A new optimization algorithm based on teacher learning algorithm for optimal operation of electric grids

Article type: Research Article

Authors: Bahmani-Firuzi, Bahman^{a; *} | Khorshidi, Reza^b

Affiliations: [a] Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran | [b] TCF (Telecommunication Company of Fars)

Correspondence: [*] Corresponding author. Bahman Bahmani-Firuzi, Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran. Tel./Fax: +98 9177154688; E-mail: bahman_bah@miau.ac.ir.

Abstract: The future smart grids will contain a high number of Plug-in Electric Vehicles (PEVs) that will move in the grid widely. The high penetration of these devices will bring new challenges regarding the optimal operation and management of the system. In this way, this paper proposes a realistic framework to first model PEVs movements in the grid and second schedule their movement for minimizing the costs. The cost function consists of the total network cost for supplying the electric loads and PEVs for 24 hour time horizon. According to the high complexities of the problem, a new optimization framework based on teacher learning algorithm (TLO) with a new modification method is proposed to search the problem space thoroughly. The feasibility and satisfying performance of the proposed optimization framework are examined on the IEEE test system.

Keywords: Plug-in Electric Vehicle (PEV), Vehicle-to-Grid (V2G), Modified Teacher Learning Optimization (MTLO)

DOI: 10.3233/IFS-162145

Journal: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 6, pp. 3839-3846, 2017