OPTIMAL SCHEDULING OF ELECTRIC VEHICLE'S CHARGING/DISCHARGING
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The advent of Electric Vehicles (EVs) demonstrates the effort and determination of humans to protect the environment. However, as the number of EVs increases, charging those EVs consume large amount of energy that may cause more pressure on Grid. On the other hand, the smart grid enables two-way energy flow which gives EVs the potential to serve as distributed storage system that may help mitigate the pressure of fluctuation brought by Renewable Energy Sources (RES) and reinforce the stability of power systems. Therefore, establishing efficient management mechanism to properly schedule EV charging/discharging behavior becomes imperative. In this thesis, we consider that EVs have one charging mode, Grid-to-Vehicle (G2V), and two discharging modes, Vehicle-to- Grid (V2G) and Vehicle-to-Home (V2H). In V2G, EVs send back their surplus power to grid, while in V2H, EVs supply the power for appliances in a house. We aim to design optimal algorithms to schedule the EV’s operations. We first consider an individual residential household with a single EV, where the EV can operate at all three modes. When the EV works in G2V mode, the owner pays the cost to utility company based on the real-time price (RTP). When the EV works in V2G mode, the owner earns the reward based on the market price from utility companies. In V2H, the owner uses the EV battery to provide power to appliances in the house rather than purchasing from the utility. We propose a linear optimization algorithm to schedule the EV’s operations based on the RTP and market price subject to a set of constraints. The objective is to minimize the total cost. The results show that in general the EV chooses G2V when the RTP is low, responding to demand response. When the RTP is high, the EV tends to work as V2H to avoid buying from the utility. When the market price is high, the EVs will perform V2G to obtain more revenue. Noting that it is not practical for a single EV to perform V2G, we further consider a different scenario in which a group of EVs is aggregated and managed by an aggregator. One example is a parking lot for an enterprise. Initially only V2G is considered, that is, EVs work as energy supplies and the aggregator collects the energy from all connected EVs and then transfers the aggregated energy to the grid. Each EV needs to decide how much energy to discharge to the aggregator depending on its battery capacity, remaining energy level, and etc. To facilitate the energy collection process, we model it as a virtual energy “trading” process by using a hierarchical Stackelberg Game approach. We define the utility functions for aggregator and EVs. To start the game, the aggregator (Leader) announces a set of purchasing prices to EVs and each EV determines how much energy to sell to the aggregator by maximizing its utility based on the announced price and sends that number to the aggregator. Then the aggregator adjusts the purchasing prices by maximizing its utility based on the optimal energy values collected from the EVs and the game process repeats till it converges to an equilibrium point, where the prices and the amounts of energy become fixed values. The proposed game is an uncoordinated game. We also consider power losses during energy transmission and battery degradation caused by additional charging-discharging cycles. Simulation results show the effectiveness and robustness of our game approach. At last, we extend the game to include G2V as well for the aggregated EV group scenario. That is, EVs may charge their batteries according to the RTP so that they can sell more to the aggregator to increase the profit when the purchasing price from the aggregator is attractive. We propose a SG-DR algorithm to combine the game model for V2G and the demand response (DR) for G2V. Specifically, we adjust the utility function for EVs and then update the constraints of the game to include the DR. Subject to the duration of parking period, we solve this optimization problem using our combined SG-DR algorithm and generate EVs’ corresponding hourly charging/discharging pattern. Results show that our algorithm can increase up to 50% utility for EVs compared with the pure game model. Finally, in conclusion, we summarize our work under different scenarios. Then we analyze the potential risk and propose the future trend of EV’s development in Smart Grid.