کنفرانس بین المللی فناوری اطلاعات و دانش

صفحه اصلی / یازدهمین کنفرانس بین المللی فناوری اطلاعات و دانش

Integration ‎of ‎E‎lectric Vehicles in Smart ‎Grid using ‎Dee‎p ‎Reinforcement ‎Learning

نویسندگان :

Farkhondeh Kiaee¹

1- دپارتمان مهندسی برق و کامپیوتر، دانشکده شریعتی، دانشگاه فنی و حرفه‌ای استان تهران، ایران

کلمات کلیدی :

‎‎vehicle-to-grid‎, ‎deep Learning, electric vehicles, ‎r‎einforcement ‎learning, ‎‎double Q-‎network

چکیده :

‎‎‎‎‎The vehicle-to-grid (V2G) ‎technology‎ provides an ‎op‎portunity‎ to generate revenue by selling electricity back to the grid at peak times when electricity is more expensive.‎‎‎‎ ‎Instead of sharing a ‎contamin‎ated ‎‎pump ‎handle‎ at a ‎gas station ‎during the current covid-19 ‎pan‎demic‎, ‎plugging‎ in ‎the electric vehicle (‎EV)‎ ‎at ‎home‎ ‎makes ‎feel ‎much‎‎ ‎safer.‎‎‎ ‎‎‎‎‎A V2G control algorithm is necessary to decide whether the electric vehicle (‎EV)‎ should be charged or discharged in each hour. In this paper, we study the real-time V2G control problem ‎under‎ price ‎uncertainty‎‎ where the electricity price is determined dynamically every hour. ‎‎Our model is inspired by ‎‎‎‎the Deep Q-learning ‎(DQN) ‎algorithm ‎which combines ‎popular Q-‎learning‎ with a deep neural ‎network‎‎. ‎The proposed ‎Double-DQN ‎model is an ‎‎update of the DQN which ‎maintains ‎‎two ‎distinct ‎network‎s to ‎select or ‎evaluate an ‎action. ‎The Double-DQN ‎algorithm‎ ‎is used to control charge/discharge operation in the hourly available electricity price in order to maximize the profit for the EV owner during the whole parking time. ‎Experiment‎ results show that our proposed method can work effectively in the ‎‎real electricity market and it is able to increase the profit significantly compared with the ‎other state-of-the-art‎ EV charging schemes.‎