Effect Of Wind Power Penetration Limit Based On Peak Load Regulation Capacity Of Grid

Peak load regulation is one of the main problems that are holding back China's power grid being connected with large-scale wind farms. The research of regulation capacity limit of conventional generating units is a prerequisite to solve the problem.At present, all of researches are not suitable for the future of wind power technology development because these don't consider the impact of wind power forecasting. In order to meet the development trend of wind power technology, this dissertation broke through the limitations of existing boot mode, and given a method that can calculate regulation capacity limit of conventional generators. The results can not only grasp on conventional generators acceptable to the largest wind power, can also learn peaking capacity deficiencies of existing power supplies. All that can provide a scientific basis for further equipment modification.The main contents in this dissertation include studying regulation capacity limit of conventional generators, and studying wind power prediction method. The specific achievements are as follows:Studied the relationship between spread and between data density, according to the uneven distribution of wind speed, a new T-S(Takagi-Sugen, T-S) fuzzy GRNN(General Regression Neural Network, GRNN) wind power prediction method is proposed. In this method, the distance between training data and density center is a criterion, the input data space is divided into several sub-space, a GRNN model is created in every sub-space, and a final prediction result is calculated by defined fuzzy rulers. In order to get accurate data center, the data field clustering method is used in this dissertation. In the method, an optimum objective function for reducing influence of sample deviation is constructed and an approximate solution is given of optimum affection factor. For training the fuzzy parameters, the dissertation uses a good performance PSO(Particle Swarm Optimization, PSO) search algorithm. The simulation results prove the correctness of the model and algorithm.A normal distribution is usually used to model wind power forecast error, but it is not valid in some special cases. For solving this problem, an interval estimation method of wind power forecasts based on Beta distribution is proposed. In this method, according to the security and economy requirements of grid reserve capacity, a new universal optimization model is proposed to calculate minimum probability interval of Beta probability density function. By introducing the inverse function of Beta PDF, equality constrained optimization problem is simplified into an unconstrained optimization problem, and a smart algorithms is presented. The simulation result shows correctness of the algorithms and the rationality of Beta PDF. According to the situation that no reliable wind power prediction results can be used, a new optimization model is created for calculating negative regulation capacity limit of conventional generators in the dissertation. The dissertation studies the mechanism of the negative peak load regulation of conventional generators based on active power balance equation., a new model is proposed to calculate the limit of capability of negative peak load regulation, and a practical two-tier algorithm is given. The simulation results prove the correctness of the model and algorithm.According to the situation that wind power prediction is used in generation dispatch schedule method, a new optimization model is created for calculating regulation capacity limit of conventional generators in the dissertation. The influence of wind power prediction for generation dispatch schedule is analyzed. A principle is summarized to create model of regulation capacity limit. A concept of regulation units state chain is defined. On the concept, an optimization model is deduced. According to multi-source tree of regulation units state chain, an algorithm is proposed. The simulation results prove the correctness of the model and algorithm.