Demand response control for PHEV charging stations by dynamic price adjustments

Because of their economical operation and low environmental pollution, PHEVs (Plug-in Hybrid Electric Vehicles) are rapidly substituting gasoline vehicles. However, there still exist obstacles to proliferating their use, such as their relatively short driving range and long battery charging time. At the same time, it is recognized that the current increasing trend of PHEV use will have a serious impact on the stability of power grids (i.e., electricity providers). Along with improving the performance of PHEVs, the installation of charging stations, which addresses such problems, is essentially required in smart grid communities. This paper proposes an operational framework for multiple PHEV charging stations. To maintain the power grid stability, regulating electric supply for charging stations through support planning is an attractive approach. In this direction, we determine a condition under which customers can receive improved QoS (Quality of Service), and provide an algorithm which allocates PHEVs into the condition. Our analysis is based on a multi-queue system, used as a model of charging stations whose dynamics we investigate. Specifically, our interest is the performance change when demand responses (i.e., the behavior of customers) are controlled. We proceed with our investigation in two steps: In the first step, we consider the PHEV allocation problem. We formulate an optimization problem which can minimize the waiting time of customers and obtain its solution. Then, we additionally regard the size constraint of charging stations and propose an optimal PHEV allocation algorithm. In the second step, we modify this algorithm to work in realistic scenarios. If PHEVs do not receive any incentives (or penalties), there is no restriction to control their allocation. At the station side, we suggest price control methods and show that the optimal allocation can be attained by them. For each step, we provide test results to validate our analysis.