Modeling Of Wind Power Probabilistic Characteristics And Its Application On Risk Analysis

With problems of energy and environment becoming gradually serious, wind power generation has been developed rapidly as its mature technology, clean and renewable property and giant reserves. Because of the increasing capacity of wind power integration into power system, the uncertainty of wind power has posed great challenges to economic dispatch, security and stability of power system. Thus, modeling of wind power probabilistic characteristics can describe the uncertainty of wind power reasonably, which can provide the essential probabilistic information for adjustment measures such as risk analysis and economic dispatch. Longitudinal moment probabilistic model and variation Markov chain model are proposed using long-term operation data and variation of wind power respectively. The application of each model is discussed respectively in the aspect of risk analysis for day-ahead economic dispatch and online risk analysis. The main researches are as follows:(1) The longitudinal moment Markov chain model is proposed, in which the division of state space is improved compared with traditional mothed and the set of transition probabilities matrices according to moments is provided. Combining with obtainded achievements on distribution charicteristics of longitudinal moments by research group, the longitudinal moment probabilistic model of wind power is proposed, in which the longitudinal moment distribution model and the longitudinal moment Markov chain model are included. The inherent law of probabilistic distribution at different moment and the inherent transition information between moments are described by them two, respectively.To illustrate the effect of improvement, a wind power forecasting method based on longitudinal moment Markov chain model is put forward. The case study shows that the proposed method achieves a higher forecasting precision than that based on traditional Markov chain model. The inherent law according to moments in the long-term operation data of wind power is summarized by the longitudinal moment probabilistic model. So the distribution and change of wind power are fully described, which can provide probabilistic conditions for optimization decision of power systems containing wind farm.(2) A novel risk analysis method for day-ahead dispatching plan of power system containing wind farm is proposed in basis of wind power longitudinal probabilistic model. Operational risk caused by randomness of wind power is assessed before the execution plan. To analyze out-of-limit line flow, the severity function is defined to measure the proximity to line thermal limit. The independent risk index of single moment based on longitudinal moment probabilistic model expresses the risk of out-of-limit line flow caused by random wind power at corresponding moment. Meanwhile, the related risk index of adjoining moments based on longitudinal moment Markov chain model expresses the risk and its change caused by the state change of wind power output. The case study shows that the proposed method can fully describe the operational risk and its change caused by the fluctuations of wind power output. So the results calculated by the new method have reference value to day-ahead dispatching plan formulation and adjustment.(3) The Markov chain model of wind power variation is put forward, which can directly describe the probabilistic characteristic of variations between adjoining moments. Based on that, a fast estimation for operation state and online risk analysis method aiming at power system containing wind farms are proposed. In actual simulation, grid-connected node voltage is used to analyze the change of operation state and risk index under different fluctuations of wind power output. The result of simulation shows that the proposed model and methods can preferably meet the features and requirements of online risk analysis by rapidly predicting the wind power variation and estimating the power system operation state. Therefore, the proposed model and methods will provide reference information for decision-maker to take preventive and control measures to deal with wind power fluctuation.