Probability Distribution Analysis At Longitudinal Time And Energy Storage Optimization Of Wind Power

Along with the increasingly severe problems concerning energy and environment, and increasing exhaustion of non-regeneration energy such as coal and petrol, the authorities all over the world have already regarded renewable energy as an important solution. Among the renewable energy, wind energy has become the energy with the most large-scale exploitable potential since it has the advantages of less pollution, larger reserves and farmland-free. In recent years, with the increasing development of wind generation technology, its scale and capacity are enlarging year by year. Therefore, the impact on distribution network of wind generation is under intense focus.Wind generation has the characteristics of fluctuation and intermittency, which lead to the problems of uncertainty and forecasting difficulty. Large-scale wind generation integration into power grid brings challenges such as safe and stable operation of power grid, power quality and so on. In this way, how to stabilize wind generation fluctuation has become an important research project. In this background, many aspects such as wind power fluctuation characteristics, optimization of power storage capacity, and energy storage after wind power classification are researched, whose aim are to increase wind power reliability, utilization rate and dispatching. In this thesis, the main tasks are as follows:First of all, a new method to analyze wind power fluctuation characteristic called longitudinal time probability analysis is proposed. Based on measured historical data, this method uses statistical data of daily wind power output at the same time point of365days or longer, and obtains the probability distribution results of96time points. Then wind power output probability characteristic expressed by piecewise function could summarized by function fitting, and pre-evaluation of wind power predicted value could be obtained. This method not only proves that probability distribution characteristic on longitudinal time is the inherent attribute of wind power output, but also provides evidence for subsequent power classification.Secondly, in order to make wind power output satisfy dispatching demand, energy storage system is introduced, and the optimization method of power storage capacity concerning battery lifetime and excessive discharge phenomenon is proposed. In this method, the minimum synthetically economic cost composed of three parts, namely the operating cost converted from the loss of battery life due to the depth of discharge and the over-discharge, the penalty cost converted from the amount of the energy that does not meet the demand of expected output and the inherent cost of energy storage devices, is taken as the optimization objective, and the power, the capacity and the battery life are taken as constraints, and the optimized energy storage capacity is solved by genetic algorithm. After deploying this capacity, wind power fluctuation could be minimized from aspects of economics and reliability, which satisfies dispatching demand.Thirdly, in order to decrease energy storage system capacity and cost as well as increase wind power utilization rate, a wind power classification method based on probability analysis at longitudinal time and interval estimation theory is proposed, and optimization method of power storage capacity is conducted. In wind power classification, wind power is classified as level1output, level2output and level3output. The first two have higher reliability, and could be utilized in wind power dispatch. Level3output is used in energy storage capacity optimization. Energy storage capacity has a smaller value using this method, which decreases energy storage cost substantially. The summation of three level outputs can be used as wind power output, which could increase wind power plant output stability and utilization rate effectively.Lastly, based on the research above, wind power output is predicted by using wind power output with smaller energy storage system after classification as historical data. In comparison with wind power output prediction without energy storage system, the former one has much higher prediction accuracy. This method of energy storage after classification is of practical meaning to realize wind power reliability and dispatch.