Agent-based Charging Scheduling of Electric Vehicles

TY - GEN

T1 - Agent-based Charging Scheduling of Electric Vehicles

AU - Ghasem Azar, Armin

AU - Jacobsen, Rune Hylsberg

PY - 2017/1/4

Y1 - 2017/1/4

N2 - The electric vehicle technology intends to mitigate negative impacts of the energy challenge on the current transportation infrastructure. However, integrating a large number of such vehicles imposes a significant additional load to the grid and may overload it. This paper proposes a hierarchical event-driven multi-agent system framework for coordinated charging scheduling of electric vehicles. Household agents negotiate temporal travel patterns with substation agents to decide when electric vehicles should charge their batteries. A scalable load scheduling algorithm is proposed to schedule charging process of electric vehicles in real-time regardless of using any forecasting method. It aims to permit as many electric vehicles as possible to operate while keeping their aggregated charging energy consumption below continuous electricity-price-dependent thresholds over time. Simulations confirm that the framework benefits from charging flexibilities, reduces the charging cost, and shaves the grid's peak.

AB - The electric vehicle technology intends to mitigate negative impacts of the energy challenge on the current transportation infrastructure. However, integrating a large number of such vehicles imposes a significant additional load to the grid and may overload it. This paper proposes a hierarchical event-driven multi-agent system framework for coordinated charging scheduling of electric vehicles. Household agents negotiate temporal travel patterns with substation agents to decide when electric vehicles should charge their batteries. A scalable load scheduling algorithm is proposed to schedule charging process of electric vehicles in real-time regardless of using any forecasting method. It aims to permit as many electric vehicles as possible to operate while keeping their aggregated charging energy consumption below continuous electricity-price-dependent thresholds over time. Simulations confirm that the framework benefits from charging flexibilities, reduces the charging cost, and shaves the grid's peak.

KW - Coordinated charging scheduling

KW - Demand response

KW - Electric vehicle

KW - Multi-agent systems

KW - Smart grid

UR - http://www.scopus.com/inward/record.url?scp=85014277660&partnerID=8YFLogxK

U2 - 10.1109/OnlineGreenCom.2016.7805408

DO - 10.1109/OnlineGreenCom.2016.7805408

M3 - Article in proceedings

VL - 2016

SP - 64

EP - 69

BT - 2016 IEEE Online Conference on Green Communications, OnlineGreenComm 2016

PB - IEEE

T2 - IEEE Online Conference on Green Communications

Y2 - 14 November 2016 through 17 November 2016

ER -