Deploying stochastic coordination of electric vehicles for V2G services with wind

Deploying stochastic coordination of electric vehicles for V2G services with wind

For access to this article, please select a purchase option:

Buy chapter PDF
(plus tax if applicable)
Buy Knowledge Pack
10 chapters for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
ICT for Electric Vehicle Integration with the Smart Grid — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In this work, we evaluate the effect of charging and discharging plan on integration of electric vehicles (EVs) with wind penetrated distribution grid. A probabilistic model is established by considering the stochastic environment of wind and the accessibility of network constraints. Next, a methodology based on fuzzy approach is applied to model the load profile of EVs with the stochastic availability of EVs, that is, arrival and departure times. Based on this stochastic load profile of EVs, particle swarm optimization with evolution strategy (ESPSO) procedure is applied to incorporate EVs into the distribution grid. Peak load shaving is discussed with vehicle to grid (V2G), having considered the EV charging cost, degradation cost of EV battery, and frequency regulation incomes along with spinning reserve earnings in single objective function. The study is further extended with a varied range of EV penetration in the distribution network

Chapter Contents:

  • Notation
  • 13.1 Introduction
  • 13.2 Test system
  • 13.3 Probabilistic model of wind power
  • 13.4 Stochastic load modeling of EV
  • 13.4.1 Stochastic adaptive fuzzy model of EVs
  • 13.4.2 Charging level and type of EVs
  • 13.4.3 Initial SOC and EVs load profile
  • 13.5 Financial and operational modeling
  • 13.5.1 Real-time pricing policy
  • 13.5.2 Degradation cost of EVs battery
  • 13.5.3 Frequency regulation
  • 13.5.4 Spinning reserves
  • 13.6 Formulation of charging/discharging strategy
  • 13.6.1 Charging/discharging energy of EVs
  • 13.6.2 Optimizing strategy and function
  • 13.6.3 ESPSO
  • 13.7 Case studies and discussion
  • 13.7.1 Comparison of load profiles of EVs on different modeling schemes
  • 13.7.2 Impact of wind and EV penetration
  • 13.8 Conclusion
  • References

Inspec keywords: battery powered vehicles; stochastic processes; power distribution economics; particle swarm optimisation; power distribution planning; power generation economics; vehicle-to-grid; load (electric); wind power plants; probability; power generation planning; fuzzy set theory; electric vehicle charging

Other keywords: charging-discharging plan; Peak load shaving; spinning reserve earnings; ESPSO; probabilistic model; wind penetrated distribution grid; EV battery degradation cost; V2G services; distribution network; EV charging cost; wind stochastic environment; particle swarm optimization with evolution strategy procedure; fuzzy approach; vehicle to grid; frequency regulation incomes; EV stochastic load profile; electric vehicles stochastic coordination

Subjects: Transportation; Combinatorial mathematics; Optimisation techniques; Distribution networks; Power system planning and layout; Other topics in statistics; Power system management, operation and economics; Wind power plants

Preview this chapter:
Zoom in

Deploying stochastic coordination of electric vehicles for V2G services with wind, Page 1 of 2

| /docserver/preview/fulltext/books/tr/pbtr016e/PBTR016E_ch13-1.gif /docserver/preview/fulltext/books/tr/pbtr016e/PBTR016E_ch13-2.gif

Related content

This is a required field
Please enter a valid email address