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
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Research on Wind Speed Vertical Extrapolation Based on Extreme Learning Machine Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Short-Term Optimal Scheduling with the Consideration of Electric Vehicle Driving Rules

verfasst von : Xiaolin Ge, Chenhao Pei

Erschienen in: Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration

Verlag: Springer Singapore

Einloggen

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

search-config
loading …

Abstract

Taking into account the daily driving rules of electric vehicle (EV), a novel short-term optimal scheduling model is proposed. And to describe the driving characteristics of the EV, EVs are divided into four types according to the detailed driving rules. And the stochastic driving time, access time and daily mileage of different types of EVs are simulated by a large number of scenes. Besides, the interaction between the EV and the power systems is added to establish the coupling between the output of units, and the operation of EVs. Due to the complexity of the constraints, the stochastic nonlinear unit commitment model is converted into mixed integer linear programming problem and solved by CPLEX. Case studies show the necessity of considering the stochastic driving rules of EVs, and the classification of EVs can make the dispatching decision more reasonable.

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

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

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"

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
Vorheriges Kapitel Neural-Network-Based Tracking Control of Offshore Steel Jacket Platforms
Nächstes Kapitel Dispatching Analysis of Ordered Charging Considering the Randomness Factor of Electric Vehicles Charging
Literatur
1.
Luo, Z., Hu, Z., Song, Y., et al.: Study on plug-in electric vehicles charging load calculating. Autom. Electr. Power Syst. 35(14), 36–42 (2011)
2.
Jie, D., Yi, T., Jia, N.: A strategy for distribution of electric vehicles charging load based on user intention and trip rule. Power Syst. Prot. Control 43(16), 8–15 (2015)
3.
Xiaolin, G., Lizi, Z.: Wind-hydro-thermal stochastic unit commitment problem considering the peak regulation constraints. Trans. China Electrotech. Soc. 29(10), 222–230 (2014)
4.
Xue, S., Yuan, Y., Fu, Z.X., et al.: Unit commitment in power system considering vehicle-to-grid. Power Syst. Prot. Control 41(10), 86–92 (2013)
5.
Lu, L., Wen, F., Ledwich, G., et al.: Unit commitment in power systems with plug-in hybrid electric vehicles. Autom. Electr. Power Syst. 23(7), 1205–1220 (2011)
6.
Zhang, S., Zechun, H.U., Song, Y., et al.: Research on unit commitment considering interaction between battery swapping station and power grid. Proc. CSEE 32(10), 49–55 (2012)
7.
Shao, C., Wang, X., Wang, X., et al.: Hierarchical charge control of large populations of EVs. IEEE Trans. Smart Grid 7(2), 1 (2015)
8.
Zakariazadeh, A., Jadid, S., Siano, P.: Multi-objective scheduling of electric vehicles in smart distribution system. Energy Convers. Manag. 79(3), 43–53 (2014)CrossRef
9.
Saber A.Y., Venayagamoorthy, G.K.: Optimization of vehicle-to-grid scheduling in constrained parking lots. In: Power & Energy Society General Meeting, 2009. PES 2009. IEEE, pp. 1–8. IEEE (2009)
10.
Ke, W.U., Wang, C., Sun, H., et al.: Unit commitment in power systems considering large-scale electric vehicles. In: Electric Power Construction (2014)
11.
Zhang, X., Juping, G.U., Yue, Y., et al.: Optimum electric vehicle charging model and its approximate solving technique. Proc. CSEE 34(19), 3148–3155 (2014)
12.
Shao, C., Wang, X., Wang, X., et al.: Cooperative dispatch of wind generation and electric vehicles with battery storage capacity constraints in SCUC. IEEE Tran. Smart Grid 5(5), 2219–2226 (2014)CrossRef
13.
Gaowang, L.I., Qian, B., Shi, D., et al.: Unit commitment problem considering plug-in hybrid electric vehicle. Power Syst. Technol. 37(1), 32–38 (2013)
14.
Tushar, M.H.K., Assi, C., Maier, M., et al.: Smart microgrids: optimal joint scheduling for electric vehicles and home appliances. IEEE Trans. Smart Grid 5(1), 239–250 (2014)CrossRef
15.
Taylor, J., Maitra, A., Alexander, M. et al.: Evaluation of the impact of plug-in electric vehicle loading on distribution system operations. In: Power & Energy Society General Meeting, 2009. PES 2009, pp. 1–6. IEEE. IEEE Xplore (2009)
16.
Luo, Z., Hu, Z., Song, Y., et al.: Study on plug-in electric vehicles charging load calculating. Autom. Electr. Power Syst. 35(14), 36–42 (2011)
17.
Wang, Q.: Research on electric vehicles orderly charging and discharging considering unit commitment and comprehensive benefits. In: North China Electric Power University (2016)
18.
Carrión, M., Arroyo, J.M.: A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Trans. Power Syst. 21(3), 1371–1378 (2006)CrossRef
19.
Li, T., Shahidehpour, M., Li, Z.: Risk-constrained bidding strategy with stochastic unit commitment. IEEE Trans. Power Syst. 22(1), 449–458 (2007)CrossRef
20.
Ge, X., Zhang, L.: Wind-hydro-thermal stochastic unit commitment problem considering the peak regulation constraints. Trans. China Electrotech. Soc. 29(10), 222–230 (2014)
21.
We, L., Shahidehpour, M., Li, T.: Stochastic security-constrained unit commitment. IEEE Trans. Power Syst. 22(2), 800–811 (2007)CrossRef
Metadaten
Titel
Short-Term Optimal Scheduling with the Consideration of Electric Vehicle Driving Rules
verfasst von
Xiaolin Ge
Chenhao Pei
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration

Print ISBN: 978-981-10-6363-3

Electronic ISBN: 978-981-10-6364-0

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-6364-0_30