Skip to main content

Optimal Allocation of Electric Vehicles Charging Station in Distribution Network Beside DG Using TSO

  • Chapter
  • First Online:
Planning of Hybrid Renewable Energy Systems, Electric Vehicles and Microgrid

Abstract

Increasingly, there is growth in electric cars globally, and it will keep rising owing to increasing knowledge and interest on the part of people, all while considering the significant environmental and financial effects. Installation in the distribution system of rapid electric vehicle charging stations that meet the increasing charging demand for electric vehicles. Although fast-charging stations are placed in the distribution system, the implementation of these stations leads to adverse effects such as higher power loss and a more inferior voltage profile. To minimize these adverse effects, one must strategically locate charging stations and allocate dispersed generation appropriately across the distribution system. In the article, the negative impacts of charging stations on radial distribution systems and the positive impacts of distributed generation on unbalanced systems to balance single and multiple distributed generation to reduce active and reactive power loss and enhance voltage stability were evaluated for IEEE-25 unbalanced radial distribution systems. The distribution system with sufficient active and reactive power injection has the distributed generation with unity, fixed, and optimum power factor assigned to enhance it. Various optimization strategies were proposed for EVCS allocation, but a novel physics-based meta-heuristic algorithm, Transient search optimization (TSO), which had just been created, was used for multi-objective functions. Based on the modeling findings, the voltage profile improved, and power loss was reduced in every scenario. The convergence features have emerged regarding the new algorithm recently created to coordinate all scenarios with the findings of the outcomes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

ANN:

Artificial neural network

APL:

Active power loss

CO2:

Carbon dioxide

CRAE:

Correlation attribute evaluation

DG:

Distributed generation

EVCS:

Electric vehicles charging stations

EVs:

Electric vehicles

EVSE:

Electric vehicle charging station expenditure

GA:

Genetic algorithm

PSO:

Particle swarm optimization

RAPL:

Reduction in active power loss

TAPL:

Total active power loss

TSO:

Transient search optimization

URDS:

Unbalanced radial distribution system

References

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bhadoriya, J.S., Gupta, A.R., Zellagui, M., Saxena, N.K., Arya, A.K., Bohre, A.K. (2022). Optimal Allocation of Electric Vehicles Charging Station in Distribution Network Beside DG Using TSO. In: Bohre, A.K., Chaturvedi, P., Kolhe, M.L., Singh, S.N. (eds) Planning of Hybrid Renewable Energy Systems, Electric Vehicles and Microgrid. Energy Systems in Electrical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-19-0979-5_29

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-0979-5_29

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-0978-8

  • Online ISBN: 978-981-19-0979-5

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics