Optimization Operation Of Microgrid

The demand of the electric energy increases with the development of the modern gird. This makes the gird’s safety more and more important. The micro-grid provides a perfect solution to this problem. With the exhaustion of fossil energy, the price of oil and coal and other resource is rising. The environmental problem cased by these source is getting more and more attention. The development of wind power and photovotaic power provide a perfect solution for these problem. However, these power’s intermittence is limiting its development. At the same time, micro-grids provide friendly accesses for wind power generation and photovoltaic power generation. With effective control of microgrid, the problem of intermittence can be solved. In order to improve the function of micro-grid, the demand response is getting more and more attention.This thesis take the family micro grid as an example and conside the time price, the expense and the comfort of the user to control the controllable load.The optimal operation model of the family microgrid is proposed and solved by the particle swarm optimization algorithm.The optimized operation of microgrid is realized. First of all, each part of household microgrid is analysised and modeled, taking account of its operating characteristics. The power output characteristics of each component are described accoding to the optimization objective. Because of different home loadcharacteristics, the appliances are classified into 3 kinds: shiftable-interruptable loads, shiftable-uninterruptable loads, environment-related loads, in the process of controling. Accoding to differenr loads, their optimization limitation and variables are given to make their operation strategy. Self-producing and self-using strategy are adopted for the photovotaic and wind power, so their power is consumed firstly. This paper takes both electricity cost and user’s satisfaction into consideration to move power left from peak load time to grain load time and flat load time to save money and optimizes the operation of the micro-grid with the use of particle swarm optimization algorithm. In the case study, the electricity cost of the micro-grid after the optimization is compared with the one without optimization. And the the optimization algorithm is verified.