首页|Researchers from University of Waterloo Provide Details of New Studies and Findi ngs in the Area of Machine Learning (Machine Learning-based Control of Electric Vehicle Charging for Practical Distribution Systems With Solar Generation)
Researchers from University of Waterloo Provide Details of New Studies and Findi ngs in the Area of Machine Learning (Machine Learning-based Control of Electric Vehicle Charging for Practical Distribution Systems With Solar Generation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating from Waterloo, Ca nada, by NewsRx correspondents, research stated, “The adoption of Electric Vehic les (EVs) and solar Photovoltaic (PV) generation by households is rapidly and si gnificantly increasing. Utilities are facing the challenge of efficiently managi ng EV and PV resources to help mitigate the undesirable effects on grid operatio n.” Financial support for this research came from NSERC-Alliance/OCE-VIP. Our news editors obtained a quote from the research from the University of Water loo, “Existing approaches to solve these issues depend on accurate but hard to p redict behavior of EVs and PVs, detailed knowledge of customers, and grid infras tructure, all of which complicate the effective deployment of these resources. M otivated by these practical challenges and in collaboration with industry partne rs working on addressing these issues, this paper proposes a two-level data-driv en smart controller for EV charging in distribution systems. The controller is m odeled as a Deep Reinforcement Learning (DRL) agent, which coordinates the charg ing rates of multiple EVs connected to a realistic residential feeder with high penetration of PV generation. The first level coordinates the aggregated EV load at distribution Medium Voltage (MV) level to provide Demand Response (DR) servi ces; at the Low Voltage (LV) level it aims to maximize the EVs’ state of charge at departure while avoiding the overloading of the MV/LV distribution transforme rs.”
WaterlooCanadaNorth and Central Amer icaCyborgsEmerging TechnologiesMachine LearningUniversity of Waterloo
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