Energy Management For A Residential Microgrid Including A Vehicle-to-grid System

With the rapid development of the smart grid, distributed energy-based micro grid has been widely used in smart grid, to enhance the flexibility and economy of power grid and become an important direction for future development of smart grid in user side; moreover, electric vehicle(EV) as a typical portable energy storage device, show great market potential owing to its high efficiency, low emission characteristics. The energy storage unit or Vehicle-to-Grid(V2G) system, as is considered in this work, can provide additional energy to the residential micro grid when renewable energy in short supply, and charge EV When an adequate supply of renewable energy or electricity prices are low. This process is used to generate savings for the V2 G system owner, but may also have a preferable benefit in reducing power system overloads during peak hours.An important issue in the process of residential microgrid operation control is that how to coordinate the output of the grid and the EV to minimize the residential microgrid operation cost and to meet the all kinds of constraints and the family load demand. In order to solve this problem, this paper firstly introduced the research background and significance, further study of the application of microgrid energy management strategy. Then analyzed and modeling the various components of the residential microgrid module, build two family microgrid structures, proposed three energy management strategies, and finally use the improved particle swarm optimization algorithm as the method for solving the scheduling model.This paper firstly developed the structure of a residential microgrid which consists of EV, wind turbine, fuel cell, super capacitor and family load. The family load can be divided into basic load and controllable load. Three energy management strategies are proposed for the residential microgrid with EV. In the first strategy the output of EV only depend on the current SOC of EV and the gap between the demand power and the generated power. Considering the real-time electricity price and family controllable load, the other strategies are more complex and can greatly reduce family microgrid operating costs. The third strategy is on the basis of the second one, change the reference SOC by predicting the output of renewable energy, to maximize the use of EV’s batteries. According to the three strategies, proposed the lowest operating costs and the lowest comprehensive cost optimization, and analyzes the energy scheduling result using different strategies under different optimization goal. The result show that the strengths and weaknesses of each strategy and the EV participate in V2 G can let the EV owner and the grid operators to achieve a win-win situation.