الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
 |  | from | 206 |  |  |
| | | from | 206 | | |
Abstract
The increasing awareness of the negative effects of climate changes and the need for alternative energy sources have led to the widespread acceptance of electric vehicles as a safe alternative specially with increasing fuel prices. As electric vehicles are volatile load, there has been an increase in the need for the charging infrastructure for electric vehicle charging. Therefore, there is a great need to develop such charging approaches, time period would diminish peak load growing by shifting charging load to off peak periods by defining an optimal schedule for electric vehicle charging.
In this thesis, a techno-economic approach aims at providing an optimum solution for charging / discharging of electric vehicles in smart grids. The novelty is to obtain a multi-objective model introduced to solve the electric vehicle scheduling problem in electrical distribution system over the day/week load-ability uncertainty. The introduced method gives a solution for prosumer (producer /consumer) charging schedule problem. The anticipated method applied to satisfy both customer and utility benefits, where the first objective is the cost function for G2V/V2G mode and the second objective is the multi objective single representation which combines 1st objective (total price) with voltage deviation function for showing power quality.
In addition, the efficiency of the proposed methodology using different meta-heuristic algorithms has been investigated on IEEE 69 bus network. Various techniques are applied; grey wolf optimization algorithm (GWOA), jelly fish optimization algorithm (JSOA), humming bird optimization algorithm (HOA), and bat Algorithm (BOA).
Furthermore, the system is evaluated based on different cases and scenarios for getting optimal coordinated charging / discharging schedule for EVs integration in power grid with selection the best price of energy is applied.
Moreover, the introduced methodology considers the EVs are charged and discharged in one-time interval with selection the best price of energy taking into consideration whether electric vehicles are in consumption mode or in generation mode with preserving the voltage deviation and operational system constrains.
The results for each case are depicted. Comparison between the mentioned algorithms were executed for confirming and demonstrating the robustness and proved that the tuned JSOA (jelly fish optimization algorithm) and tuned GWOA (Grey wolf optimization algorithm) surpassed the HOA (Humming bird optimization and bat algorithm in term of reaching better values for the EVs coordinated charging/ discharging schedule objective functions.
Keywords— electric vehicles, electric vehicles charging coordination, IEEE 69-bus system, meta-heuristic optimization method.