2. Implementation of Vehicle to Grid using Fuzzy Logic Controller
δ = 0 in equations (2.4) and (2.5), the active and reactive power at the sending end becomes as follows:
PEV = 0 (2.6)
QEV = E2−EV
X (2.7)
Now if only real power injection by the EV is required, angleδ will be made equal to 90 degree.
Substituting δ = 90 degree in equations (2.4) and (2.5), the active and reactive power at the sending end becomes as follows:
QEV = E2
X (2.8)
Hence, EVs with the help of converters can support active as well as reactive power or a combination of both by selecting a desired value of angleδ. The converter will be controlled in such a way that the phase angle between E and V is always zero so as to inject only reactive power for low voltage correction. For large amounts of voltage correction, the angle has to be adjusted in order to inject real as well as reactive power at the concerned node. Since the aim of this work is to present the concept of grid support by injecting only real power, a power factor of 0.9 is assumed for all cases of charging and discharging systems.
Thus, the EVs at the charging station will be discharging during peak hours to improve voltage level of the grid and charging during the off-peak hours of the day. During the charging phase, the EVs’ batteries will consume power from the grid system while the power consumption will become zero when the battery system is fully charged.
2.3 V2G and its impact on the radial distribution net-
2.3 V2G and its impact on the radial distribution network
the loads at respective node. Vi is the voltage of bus i, Pi and Qi are the real and reactive power flows in line i. PLi and QLi are the loads at bus i. The batteries of the EVs, behaving as a distributed energy resource (DER), are connected at bus i via a charging station. This charging station injects real power PEV and reactive power QEV. The equation for voltage at (i+ 1)th bus can be obtained as [44],
i−1 i
0 i+1 n
Si
−1
SLi+1
Utility Si
SLn SLi
−1 SLi
S0 Si+1 Sn
PEV +jQEV
Figure 2.4: A Radial System.
Vi2+1 =Vi2−2(Piri+Qixi) + (Pi2+Q2i
Vi2 )(ri2+x2i) (2.9) where ri and xi are the resistance and reactance of the ith line respectively. Vi is the voltage of the ith node. From Figure 2.4, active power and reactive power at (i+ 1)th node can be calculated with the help of forward branch equation [45].
Pi+1 =Pi−PLi+1−Plossi
Qi+1 =Qi−QLi+1−Qlossi
(2.10)
In above equations, Pi and Qi includes the PEV and QEV which are the active and reactive power supplied by the charging station.
The real and reactive power losses for line i are
Plossi =Pi+1+PLi+1−Pi (2.11)
Qlossi =Qi+1+QLi+1−Qi (2.12)
As PEV and QEV which are the active and reactive power supplied by the charging station is included in the Pi and Qi of Eq. (2.11) and Eq. (2.12), the real and reactive power loss will reduce.
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2. Implementation of Vehicle to Grid using Fuzzy Logic Controller
The real and reactive power loss can also be written as follows.
PLossi= (Pi2+Q2i
Vi2 )ri (2.13)
QLossi = (Pi2+Q2i
Vi2 )xi (2.14)
For example, let PLi+1 is 150 kW, Pi+1 is 350 kW and Pi without inclusion of PEV is say 450 kW. Then the PLossi is 50 kW. If there is PEV of 40 kW which is included in Eq. (2.11), PLossi will be 10 kW. Therefore, it can be proved that the real power loss can be reduced with the injection of power by the EVs batteries.
Voltage rise caused by injection of EV’s energy to the ith node is approximated by the following equation [46].
∆VEV = PEVri+QEVxi
Vi
(2.15) As explained in the above example, power loss will reduce due to injection of real power into the grid. However, injection of real and reactive power if not controlled, node voltage will rise which can be observed in Eq. 2.15.
From Eq. (2.11), Eq. (2.12) and Eq. (2.15), it is found that the power injection by EVs’
battery reduces power losses and improves node voltage of the network. This is due to the fact that power flow in the transmission and distribution systems is reduced, as EV battery generates power locally to fulfill demand. This reduction in power losses is one of the main features of V2G . Other benefits of V2G are peak demand management, voltage stability and reactive support. The main work of this chapter is to utilize the V2G for voltage support and peak demand management using Fuzzy Logic Controller (FLC).
2.3.1 Need for coordination of EVs batteries at the CS
It is observed in the previous section that by selecting the proper value of angle δ, real and reactive power can be injected to the node. The real and reactive power losses get reduced due to injection of power from the battery to theith node. It implies that the control of power flow
2.3 V2G and its impact on the radial distribution network
between the battery and the grid needs to be control. In the case of a single battery, the power flow can be easily control by changing the proper value of angle δ. However, if a large number of EVs are present in a CS, proper coordination among the EVs is a must to achieve the desired power flow. Moreover, EVs which will arrive at the CS, may have different energy ratings and initial state of charge (SOC). Therefore, a suitable algorithm has to be developed so that it can handle different types of EVs’ batteries. Also, the proper control techniques have to be used which can control the charging/discharging rate of EV’s battery. Injection of large power to the node may lead to voltage rise and over-drawal of large power to charge the battery may lead to voltage collapse. Hence, a proper control techniques needs to be developed which takes a grid condition such as voltage of the distribution node in to account while charging/discharging.
In this chapter, the proper control techniques has been developed to control the power flow between EVs and the grid.
2.3.2 Assumption
Following assumptions have been made in this work:
• V2G implementation using FLC has been designed at a system level. Due to large dy- namics involved in the distribution system, converters connected to charge and discharge the EVs’ batteries have not been modeled.
• Efficiencies of the batteries, charging system and converters have not been considered since the aim of this work is to coordinate the EVs at the CS to provide the grid support.
• Generally, the batteries of EVs ranges from 8 kWh to 40 kWh depending on the type of EVs [47]. In this chapter, three types of EVs are assumed to be present at the charging station with a battery energy of 10 kWh, 16 kWh and 20 kWh respectively. Assumed energy ratings are the standard battery ratings which have been used in the EVs.
• Total number of EVs are assumed to be 100 at the charging station. This assumption is based on the maximum power transfer capability at the desired node (point of common coupling).
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2. Implementation of Vehicle to Grid using Fuzzy Logic Controller