Contents
1.3 Motivation and Reason to Adopt the Method
In literature, many researchers have validated the EVs and PEVs can be used as distributed energy storage system for grid support. They have implemented an extensive control strategy for scheduled charging/discharging of the EVs’ batteries by controlling the charge/discharge rate. In few research papers, large scale renewable-energy source has been integrated with the power grid and it has been observed that the EVs’ batteries play an important role for smoothening the natural intermittency and frequency stability. However, few researchers have analyzed the technical impacts of the distributed generators with ESS. Therefore, the EVs would support the grid during peak power demand and store the energy during off-peak hours. Several works has been developed to validate the V2G implemen- tation. They have demonstrated various control algorithms to supply the power to electric grid for stabilization and peak load shaving. Tomic et al have demonstrated that the EVs can provide power to grid when they are parked in parking bay or not in use. Few research papers, they have focused on frequency regulation to coordinate multiple EVs with an optimal aggregator. They have designed an optimal scheduled charging methodology and an aggregator for multiple EVs coordination which can support the grid. Singh et al have designed an aggregated EVs energy system which can support the grid and charge their battery based on the grid condition. They have developed fuzzy based control strategy which can control the power in either direction. Moreover, they have analyzed system level control methodology for voltage regulation. They have not designed converter and inverter which can provide/take the power to/from grid [74]. Also, the aggregated V2G system minimizes the charging cost and reduces the power losses incurred by the fluctuating load. Lee et al have analyzed PHEVs charging behavior and its impacts on the electric grid by using the daily driving schedule. Some re- searchers have implemented sophisticated control methodology for modeling the system and analyzed load demand in a distribution system due to EV charging.
In most of the research work that have been reported, the V2G implementation with grid is as a plug-in or wired system. Only recently, contactless or inductive power transfer charging system is gaining attention for EVs and HEVs application due to elimination of direct electric contact. These charging systems overcomes the drawbacks of traditional wired charging systems such as heating of the sockets and cables, risk of fire, electrical injuries and cable breakage [2,92]. Many research works
has been reported in literature to validate the IPT for V2G system. They have developed robust con- trol methodology for controlling the power in either direction. Sallan et al has developed a detailed design procedure for IPT system with 2kW prototype model. Many researchers have modeled con- verter and inverter configuration for transferring the power. The power transfer takes place only when the charging system is synchronized with the grid. In other words, the power flow is not being guar- anteed without synchronization of the system with grid. The PLL based synchronization technique is most suitable for DESS. In few papers, they have developed sophisticated control algorithm for the synchronization of grid connected with converter system. Numerous research works has analyzed the losses, economic burden and the impacts of DN while transferring the power. The economic burden to the EVs owners and the electric grid operator has been reported in few papers.
It can be concluded from the above literature that, to mitigate the peak power demand multiple EVs batteries are required to support the grid and store the energy during off-peak hours. However, coordination of multiple EVs for grid support requires an optimal aggregator. Therefore, a sophis- ticated charging station is required which provide aggregated energy for the grid support. EVs are charged at a place called as CS and this CS is situated near the DN in the parking bay. The parking bay can be a residential complex or an office complex or even a shopping complex. The EVs normally stay for longer duration in the parking bay and thus V2G concept can be achieved.
Sishugram power substation in Guwahati city has been considered for this analysis. The distri- bution network has been developed in MATLAB Simulink environment based on the data obtained from this substation. The multiple EVs are connected to the distribution node (11kV/440V) via ac bus. Therefore, the EVs can provide the power based on the DN status and energy availability of the CS.
If EVs’ batteries are intended to perform V2G operation, it would get subjected to varying node voltage conditions. Also, during acceleration and regenerative breaking conditions, the EVs’ batteries would get discharged and charged frequently. Such frequent charging/discharging operating condi- tions affect the internal circuit parameters due to change in state-of-charge or depth-of-discharge, charge rate or discharge rate of the battery. Moreover, the capacity of EVs’ batteries would decrease due to frequent charging/discharging process at different charge/discharge rate. Therefore, a suitable
EV battery model is required to predict the V2G interaction. A mathematical model of EVs’ batteries for different charge/discharge rate has been developed. Also, in this thesis a mathematical model for economic evaluation of V2G interaction is developed based on the capacity fading of EV battery at different charge/discharge rate. This model determines the optimal cost of electricity so that both the grid and the EVs owners are benefited.
Primary side
Fuzzy logic controller
Aggregator CS Primary side Primary side
Fuzzy based Primary and Secondary side controller
Fuzzy based Primary and Secondary side controller Fuzzy based Primary and Secondary side controller
Information about node voltage
Information of total and individual EV battery energy
Distribute the reference signal to each EV
battery Total power transfer
between CS and DN
Duration and total energy of CS
Information from
BCCS unit Reference
BCCS unit
Information from Reference
Reference BCCS unit
Information from Power flow
Control signal
Secondary side or EV Side
Secondary side or EV Side Secondary side
or EV Side
Utility Grid
ac bus
Power P1re f
Power P2re f
Power Pnre f
P1re f
P2re f
Pnre f
Figure 1.7: Illustration of smart charging station.
The schematic block diagram of the smart charging station (SCS) is shown in Fig. 1.7. It mainly
consist of (a) utility grid, (b) fuzzy based central control unit (CCU), (c) CS aggregator,(d) primary side (PS) and secondary side (SS) converter units, (e) contactless coil coupled with the PS and SS converter unit and lastly (f) controller for the converter unit. Total power (Pgrid) flow in either direc- tion has been controlled by designing an extensive fuzzy based controller which decides the amount and direction of power flow. The fuzzy based CCU output could be positive or negative. The positive power implies that batteries getting charged and the negative power imply that batteries discharging for the grid support. The CCU decides the total power based on the node voltage (Vnode), avail- able/required energy (Eavail/Estor) of the EVs batteries present in the CS and duration to support/inject the power from/to grid. Then, the CS aggregator gives the reference power (Pbn) signal to nth EV battery based on the Pgrid, Eavail/Estor and individual energy (Ebn) of the EV battery. The PS and SS controller would control the power flow based on the Pbn.