Ebij Available energy from the jth battery of the ide CS Eavailcom Energy available after a certain commuting distance. SOCrem Difference between current SOC (SOCcr) and SOC limit (SOClt) SOCinitial SOC at the time of starting from home/office.
Introduction
Frequency regulation is used to fine-tune the grid's frequency by adapting the production to the load demand. The interconnection of electric vehicles to the grid is sometimes referred to as distributed energy storage.
Literature review
Use of EVs for frequency control has been discussed by developing an optimal aggregator [16,17]. In this paper, two approaches (static and dynamic) were discussed to demonstrate the unpredictable mobility of EVs.
Motivation
In this thesis, mainly the voltage regulation at the distribution network was achieved by coordinating the EVs. The presence of EVs at the charging stations (CSs) is more likely because 90% of the time EVs will be idle [41].
Aim of the thesis
The proper distribution of power among the EVs present in a CS is a challenging goal, since the EVs present in the CS will have different battery specifications and different states of charge (SOC). In this figure, grid refers to the node of a distribution substation, where a residential or commercial area exists and there is a possibility that a large number of EVs are present in this area.
Contributions
Thesis layout
Power distribution between CSs of the MCS is achieved by designing a suitable aggregator. A real-time scenario of EV movement in MCS with intra-nodal driving is considered.
Modeling of the V2G system
A typical distribution system
The analysis of charging and discharging of electric vehicles using FLC and its positive impact on improving the voltage profile and meeting peak demand are discussed in Section 2.6. This section also presents the load profile smoothing based on the results discussed in the previous section.
EV’s battery
Due to these assumptions, the battery is modeled as a source capable of delivering the required power during discharge. In the case of charging, the battery is modeled as a sink that can absorb power from the network.
EVs utilization for grid support
Due to variation in the voltage with respect to the SOC, the power drawn from/by the battery also changes, which affects the total energy exchange between the grid and the CS. During the charging phase, the EV batteries consume power from the grid, while power consumption becomes zero when the battery system is fully charged.
V2G and its impact on the radial distribution network
Need for coordination of EVs batteries at the CS
In the case of a single battery, the power flow can be easily controlled by changing the right value of angle δ. In this chapter, the proper control techniques are developed to control the power flow between EVs and the grid.
Assumption
Also, the right control techniques must be used which can control the charge/discharge rate of the EV's battery. Therefore, a proper control technique must be developed which takes into account a grid condition, such as voltage at the distribution node, during charging/discharging.
V2G infrastructure based on a Fuzzy Logic Controller
Fundamentals of Fuzzy Logic Controller
The “fuzzy centroid” of the composite region of the output membership function is then calculated to obtain a binary output value [ 49 , 50 ].
Design of Fuzzy Controllers for V2G
Now 250 kWh of energy as decided by CSC will be one of the inputs to the V2G controller. This updated CS energy information and current node voltage will be the input to the V2G controller.
Fuzzy membership functions for V2G and CS controller
The CSC controller decides that only 250 kWh of negative energy can be sent to the grid. This input energy information will be updated every moment with the amount of energy supplied to the grid using the integrator.
Possible scenarios of EVs at charging station
If the energy rating of the battery is 10 kWh, the energy required to charge 25 EVs of similar rating during peak times will be 175 kWh. The following equations can also be used for calculating the total energy required for charging and the total energy of EVs available during discharge:.
Analysis of charging and discharging
Discharging of EVs’ energy to the grid
During peak hours, the node voltage before EVs discharge energy into the grid is found to be 0.93 p.u. From the simulation results, it was found that the net power discharged at the node is 104 kW and is shown in figure 2.14.
Charging of EVs from the grid
This implies that the controller has allowed more EVs to charge during off-peak hours as it has sensed the high node voltage during these periods. In this table, it is found that CS requires 435 kWh of power to charge their batteries and from figure 2.18, it can be seen that 60 kW of power is drawn from CS.
Discussion of results
Discharging energy to the grid
Analysis of the first scenario can be validated from Figure 2.14, where power discharge to the node during peak times is 104 kW. Analysis of the second scenario can be validated with the controller output shown in Figure 2.18, where power discharge to the node is found to be 70 kW.
Charging of EVs from the grid
From Figure 2.22 we see that 60 kW of power was consumed during peak hours to charge the EVs. The selection of the suitable charging scheme from these different options is done based on the results obtained from Figure 2.24, where the power consumed by the charging station appears to be 170 kW.
Flattening of load profile
In the figure, load leveling for the peak and off-peak hours is shown, only for the simulated time. The charging and discharging of EVs for such a scenario can be done in slots at the charging station.
Summary
In the next chapter, the coordination of EVs at the charging station to achieve the desired power flow is presented. The accumulated energy of the EVs is assumed in the CS to show the control aspects of the energy flow between the CS and the grid.
Modelling of the system
Multi charging station
The chapter is organized as follows: Section 3.2 discusses the modeling of the MCS, the distribution network and the battery. This is calculated based on SOC limit, SOC, AHR and battery power demand (P b11).
Battery
The current box is the final charge/discharge rate at which power will flow between the grid and the battery. The batteries are charged when the node voltage is high and the batteries are discharged when the node voltage is low, taking into account their SOC.
Distribution network
However, the charge/discharge efficiency of the system was assumed to be 90% to confirm any effectiveness of the model. The impedance of the entire section is assumed to be constant with a resistance of 0.0027 p.u and a reactance of 0.0023 p.u.
Design of MCS for dynamic load management
Individual battery control and its algorithm for charging and discharging 60
The distribution of power between the CSs is based on the CS's ability to handle the power. The power distribution between the batteries is based on the available/required energy of the battery.
Fuzzy logic based V2G controller
Consider another scenario, where the junction voltage is (0.98 p.u) and all batteries reach a lower SOC than the SOC limit. In this case, the batteries are charged, but at a very low rate, because the controller limits the power consumption of the batteries.
Assumptions and background
Some critical cases are summarized in Table 3.2, when all arriving EVs have SOC < SOClt or SOC> SOClt. In Table 3.5, "To" indicates that electric vehicles will take CS/Transit in the next time interval.
Results and discussions
Case I
- Case I(a): 0900 hrs-1700hrs (off-peak hours)
- Case I(b): 1700 hrs-2200hrs (peak hours)
- Case I(c): 2200 hrs-0900hrs (off peak hours)
- Case I(d): High SOC EVs leave CS 2 during peak hours
- Case I(e): Low SOC EVs arrive at CS 1 during off-peak hours . 79
Voltage of the test node with and without implementation of the MCS is shown in Figure 3.27. The SOC of the batteries for first time slot at CS1 is shown in Figure 3.29.
Summary
Available energy is excess battery energy and this energy can be discharged to the grid. Also, batteries that have less energy available can discharge a smaller amount of energy to the grid and vice versa.
Modelling of the V2G system
Modelling of the charging station
Distribution substation
Controllers and aggregators
- Integration of renewable energy sources
- Fuzzy Logic Controller
- Division of power among the batteries
- Individual battery control
- Assumptions and background
In this figure, V(p.u.) is the node voltage in p.u., E is the available energy in the batteries, Pcomp is the power to be compensated, and P is the required power of CS. In Figure 3.5, Cratelimit is the preferred maximum Crate set by the vehicle owner (see Table 4.2) and Vbat is the battery voltage.
Results and discussion
Case I: Commercial and residential load at node 3.3
The voltages of the node with and without integration of the solar and wind energy sources are shown in figure 4.13. It is observed that the voltage of the node diverges from 1 p.u to a higher value with the integration of renewable energy sources.
Case II: Industrial load at node 2.3
The voltage of node 2.3 (with and without renewable energy sources) is shown in figure 4.18. It is observed that the proposed controller maintains a curve almost similar to that of the 'power to compensate'.
Summary
Among the different storage technologies, the use of electric vehicle (EV) batteries could become the potential storage for the electricity grid (Chapter 2 and Chapter 3). The modeling of the V2G system at DS and CS levels, together with the battery modeling, is presented in Section 5.3.
Description of the test system
This assumption does not affect the proposed algorithm because it takes into account the capacity loss of the batteries, which is the main cause of reducing the efficiency of the charging system. The chapter is structured as follows: A description of the test system is given in section 5.2.
Modelling of the system
- Modeling of the V2G at the substation level
- Aggregator at the subfeeder level
- Modeling of the CS
- Battery
The detailed block diagram of the battery model based on the capacity loss is shown in Figure 5.7. The battery's energy is updated based on the processed energy and at different boxes.
Design of controller and aggregators of the multi node CSs
- Controller and aggregator at the DS
- Aggregator at the subfeeder level
- Controller at the CS level and the aggregator
- Control scheme of the battery
Psurplusgrid is the required/available power for grid support, as determined by the substation. This power is the net power to be compensated by the CS and is called Psurplus.
Development of scenarios
Results and discussion
CASE I: Off-Peak hours (2200 hrs to 0800 hrs)
Now, based on the power distribution between CSs by the subfeeder aggregator, the CSC will decide the net power flow between the CS and the node. The amount and direction of the net power flow between the CS and the grid is shown in Figure 5.10.
CASE II: Peak hours (1700 hrs-2200 hrs)
Consequently, CS23 energy will not be included in the net available energy, which is one of the inputs of DS FLC. Now based on Eq. 5.10), Psurplus can be calculated which is one of the inputs of the DSC.
Summary
The collector at the MCS level distributes the power among the CSs, and the aggregator at the CS distributes the power among the EV batteries present in the CS. Finally, the CS aggregator distributes the power between the electric vehicle batteries present in the CS.
Development of a complete system
It has been observed that each chapter in this thesis presented a real-time coordination of the EVs at the CS either at a single node or at a multi-node to achieve the desired power flow between the CS and the grid. Two controllers have been shown, the first controller being the substation controller and the second at the CS level.
Contributions of the present Work
Suggestions for future research
Fuzzy Logic Controller
Fuzzification and Defuzzification
This is mathematically defined as A.2), the output values can be found as below. A detailed block diagram of the battery model based on capacity loss is shown in Fig.
C rate calculation
- Aggregator in V2G system
- Electric Vehicles in V2G system
- Different grid support provided by V2G
- Outline of existing literature on V2G
- Coordination of EVs present at the CS
- Controllers implemented in V2G infrastructure
- Radial test feeder of 33kV substation of Guwahati city
- EVs’ batteries used as a Distributed Energy Source
- A Radial System
- Fuzzy Controller
- Charging Station Controller
- V2G Controller
- Fuzzy Membership Function (Voltage)
- Fuzzy Membership Function (Energy)
- Fuzzy Membership Function- load/source of EV battery
- Fuzzy Membership Function (SOC)
- Peak hours voltage before discharging of EVs’ energy to the grid
- Peak hours voltage after discharging of EVs’ energy to the grid (Scenario I)
- Power supplied by EVs charging station ( Scenario I)
- Energy from individual charging station Controllers (Scenario I)
- Voltage at node 6.3 of subfeeder 5 at a p.f of 0.85
- Power discharge during off peak hours at a p.f of 0.85
- Power supplied by EVs charging station (Scenario II)
- Voltage at node 6.3 of subfeeder 5 (scenario II)
- SOC after discharging of EVs’ energy to the grid
- Voltage during charging(Scenario I)
- EVs charging from the grid (Scenario I)
- Voltage during charging ( scenario II)
- EV’s charging from the grid (Scenario II)
- SOC of EVs’ battery after charging
- Peak demand management using available energy of EV’s battery (scenario I) . 47
- Charging schemes of EVs for scenario II
- Flattening of load profile at node 6.3 during discharging and charging of EVs
- MCS connected to the distribution grid
- Block diagram representation of MCS
- Block diagram representation of a CS and the individual battery control
- Radial distribution system of a substation of Guwahati city
- Flow chart for controlling the flow of power in individual battery
- Membership functions for three inputs and one output of the V2G Controller
- Transition of EVs between possible destination
- Case I(a): (Off-peak hours: 0900 hrs-1700 hrs):- Power supplied by the grid to
- Case I(a): (Off-peak hours: 0900 hrs-1700 hrs):- Energy required by CSs for
Sharkawi, “Optimal Energy Scheduling from Vehicle to Grid and Ancillary Services,” IEEE Transactions on Smart Grid, p. Kar, “Implementation of vehicle to grid infrastructure using a fuzzy logic controller,” IEEE Transactions on Smart Grid, vol.
