I would also like to thank the Head of the Department and other faculty members for their kind help in carrying out this work. Proper implementation of controlled charging and discharging can mitigate the peak power demand of the DN by providing power to the grid (peak shaving) or power drawn from the grid (valley filling).
List of Acronyms
EIG Energy injection control EOCV Final charge voltage EODV Final discharge voltage EV Electric vehicles.
List of Symbols
ET/Etotal Total battery energy EV or CS/Total processed battery energy. Mdep Depreciated monetary value of the battery after a certain number of cycles n The number of cycles the battery has interacted with the network.
Introduction
Contents
Introduction
The gap between the top. and off-peak power consumption can be reduced if ESS is used. The electric vehicles (EVs) can be used to mitigate the peak power demand because these vehicles are mostly kept parked [21-24].
Electric Vehicles in Smart Grid
- Vehicle to Grid
- Major Issues in V2G Interactions
- Literature Survey
- Aggregator
- Energy Storage System or EVs Batteries
- Inductive Power Transfer
- Synchronization
- Economic Impacts of EVs and Grid
- Possible Solution Related to V2G Interactions
In this work, a soft control strategy was used to control the power flow between the grid and EV batteries. In this work, we studied the regulation of power flow in both directions without a communication link.
Motivation and Reason to Adopt the Method
Therefore, the EVs can supply the power based on the DN status and energy availability of the CS. The CCU determines the total power based on the node voltage (Vnode), the available/required energy (Eavail/Estor) of the EV batteries present in the CS and the duration of supporting/injecting the power to/from the electricity grid.
Aim of the Thesis
Fuzzy logic controllers are used to control the power flow because they are very suitable for uncertain situations of electric vehicle arrival in SCS. v) The converter and inverter unit should be developed for electric vehicle battery charging/discharging system. vi) The contactless or IPT system must be designed to defeat the traditional wired charging system. vii). Finally, a mathematical model should be required for the economic evaluation of V2G integration based on energy transfer.
Bidirectional Contactless Charging System for V2G Power Transfer
This mathematical model calculates the benefit cost to grid operators during EV battery charging and estimates the benefit cost to EV owners during grid support. To determine the optimal cost of energy transfer between EV batteries and the grid in order to financially benefit both the grid and EV owners.
Main Contributions
Thesis Organization
The aim of this work is to develop a mathematical model for the integration of electric vehicles into the electricity grid. This bidirectional exchange of energy between the electricity grid and electric vehicles results in complex financial calculations. The energy supplied by the EVs to the grid depends on the battery capacity and further battery capacity is affected by capacity loss (CL).
Mathematical Modeling of Li-Ion Battery Using Genetic Algorithm Approach for V2G
Introduction
It is possible to analyze the loss of capacity and thus predict the life of the battery. The circuit-based battery model can be used to represent the electrical characteristics of EVs. Cr and Dr characteristics are calculated using BM parameters which are obtained from GA and compared with different types of data of battery manufacturers.
Battery Model
- Charge/Discharge Rate and SOC Calculations
- Battery Power and Processed Energy
The battery terminal charge or discharge voltage (VcCi or VdC. j) changes with battery capacity, S OCcr/DODcr levels, and Cr/Dr. The parameters of non-linear relationship of VcCi/VdC. j can also be represented in the form of polynomial equations, where i and j indicate the ith and j'th calculated value of the charge and discharge voltage. These equations capture the non-linear behavior of the battery, which depends on the actual charge/discharge voltage of the battery. The Cr and S OCcr of the battery vary depending on the current state of the battery.
Parameter Extraction Using Genetic Algorithm Approach
- Genetic Algorithm
- Parameter Extraction Process Using GA
- Parameter Specifications
- Generation of Initial Solution Set
- Coding
- Fitness Function
- Selection
- Reproduction
- Crossover
- Mutation
The best solution set values are converted from actual value to binary string of solution set. A string is selected from the random number and is compared to the previous best solution set based on the cumulative probability. After the next generation solution set, the crossover genetic operators for the selected solution set will be applied.
Capacity Fade Model
F′(x) is maximized, when the difference between the measured (catalogue value) (QlM . k) and calculated (QCl . k) capacity loss is minimized. The battery manufacturers' charge/discharge rate characteristics were extracted using the mathematical equations described in Section 2.2. Capacity loss is calculated using the energy of the battery, pre-exponential factor, gas constant, temperature and adjustable factor.
Model Validation
- Performance Characteristics of BM
- Capacity Fade Analysis
2.19 (a) and (b), the capacity loss as a function of cycle number and processed energy has an increasing behavior. The capacity loss as a function of cycle number and total processed energy is shown in Fig. The comparison of residual capacity curve as a function of cycle number and total processed energy is shown in Fig.
Summary
Modeling and Control of Contactless based Smart Charging Station in V2G Scenario
Introduction
Many studies have been done on V2G system, with most of the work focused on the impact of charging systems on the distribution network. However, none of the studies attempted to study the control and coordination of BCCS unit with multiple EVs in a charging station (CS) connected to a distribution node (DN) of the network. In detail, this Chapter describes a multi-point BCCS unit present in a CS connected with the DN of the grid is investigated.
Modeling of Multi-Point Smart Charging Stations
- Distribution Network Model
- Multi-Point Smart Charging Station(SCS)
- EV Battery Model
The individual charging points are non-contact, bidirectional and connected to the DN network via the ac bus. The CCU decides the net power flow between the network DN and the CS. The controllers present in the BCCS multi-point unit control the flow of power between the batteries of individual electric vehicles and the DN network.
Modeling of Smart Charging Station
- Central Control Unit (CCU) and CS Aggregator
- Synchronization Mechanism
- Multi-Point BCCS Unit
- G2V Operation
- V2G Operation
The three-phase phase-locked loop (PLL) evaluates the unit vectors (sinθ and cosθ), frequency and angular time of the BCCS unit and network. The measured power is calculated by the power estimator unit based on the three-phase voltage and current of the BCCS unit, the unit vectors and the system frequency. The range of theδ for LAC-FLC is chosen based on the BCCS and DN unit impedance.
Application Scenario of Multi-Point SCS
EVs' batteries will not charge when the node voltage is low (peak hours) and batteries will not discharge when the node voltage is high (off-peak hours). Each BCCS unit of the SCS is designed for maximum peak power handling capacity of 50 kW. In Table 3.8, the battery parameter is mentioned for a single EV battery with the rating of 250V, 8kWh and SOC level of 70%.
Results and Discussion
However, it is observed from Table 3.13, EVs' batteries are not able to provide the required power to the grid. The initial and final SOC set by the vehicle owners for five groups of EVs' batteries are given in Table 3.15. EVs' batteries are not charged above the maximum limit (S OCmax) and they are not discharged beyond the minimum limit (S OCmin) set by the vehicle owner.
Summary
Therefore, this CS was more suitable for support during peak energy consumption and also for energy storage during off-peak times. SCS was used to support the grid during peak electricity consumption and store energy during off-peak periods. The calculation of economic benefits based on capacity loss in electric vehicle batteries was also analyzed.
Mathematical Modeling For Economic Evaluation Of Electric Vehicle To Smart Grid
Introduction
In V2G scheme, the peak hour energy delivered to the grid by the EV depends on the battery capacity. The integration of the EV with the grid results in capacity loss of the battery, which mainly depends on Cr. In section 4.4, a scenario was analyzed where the EV is not integrated into the grid.
Capacity Fade/Loss Model
The amount of energy that can be extracted from the battery or stored in the battery decreases rapidly due to the CL in the EV battery. The CL mainly depends on the total energy processed during charging/discharging, Cr, Dr and temperature [180]. The battery capacity depends on certain specified conditions such as Cr, Dr, SOC and temperature [181].
Mathematical Model for Economic Evaluation
- Notations
- Assumptions
- Energy Required by EV Battery
- Peak Hour Energy Supplied to Grid
- Capacity Loss Compensation
- Determination of Tariff for Grid Operators
The grid operators will charge the EV owners depending on the total amount of energy consumed by the battery. It is the EV owners who determine and deliver the amount of energy to the grid. The next subsection presents the amount to be paid by the grid operators (for peak energy) to the EV owners.
Electric Vehicle Without Supporting the Grid
Grid operators to make profit, tariff paid by consumers to grid operators per kWh peak time energy (x′1) greater than tariff paid by grid operators to EV owner per kWh in peak time (x2) and tariff paid by consumers to network operators per kWh in the low season (x′1) higher than the tariff paid by grid operators to the electricity owner per kWh in the low season (x′2). The next section presents the scenario where the EV uses its full energy for transportation without supporting the grid. The next section presents the cost-benefit related calculations for a model where EV supports the grid during peak hours.
Analysis of Energy Trading Scenario Under Different Charging/Discharging Rates
This is due to the fact that while EV owners receive compensation for CL, grid operators do not receive such financial benefits. It is observed that the money paid by EV to grid operators is the same for 1Cr-1Dr, 2Cr-2Dr and 3Cr-3Dr. This is the main reason for higher transportation costs per kilometer for grid-integrated electric vehicles.
Summary
Integration of the EV with the electricity grid is desirable, as this provides the opportunity for network support when necessary.
Conclusion and Future Works
- Summary of the Present Work
- Complete V2G System and Control Architecture
- Contributions of the Present Work
- Scope for Future Research
Further, an SCS is developed based on the battery model and fuzzy logic controllers, which control the power flow between EV batteries and the grid. Therefore, a mathematical model has been developed for the economic evaluation of bidirectional energy transfer between EVs and the grid. The CFM model is used to study the EV battery performance during vehicle-to-grid interaction.
Battery Sample Calculation, Initial Population and Solution Set
- Sample Calculation for Battery Model
- Battery Power and Processed Energy
- Capacity Fade Model
- Genetic Algorithm
- Initial Population
- Polynomial Coefficients (a 1 − a 31 )
- Battery Parameters
A sample calculation of capacity sinks at different charge and discharge rates is given in Eq. The performance characteristics of the battery model depend on the parameters (R1,R2,C and V0) of the electrical equivalent circuit. Therefore, the general polynomial equation for designating battery charge and discharge rate characteristics given in Eq.
Fuzzy Logic Controller
Introduction
- Fuzzification
- Defuzzification
The most popular method is center point which estimates the center of gravity of the fuzzy set. Pgrid(p.u) Figure B.3: Crisp value of the Pgrid. B.2), the sharp value of the output power is shown in Fig. Pgrid(p.u) shown. Figure B.5: Crisp value of the Pgrid. B.2), the sharp value of the output power is shown in Fig.
Filter Design
- Modes of Operation
The output voltage is controlled in both directions by controlling the duty ratio (D) of the S13 or S14. The BB converter works by storing the energy in the inductor (Lb) during the interval in which switch S13in is turned on (ton). Suppose the peak-to-peak ripple voltage is 3% of the maximum voltage and the ripple current is 3% of the rated voltage.
Contactless Power Transfer System
- Self and Mutual Inductance Calculation
- Electrical Circuit Parameter Calculation
Therefore, a general expression for mutual inductance of the primary and secondary side coil is given in Eq. The voltage induced on the secondary side of the contactless coil is given in Eq. Reflected impedance on the primary side can be expressed as the ratio of the reflected voltage and.
