Real-time simulation of EV grid integration with internet-inspired charging control

Analyzing realistic EV-grid integration (EVGI) with available simulation tools is cumbersome due to the software overhead associated with offline simulation. Alternatively, real-time hardware platforms are becoming convenient means for testing and evaluating systems before field implementation. This study presents a digital implementation of an EVGI model in real-time on a multi-core processor based simulation platform. Furthermore, an Interned-inspired EV charging control algorithm is proposed in a decentralized fashion to prevent congestion related problems in a residential distribution grid. The impact of the proposed EV charging control on the IEEE 37-node test system is evaluated through the real-time analysis. The developed controller results show promise for extension to any utility-interfaced power electronics system. Real-time simulation implementation requirements and challenges in the context of EVGI are also discussed

Analysis Of An Internet-Inspired Ev Charging Network In A Distribution Grid

Electric vehicles (EVs) are transforming the modern transportation and energy systems. However, due to increasing battery and charger capacities with long charging times, potential adverse effects on distribution grid will become a crucial problem. Safe and efficient operation of the grid along with a fast, convenient, and fair charging strategy is an important research tackle. In this paper, we analyze the additive increase-multiplicative decrease (AIMD) method used to solve a similar problem occurred in the early days of the Internet and apply it to EV charging using only local measurements. Then, we present a detailed analysis to understand the relationship between distance and charging power in a distribution network to better address the fairness in the proposed AIMD EV charging algorithm

Data-driven, Internet-inspired, and Scalable EV Charging for Power Distribution Grid

Electric vehicles (EVs) are finally making their way onto the roads. However, the challenges concerning their long charging times and their impact on congestion of the power distribution grid are still waiting to be resolved. With historical measurement data, EV chargers can take better-informed actions while staying mostly off-line. Proposed solutions that depend on heavy communication and rigorous computation for optimal operation are not scalable. The solutions that do not depend on power distribution topology information, such as Droop control, are more practical as they only use local measurements. However, they result in sub-optimal operation due to a lack of a feedback mechanism. This study develops a distributed and data-driven congestion detection methodology embedded in the Additive Increase Multiplicative Decrease (AIMD) algorithm to control mass EV charging in a distribution grid. The proposed distributed AIMD algorithm performs very closely to the ideal AIMD regarding fairness and congestion handling. Its communication need is almost as low as the Droop control. The results can provide crucial insights on how we can use data to reveal the inner dynamics and structure of the power grid and help develop more advanced data-driven algorithms for grid-integrated power electronics control.Comment: This work has been submitted to the IEEE Open Access Journal of Power and Energy (OAJPE) and is currently under revie

Distribution grid voltage support with four quadrant control of electric vehicle chargers

Conventionally, voltage control in distribution grids has been achieved through load tap changers, capacitor banks, and network switches. With the advancements in on-board electric vehicle (EV) chargers, voltage control can be achieved through control of EV charging/discharging as EVs can operate in any of the four P-Q quadrants with slight modifications to the charger. In this work, we use a high-fidelity on-board EV charger model, which has been validated using actual measurements. Then, we coordinate the four quadrant operation of EVs and distribution feeder to support voltage control in the grid. The efficacy of the developed models are demonstrated by using an LV secondary feeder. Operation of EVs in all four quadrants are shown to compensate for the feeder voltage fluctuations caused by daily time varying residential loads, while satisfying operational constraints of the feeder