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Decentralized Additive Increase and Multiplicative Decrease-Based Electric Vehicle Charging
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DOI：10.1109/JSYST.2020.3013189.png)
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Abstract
En 中文
Electric vehicle (EV) transition and low-cost renewable energy generation are putting power grid under a challenging transformation. Number of power electronics actuators connected to the grid is increasing, and the legacy control methods employed on the grid are not responsive to this growing demand. Thus, the grid integration of EVs and their charging management requires a system-wide solution that is scalable, autonomous, and stable. In this article, we investigate two very complex networks: Internet and power grid in the context of controlling mass-scale EV charging problem. We adapt the well-known additive increase-multiplicative decrease (AIMD) algorithm used in the Internet congestion control to EV charging in a distributed fashion. We develop an adaptation of the Internet's congestion control method for power grid considering the unique grid constraints using a decentralized concept. The advantage of the proposed method lies in its low-cost (memory-less) congestion detection mechanism based on only local voltage measurements. Results show that decentralized AIMD can successfully help flatten the peak loading caused by high EV penetration. To test the algorithm, a distribution grid model is designed based on IEEE 37-node test feeder with realistic load modeling. Finally, the results are presented in comparison with two other control architectures.
Keywords:
Electric vehicle charging
Internet
Heuristic algorithms
Threshold voltage
Additives
Voltage measurement
Artificial intelligence
Additive increase-multiplicative decrease (AIMD)
decentralized control
electric vehicles (EV)
grid integration
peak shaving
smart charging
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