Short-term Multi-objective Economic Dispatch Of Power System Considering Wind Power Integrated System

As a kind of clean energy without coal consumption and pollution, wind powerhas attracted extensive attention all over the world, especially at the present that thedemand for the electricity is increasing. Wind power is also regarded as the mostprospect energy of the renewable power generation. However, as the stochastic andintermittent characteristic of wind power, the grid faces the security challenge whenlarge-capacity wind power integrating it.In this paper, the wind power integrated system has been considered in theshort-term multi-objective economic dispatch of power system to improve theeconomy and the safety of the power system, and to reduce the amount of coalconsumption and pollution emissions of the conventional unit at the same time. Firstly,direct at the operating characteristics of the wind farm, this paper has established thetotal cost function of wind farm. In this model, the impact of the scheduled powergeneration and the effective power on the system economy has also been taken intoaccount to improve the utilization of wind power resources. Secondly, given that thefluctuations of the adjacent time for the power farm, the adjusted quantity function ofwind farm power is set up to transmit wind power as smooth as possible. Furthermore,in order to reduce possible errors, the wind power scheduling is modified. In addition,because the system loss is dynamic in the short-term economic dispatch of the powersystem, the power flow is calculated in the wind power integrated system and the totalloss is treated as an objective function to reduce the grid power loss. In this paper, theVector Evaluated Genetic Algorithm (VEGA) is used to solve the multi-objectiveproblem. In order to ensure the optimality of the current generation, the advantageindividual will be kept and replaced the disadvantage individual during thecomputation process. Finally, the typical example is analyzed in the MATLAB7.10forfour different situations respectively on the base of the wind power forecasting. Theresults have demonstrated that the proposed model is feasible and effective.