Title :
Cost and peak-to-average ratio reduction of electricity usage via intelligent EV charging
Author :
Nan Zou ; Lijun Qian ; Attia, John ; Changsheng Ai
Author_Institution :
Dept. of Electr. & Comput. Eng., Prairie View A&M Univ., Prairie View, TX, USA
Abstract :
This paper mainly focuses on the minimization of total energy cost and reduction of peak-to-average ratio of energy usage for multiple homes in a community. Taking into account different characteristics of energy usage of a future home, such as whether an electric vehicle (EV) is available, whether the EV is for daily commute, as well as the battery charging characteristics, a constrained optimization problem is formulated. Two pricing schemes are considered, the real-time energy price from The Electric Reliability Council of Texas (ERCOT), and a functional energy price which is not only based on the given ERCOT price pattern changing along with time, but also a function of the usage quantity of electricity during each time interval. It is demonstrated in this study that EV is going to play a major role in future home energy usage, as a result, intelligently charging EV would reduce the electricity cost dramatically and reduce the peak-to-average ratio of electricity usage.
Keywords :
battery powered vehicles; optimisation; pricing; ERCOT price pattern; The Electric Reliability Council of Texas; battery charging characteristics; constrained optimization problem; electricity usage; functional energy price; home energy usage; intelligent EV charging; peak-to-average ratio reduction; pricing schemes; real-time energy price; time interval; total energy cost minimization; usage quantity; Batteries; Communities; Electricity; Home appliances; Optimization; Peak to average power ratio; Pricing; constrained optimization; electric vehicle; peak-to-average ratio; smart grid;
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2014 IEEE 9th Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4799-4316-6
DOI :
10.1109/ICIEA.2014.6931232