Physics

Sociology

Scheduling (production processes)

Population

Computer network

Mathematics

Job shop scheduling

Operating system

Distributed computing

Mathematical optimization

Demography

News aggregator

Power (physics)

Grid

Electric vehicle

Programming language

Geometry

State of charge

Computer science

Plug-in

Battery (electricity)

Quantum mechanics

Routing (electronic design automation)

IEEE Transactions on Smart Grid

Plug-in electric vehicles (PEVs) are emerging as an eco-friendly and cost-effective alternative to conventional vehicles driven by internal combustion engines. However, uncoordinated charging of a large number of PEVs may cause grid failure. Therefore, charge scheduling of PEVs is an important problem. However, the charge scheduling by a single aggregator does not scale well as the PEV population grows. We propose a distributed framework for efficient PEV charging with multiple aggregators in a city where a PEV raises its charging request to a specific aggregator, and each aggregator has partial information about others. The aggregators collaborate among themselves for scheduling PEVs for charging in different charging stations owned by it or others. In this paper, we formulate a bi-objective charge scheduling optimization problem that attempts to maximize the total profit of the aggregators while maximizing the total number of PEVs charged. We first prove that the problem is NP-complete. We then propose distributed offline and online algorithms to solve the problem, and present simulation results for some realistic traffic scenarios.

Distributed Charge Scheduling of Plug-In Electric Vehicles Using Inter-Aggregator Collaboration