Optimal Operation Strategy of Power Systems in the Presence of Smart Grids and Electric Vehicles

Due to the dramatic growth in the electric power industry and the large gap between small and large loads and loads economic crisis that has gripped most of the world, an issue critical to the operation of power plants has become. Also growing use of traditional sources of energy and lack of response to this need has created a lot of problems around the world. Including that they can reduce fossil fuel resources, and the environmental impact of greenhouse gas increases noted. This problem has led to concerns of environmentally friendly technologies such as electric cars get more attention. Considering to the capability of bi-directional exchange power in vehicles, electric vehicles are a significant number of network connections To coordinated the management and intelligent control of an entity causing the network to be connected together, Considering In this type of electric vehicles as well as hardware that is installed in the parking lot, as they can quickly set up a small virtual power plant set-up costs are too high and behave. The main focus of this thesis is to develop a model in order to exploit the electrical grid in the smart grid the intelligent power network operation in the presence of electric vehicles can be connected to the network has been studied. This paper presents an optimal load management strategy for residential consumers that utilizes the communication infrastructure of the future smart grid. The strategy considers predictions of electricity prices, energy demand, renewable power production, and power-purchase of energy of the consumer in determining the optimal relationship between hourly electricity prices and the use of different household appliances and electric vehicles in a typical smart house. The proposed strategy is illustrated using two study cases corresponding to a house located in Zaragoza (Spain) for a typical day in summer. Results show that the proposed model allows users to control their diary energy consumption and adapt their electricity bills to their actual economic situation.