A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Energy

Thesis (M.Eng.) - Asian Institute of Technology, 2018

The EVs can achieve zero CO2 emission to be environment friendly. It has potentials to reduce the cost of the transportation systems. The development of the EVs is on an accelerated pace. As new loads in the distribution grid, charging EVs could influence the operation of the distribution grid. Firstly, this research aims to show the impact of EV charging based on different EV penetrations using Hua-Hin substation no.3 to be a test system. To make the outcome more realistic, the stochastic process consisting of Goodness-of-fit (GOF) test and Monte Carlo Simulation are used in this research. The outputs from stochastic process including random driving distance, random arrival time and random departure time and those data would be used to represent the EV charging demand consumptions. Finally, the smart charging scheme focusing on minimized charging cost by considering the network constraints is proposed. The smart charging scheme is developed by using Digsilent Programming Language (DPL) in DIgSILENT software focusing on minimized charging cost and considering the network constraints. Smart charging applications are implemented in different charging schemes comprising of random charging scheme, smart charging based TOU pricing, and smart charging based dynamic pricing in order to show the best solution which can achieve the customer{u2019}s benefit in form of economic charging. Similarly, the grid utilities can also get the benefits such as peak demand curtailment by shifting EV consumptions to off-peak or lowest price hours, increase operating capacity of electrical devices. As a result, the smart charging scheme is a useful solution to support the rapidly grow of EV technology transition in the nearly future effectively.