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Real-time realization of network integration of electric vehicles with a unique balancing strategy

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Abstract

This article introduces a unique dynamic balance strategy (DBS) that can overcome many disadvantages such as voltage and frequency deviations, overloading, high infrastructure costs that may occur in the network integration of electric vehicles (EVs). The proposed method is also aimed at keeping the comfort and satisfaction of the EV users at a high level without the obligation of the EV owners to inform the operator of the entry and charge planning. DBS introduces a real-time energy-sharing method that uses both the grid-to-vehicle (G2V) and vehicle-to-vehicle (V2V) topologies simultaneously without knowing the power demand profile in advance. However, from the perspective of smart grid, it is also possible to activate the vehicle-to-grid (V2G) topology in case of frequency or voltage fluctuations in the network with the proposed DBS. The proposed DBS was simulated in real time and phasor analysis for different penetration levels in MATLAB/Simulink program. Considering that EV driver behavior is highly variable and will vary regionally, it is shown that the proposed DBS gives dynamic results against all behavioral situations, varying penetration levels, and all the negativities that may occur in the penetration of EVs are overcome by keeping the satisfaction of EV drivers at a high levels.

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Abbreviations

V2G:

Vehicle-to-grid

G2V:

Grid-to-vehicle

V2V:

Vehicle-to-vehicle

DBS:

Dynamic balance strategy

BEMS:

Building energy management system

EMS:

Energy management system

EV:

Electrical vehicle

SOCEV :

Electrical vehicle state-of-charge

SOCS :

Storage state-of-charge

SOCB :

Balance point state-of-charge

SOCtar :

Target state-of-charge

SOCf :

Departure time state-of-charge

SOCin :

Initial state-of-charge

EPA:

Energy production amount

ECA:

Energy consumption amount

EBS:

Energy balance status

SEPA:

Storage energy production amount

C PJ :

Battery capacity for j. EV

C PB :

Storage capacity

TE:

Total energy

P T :

Total power

P PV :

Instant PV power production

P ID :

Grid instant power demand

P EVJ :

Instant power demand for j. EV

P S :

Storage power production and demand

T S :

Balance time value

T D :

Balance time value produc. > consump.

k c :

Critical Load Index

k f :

Storage Usage Index

I Cmax :

Maximum charging current

I Dmax :

Maximum discharge current

IrefEVJ :

Reference current value for j.EV

IrefS :

Reference current value for storage

V EVJ :

Battery voltage value for j.EV

V S :

Voltage value for storage

E EVtotalJ :

Continuous charge–discharge energy total for 24 h for j.EV

E EVdJ :

Daily energy change total for j.EV

E CJ :

Battery instant energy status for j.EV

P CJ (t):

Instant charging power value for j.EV

P DJ (t):

Instant discharge power value for j.EV

t cj :

Total charging time per day for j.EV

t dj :

Total discharge time per day for j.EV

T H :

Charging time with max. charging cur.

T L :

Discharge time with max. charging cur.

µ j :

Daily Usage Frequency Index for j.EV

µ av :

Average Usage Frequency Index for j.EV

f k :

EV Frequency Index

P crt :

Grid critical power value

P c :

Calibration constant

δ :

Deviation

α :

Satisfactions parameter coefficient

S J :

Satisfaction value for j. EV

S T :

Total satisfaction value

S AV :

Average satisfaction value

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Authors and Affiliations

  1. Institute of Natural and Applied Sciences, Gazi University, Ankara, Turkey

    Furkan Üstünsoy

  2. Faculty of Technology, Gazi University, Ankara, Turkey

    H. Hüseyin Sayan

Authors
  1. Furkan Üstünsoy
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  2. H. Hüseyin Sayan
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Correspondence to Furkan Üstünsoy.

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Üstünsoy, F., Sayan, H.H. Real-time realization of network integration of electric vehicles with a unique balancing strategy. Electr Eng 103, 2647–2660 (2021). https://doi.org/10.1007/s00202-021-01259-9

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  • DOI: https://doi.org/10.1007/s00202-021-01259-9

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