RENEWABLE BASED BATTERY PROTECTIVE ELECTRIC VEHICLE CHARGING MANAGEMENT SYSTEM
Author(s):
Praisy Roselin.S, Mrs. C. Gnanathilaka
Keywords:
Electric Vehicles, renewable energy, microgrids, Extreme Learning Machine algorithm.
Abstract
The adoption of Grid-connected Electric Vehicles (GEVs) brings a bright prospect for promoting renewable energy. An efficient Vehicle-to-Grid (V2G) scheduling scheme that can deal with renewable energy volatility and protect vehicle batteries from fast aging is indispensable to enable this benefit. This project develops a novel V2G scheduling method for consuming local renewable energy in microgrids by using a mixed learning framework. It is the first attempt to integrate battery protective targets in GEVs charging management in renewable energy systems. Battery safeguard strategies are derived via an offline soft-run scheduling process, where V2G management is modeled as a constrained optimization problem based on estimated microgrid and GEVs states. The Extreme Learning Machine (ELM) algorithm is used to train the established online regulator by learning rules from soft-run strategies. The online charging coordination of GEVs is realized by the ELM regulator based on real-time sampled microgrid frequency. The effectiveness of the developed models is verified on a U.K. microgrid with actual energy generation and consumption data. This project can effectively enable V2G to promote local renewable energy with battery aging mitigated, thus economically benefiting EV owns and microgrid operators, and facilitating decarburization at low costs.
Article Details
Unique Paper ID: 163221

Publication Volume & Issue: Volume 0, Issue no

Page(s): 85 - 90
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