Finite Action-Set Learning Automata for Economic Dispatch Considering Electric Vehicles and Renewable Energy Sources

Energies

Junpeng Zhu¹  Ping Jiang¹  Wei Gu¹  Wanxing Sheng²  Xiaoli Meng² 

[1] School of Electrical Engineering, Southeast University, Nanjing 210096, China; E-Mails:;China Electric Power Research Institute, Beijing 100192, China; E-Mails:

关键词: economic dispatch;    stochastic optimization;    electric vehicles;    wind power;    learning automata;   

DOI  :  10.3390/en7074629

来源: mdpi

【 摘 要 】

The coming interaction between a growing electrified vehicle fleet and the desired growth in renewable energy provides new insights into the economic dispatch (ED) problem. This paper presents an economic dispatch model that considers electric vehicle charging, battery exchange stations, and wind farms. This ED model is a high-dimensional, non-linear, and stochastic problem and its solution requires powerful methods. A new finite action-set learning automata (FALA)-based approach that has the ability to adapt to a stochastic environment is proposed. The feasibility of the proposed approach is demonstrated in a modified IEEE 30 bus system. It is compared with continuous action-set learning automata and particle swarm optimization-based approaches in terms of convergence characteristics, computational efficiency, and solution quality. Simulation results show that the proposed FALA-based approach was indeed capable of more efficiently obtaining the approximately optimal solution. In addition, by using an optimal dispatch schedule for the interaction between electric vehicle stations and power systems, it is possible to reduce the gap between demand and power generation at different times of the day.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.