Energies 2014, 7(7), 4629-4647; doi:10.3390/en7074629
Article

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

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Received: 19 May 2014; in revised form: 10 July 2014 / Accepted: 11 July 2014 / Published: 22 July 2014
(This article belongs to the Special Issue Energy Transitions and Economic Change)
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Abstract: 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.
Keywords: economic dispatch; stochastic optimization; electric vehicles; wind power; learning automata
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Zhu, J.; Jiang, P.; Gu, W.; Sheng, W.; Meng, X.; Gao, J. Finite Action-Set Learning Automata for Economic Dispatch Considering Electric Vehicles and Renewable Energy Sources. Energies 2014, 7, 4629-4647.

AMA Style

Zhu J, Jiang P, Gu W, Sheng W, Meng X, Gao J. Finite Action-Set Learning Automata for Economic Dispatch Considering Electric Vehicles and Renewable Energy Sources. Energies. 2014; 7(7):4629-4647.

Chicago/Turabian Style

Zhu, Junpeng; Jiang, Ping; Gu, Wei; Sheng, Wanxing; Meng, Xiaoli; Gao, Jun. 2014. "Finite Action-Set Learning Automata for Economic Dispatch Considering Electric Vehicles and Renewable Energy Sources." Energies 7, no. 7: 4629-4647.


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