Scheduling of Community Based Charging Stations with Genetic Algorithms

The paper presents a Genetic Algorithm (GA) scheduling technique for delay tolerant power tasks. The application of the paper is focused on the use case of battery charging of electric vehicles (EVs). A set of charging stations are assumed to be powered by a time varying capacity or by grid imported energy with time varying prices. The objective of the proposed scheduling and optimization technique is to minimize the overall costs of the facility manager who owns the charging stations without affecting the Quality of Service (QoS) of the users. A Genetic Algorithm (GA) optimization technique is proposed that can efficiently schedule the time of the initiation of the charging process of each EV together with the preferred charging level. The algorithm explores two charging policies, namely the Net Zero and the Cost Minimization policies and models the QoS as a function of the incomplete or delayed EV charges. The two charging policies are compared to the simplest policy, named as Serve Upon Arrival. It is observed that great cost benefits can be achieved without affecting the overall QoS for the users.