DocumentCode :
135429
Title :
A two-layer evolution strategy particle swarm optimization algorithm for plug-in hybrid electric vehicles at residential distribution grid
Author :
Jun Tan ; Lingfeng Wang
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Univ. of Toledo, Toledo, OH, USA
fYear :
2014
fDate :
27-31 July 2014
Firstpage :
1
Lastpage :
5
Abstract :
This paper proposes a two-layer evolution strategy particle swarm optimization (ESPSO) algorithm to integrate PHEVs into a residential distribution grid. A novel business model is developed for PHEVs to provide ancillary service and participate in peak load shaving. A virtual time-of-use rate is proposed to reflect the load deviation of the system. Then, an objective function is designed to aggregate the peak load shaving, power quality improving, charging cost, battery degradation cost and frequency regulation earnings into one cost function. The ESPSO approach can benefit the system in four aspects by: (1) improving the power quality; (2) reducing the peak load; (3) providing frequency regulation service; and (4) minimizing the total virtual cost. Finally, various simulations are carried out based on different control strategies, and the results have demonstrated the effectiveness of the proposed algorithm.
Keywords :
frequency control; hybrid electric vehicles; particle swarm optimisation; power supply quality; ESPSO; ancillary service; frequency regulation service; load deviation; peak load shaving; plug in hybrid electric vehicles; power quality; residential distribution grid; two layer evolution strategy particle swarm optimization algorithm; virtual time of use rate; Batteries; Degradation; Frequency control; Load modeling; Particle swarm optimization; Plug-in hybrid electric vehicles; Plug-in hybrid electric vehicle (PHEV); battery degradation; evolution strategy particle swarm optimization (ESPSO); frequency regulation; stochastic modeling; vehicle-to-grid (V2G); virtual time-of-use (vTOU) rate;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
PES General Meeting | Conference & Exposition, 2014 IEEE
Conference_Location :
National Harbor, MD
Type :
conf
DOI :
10.1109/PESGM.2014.6939361
Filename :
6939361
Link To Document :
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