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Title: Designing and Testing of a Three-Phase AC Net Meter for V2G Application
Authors: Kaur, Amrit Pal
Supervisor: Singh, Mukesh
Keywords: Electric Vehicle;Vehicle to Grid;Net Meter;Time of Use;Demand Response
Issue Date: 1-Sep-2023
Publisher: Thapar Institute of Engineering and Technology Patiala
Abstract: In today’s scenario, the estimated exponential growth of Electric vehicles (EVs) has raised con- cerns about the spike in electricity demand. Vehicle-to-Grid (V2G) technology has emerged as an effective solution that enables the EV as a prospective energy source irrespective of time and atmospheric conditions. It connects the vehicle to the grid in two operating modes: charging and discharging or V2G mode. While charging, EV acts as an electric load and draws current from the grid. In contrast, in discharging mode, EV acts as an energy source and feeds the current back to the grid when needed. The idea benefits to balance the electric grid by supplying the energy back to the grid in peak demanding hours. Overall, it enhances grid performance by improving grid effi- ciency, stability, and reliability. Establishing the V2G application involves three elements amongst the bidirectional intelligent metering plays a significant role. However, the lack of bi-directional metering concerning EV users and service providers deters the V2G applicability in real. Hence, bi-directional metering equipment and infrastructure are developed for the V2G application. Firstly, the research aimed to design and develop a cost-effective Three-Phase AC Net Meter for easy acceptance of the V2G application in society. The Net Meter is developed at technology readiness level (TRL) 9 for V2G-enabled charging stations at levels 1 and 2. The compliance con- siders the automotive grade with the International Electrotechnical Commission (IEC) standards to assure class 1 accuracy. C2000 Digital Signal Processor (DSP) based TMS320F28069 micro- controller has reduced the cost involved with affecting the performance needed for the application. The prototype assures the automotive industry standards (AIS) for electromagnetic compatibility in the practical environment. In the software implementation, coherent sampling is adopted to iv compute Fast-Fourier Transform (FFT). The choice of coherent sampling has reduced the over- head involved in identifying the suitable window for the application, and it changes the sampling rate with the change in the input frequency of the grid signal. The technique has shown better spectral resolution and harmonic detection. In the second work, an internet-of-thing (IoT) based Net Meter is deployed as a data source and recipient of information between EV, grid operator, and EVSE for the V2G technology. The device enables real-time data accumulation and connects the BMS of the Li-ion battery using MODBUS- RTU protocol to read the battery status at regular intervals of time. The developed prototype is configured as both master-slave; it operates in master mode while initiating communication with BMS. The EV parameters fetching and evaluation help to establish a coordinated charging- discharging environment without compromising the battery life. Net Meter accounts for the energy required or will be generated by the EV when connected based on the requested operation. Besides, the device is embedded with a simplified software 2-point calibration process to accurately re- scale the input sensors. The results have shown that the prototype has attained class 1 accuracy. Moreover, the ToU attribute is integrated to allow dynamic billing with evolving techniques and presents a protection mechanism against physical tampering at the EV prosumer end. Lastly, the third work proposes a research methodology for optimal demand-side management (DSM) using EVs. Aggregators (AG) are programmed to generate time-of-use (ToU) based tariff rates to motivate the EV prosumers to participate in the V2G application. Concerning this, a novel mathematical model is proposed to calculate the tariff rates using EVs’ peak and off-peak contribution coefficients. In addition, to mitigate the situation of simultaneous EV charging that may alter the consumption curve, a conditional prioritization is presented based on EVs’ state- of-charge (SoC). The best and worst-case computational complexity in the proposed technique is O(N ) due to the involvement of the linear search algorithm. In the simulation, the AG manages the data from multiple IoT-based Net Meters and effectively reduces the peak consumption by 6%–7% with an elasticity of 0.45.
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