, Liu P, Wang X (2016) Stochastic small signal stability analysis of grid-connected photovoltaic systems. IEEE Trans Ind Electron 63(2):1027–1038
7.
Pan Y et al (2016) Admissible region of large scale uncertain wind generation considering small signal stability of power systems. IEEE Trans Sustain Energy 7(4):1611–1623MathSciNet
8.
Wang Z, Shen C, Liu F (2016) Probabilistic analysis of small signal stability for power systems with high penetration of wind generation. IEEE Trans Sustain Energy 7(3):1182–1193
9.
Stadtmann F, Costa O (2017) H2-control of continuous time hidden Markov jump linear system. IEEE Trans Autom Control 62(8):4031–4037MATH
10.
Arrifano N et al (2007) Fuzzy stabilization of power systems in a co-generation scheme subject to random abrupt variations of operating conditions. IEEE Trans Control Syst Technol 15(2):384–393
11.
Liu S, Liu P, Wang X (2016) A stochastic stability enhancement method of grid connected distributed energy storage system. IEEE Trans Smart Grid 8(15):2062–2070
12.
Kienzle F, Ahcin P, Andersson G (2011) Valuing investments in multi-energy conversion, storage and demand side management systems under uncertainty. IEEE Trans Sustain Energy 2(2):194–201
13.
Clegg S, Mancaraella P (2016) Integrated electrical and gas network flexibility assessment in low carbon multi-energy systems. IEEE Trans Sustain Energy 7(2):718–731
14.
Tan J, Zhang Y (2017) Coordinated control strategy of a battery energy storage system to support a wind power plant providing multi times scale frequency ancillary services. IEEE Trans Sustain Energy 8(3):1140–1153
15.
Sajjad I, Chicco G, Napoil R (2016) Definitions of demand flexibility for aggregate residential loads. IEEE Trans Smart Grid 7(6):2633–2643
16.
Heleno M, Matos M, Lopes J (2015) Availability and flexibility of loads for the provision of reserve. IEEE Trans Smart Grid 6(2):667–674
17.
Sanandaji B, Vincent T, Poolla K (2016) Ramping rate flexibility of residential HVAC loads. IEEE Trans Sustain Energy 7(2):865–874
18.
Escobar G, Pettersson S, Manho C, Camacho R (2017) Multisampling maximum power point tracker to compensate irradiance and temperature changes. IEEE Trans Sustain Energy 8(3):1096–1105
19.
Kumar N, Hussain I, Singh B, Panigrahi B (2017) MPPT in dynamic condition of partially shaded PV system by using WODE technique. IEEE Trans Sustain Energy 8(3):1204–1214
20.
Li H, Zhao S, He W, Lu R (2019) Adaptive finite-time tracking control of full states constrained nonlinear systems with dead-zone. Automatica 100:99–107MathSciNetMATH
21.
Villalva M, Gazoli J, Filho E (2009) Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Trans Power Electron 24(5):1198–1208
22.
Hejri M, Mokhtari H (2017) On the comprehensive parametrization of the photovoltaic cells and modules. IEEE J Photovolt 7(7):250–258
23.
Nishimura S et al (2016) Over 21% efficiency of n-type monocrystalline silicon PERT photovoltaic cell with Boron emitter. IEEE J Photovolt 6(4):846–851
24.
Lasheen M, Rahman A, Salam M, Okawara S (2017) Adaptive reference voltage based MPPT technique for PV applications. IET Renew Power Gener 11(5):715–722
25.
Parida A, Chatterjee D (2016) Cogeneration topology for wind energy conversion system using doubly fed induction generator. IET Power Electron 9(7):1406–1415
26.
Parida A, Chatterjee D (2016) Model based loss minimisation scheme for wind solar hybrid generation system using doubly fed induction generator. IET Power Appl 10(6):548–559
27.
Naidu N, Singh B (2016) Experimental implementation of doubly fed induction generator based standalone wind energy conversion system. IEEE Trans Ind Electron 52(4):3332–3339
28.
Evangelisa C, Pisano A, Puleston P, Usai E (2017) Receding horizon adaptive second order sliding mode control for doubly fed induction generator based wind turbine. IEEE Trans Control Syst Technol 25(1):73–84
29.
Kheshti M, Kang X, Song G, Jiao Z (2017) Modeling and fault analysis of doubly fed induction generators for Gansu wind farm application. Can J Electr Comput Eng 38(1):52–64
30.
Chen J, Zhang W, Chen B, Ma Y (2016) Improved vector control of brushless doubly fed induction generator under unbalanced grid conditions for offshore wind power generation. IEEE Trans Energy Convers 31(1):293–302
31.
Hossain M, Ali M (2016) Transient stability improvement of doubly fed induction generator based variable speed wind generator using dc resistive fault current limiter. IET Renew Power Gener 10(2):150–157
32.
Xie K, Liao Q, Tai H, Hu B (2017) Non homogeneous Markov wind speed time series model considering daily and seasonal variation characteristics. IEEE Trans Sustain Energy 8(3):1281–1290
33.
Li H, Wang Y, Yao D, Lu R (2018) A sliding mode approach to stabilization of nonlinear Markovian jump singularly perturbed systems. Automatica 97:404–413MathSciNetMATH
34.
Lai C, Jia Y, Culloch M, Xu Z (2017) Daily clearness index profiles cluster analysis for photovoltaic system. IEEE Trans Ind Inform 13(5):2322–2332
35.
Chen P, Berthelsen K, Jensen B, Chen Z (2009) Markov model of wind power time series using Bayesian inference of transition matrix. In: Proceedings of 35th annual conference of IEEE industrial electronics, pp 627–632
36.
Lindberg D, Omre H (2015) Inference of the transition matrix in convolved hidden Markov models and the generalized Baum Welch algorithm. IEEE Trans Geosci Remote Sens 53(12):6443–6456
37.
Chen J, Lin C, Chen B, Wang Q (2016) Output feedback control for singular Markovian jump systems with uncertain transition rates. IET Control Theory Appl 10(16):2142–2147MathSciNet
38.
Qian W, Wang L, Machel Z, Chen Q (2018) Local consensus of nonlinear multiagent systems with varying delay coupling. IEEE Trans Syst Man Cybern Syst 48(12):2462–2469
39.
Shubert B (1975) A flow graph formula for the stationary distribution of a Markov chain. IEEE Trans Syst Man Cybern 5(5):565–566MathSciNetMATH
40.
Ji H, Zhang H, Cui B (2017) Containment analysis of Markov jump swarm systems with stationary distribution. IET Control Theory Appl 11(7):901–907MathSciNet
41.
Dehghani S, Abbasian S, Johnson T (2016) Adjustable load with tracking loop to improve RF rectifier efficiency under variable RF input power conditions. IEEE Trans Microw Theory Technol 64(2):343–352
42.
Niknam T, Frad A, Ostadi M (2015) Impact of hydrogen production and thermal energy recovery of PEMFCPPs on optimal management of renewable microgrids. IEEE Trans Ind Inform 11(5):1190–1197
Metadaten
Titel
Adjustable loads control and stochastic stability analysis for multi-energy generation system based on Markov model
verfasst von
Suwei Zhai
Yonghui Sun
Hantao Cui
Yinlong Hu
Zhihua Li
Publikationsdatum
06.03.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 6/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04120-0

Weitere Artikel der Ausgabe 6/2020

Neural Computing and Applications 6/2020 Zur Ausgabe

Deep Learning for Big Data Analytics

Research on path planning of mobile robot based on improved ant colony algorithm

Deep Learning for Big Data Analytics

Stock intelligent investment strategy based on support vector machine parameter optimization algorithm

Deep Learning for Big Data Analytics

First arrivals picking based on graph signal theory

Original Article

Dynamic hardware system for cascade SVM classification of melanoma

Deep Learning for Big Data Analytics

Research on supply chain partner selection method based on BP neural network

Deep Learning for Big Data Analytics

Spatiotemporal analysis of trajectory for a new real-time bus routes updated model

Premium Partner

    Bildnachweise