TY - GEN
T1 - Smart charging of electric vehicles using reinforcement learning
AU - Valogianni, Konstantina
AU - Ketter, Wolfgang
AU - Collins, John
PY - 2013/7/15
Y1 - 2013/7/15
N2 - The introduction of Electric Vehicles (EVs) in the existing Energy Grid raises many issues regarding Grid stability and charging behavior. Uncontrolled charging on the customer's side may increase the already high peaks in the energy demand that lead to respective increase in the energy prices. We propose a novel smart charging algorithm that maximizes individual welfare and reduces the individual energy expenses. We use Reinforcement Learning trained on real world data to learn the individual household consumption behavior and propose a charging algorithm with respect to individual welfare maximization objective. Furthermore, we use statistical customer models to simulate the EV customer behavior. We show that the individual customers, represented by intelligent agents, using the proposed charging algorithm reduce their energy expenses. Additionally, we show that the average energy prices, on an aggregated level, are reduced as a result of smarter use of the energy available. Finally we prove that the presented algorithm achieves significant peak reduction and reshaping of the energy demand curve.
AB - The introduction of Electric Vehicles (EVs) in the existing Energy Grid raises many issues regarding Grid stability and charging behavior. Uncontrolled charging on the customer's side may increase the already high peaks in the energy demand that lead to respective increase in the energy prices. We propose a novel smart charging algorithm that maximizes individual welfare and reduces the individual energy expenses. We use Reinforcement Learning trained on real world data to learn the individual household consumption behavior and propose a charging algorithm with respect to individual welfare maximization objective. Furthermore, we use statistical customer models to simulate the EV customer behavior. We show that the individual customers, represented by intelligent agents, using the proposed charging algorithm reduce their energy expenses. Additionally, we show that the average energy prices, on an aggregated level, are reduced as a result of smarter use of the energy available. Finally we prove that the presented algorithm achieves significant peak reduction and reshaping of the energy demand curve.
UR - http://www.scopus.com/inward/record.url?scp=84898855884&partnerID=8YFLogxK
UR - https://dl.acm.org/doi/10.5555/2908259.2908266
UR - https://www.semanticscholar.org/paper/Smart-Charging-of-Electric-Vehicles-using-Learning-Valogianni-Ketter/36c1bdcb8a6b9fd410e5ed069d12fa0975b52b6c
M3 - Conference proceeding
AN - SCOPUS:84898855884
SN - 9781577356264
T3 - AAAI Workshop - Technical Report
SP - 41
EP - 48
BT - Trading Agent Design and Analysis
PB - AI Access Foundation
T2 - 2013 AAAI Workshop
Y2 - 15 July 2013 through 15 July 2013
ER -