Research On Wind Power Prediction And Probabilistic Power Flow Of Microgrid

Wind generation system is an important unit of a microgrid. The randomness of wind and windpower has a great adverse influence on operation characteristic of a microgrid. It is of greatsignificance for energy management system of microgrid to predict power flow based on theprediction of wind speed and wind power production. To address these problems, wavelet andsupport vector machine prediction model and the ridgelet neural network trained by improveddifferential evolution algorithm are proposed to enhance prediction precise of wind speed and windpower production. Futher, a probabilistic prediction model is established to assess the uncertaintyof predictive results. Based on these results, the power flow prediction model of a microgrid isestablished to predict and analyze the influence of wind randomness on the microgrid. Theoutcomes of these researches are of great reference value for energy management system of amicrogrid. The main innovative achievements are as follows:1. For the aspect of hourly wind speed prediction, the support vector machine (SVM) predictionmethods by the historical meteorological data are studied. The parameters of least square supportvector machine (LSSVM) are determined by adaptive particle swarm optimization (APSO). A windspeed forecasting model based on wavelet and support vector machine (Wavelet-SVM) isestablished also in which the original wind speed sequences are decomposed into coarse componentsand detail components firstly, and each wavelet component is separately forecasted bycorresponding support vector machine model. Finally, the forecasting results of original wind speedseries are achieved by wavelet reconstruction. In additional, a new forecast method based onpredictive error correction is presented in the paper. The preliminary predicted wind speed iscorrected with predict error to improve the prediction accuracy.2. For the aspect of hourly wind power prediction, the ridgelet neural network based on improveddifferential evolution algorithm is adopted to predict wind power production by the means of directlyprediction and indirectly prediction methods. The simulation results show that the approximationability for high-dimensional function, generalization performance, and train speed of the proposedridgelet neural network are improved. By the prediction model, the wind power productionpredictive precise is enhanced.3. Based on the analysis of uncertainties of wind speed and wind power prediction, theprobabilistic prediction models are established to assess the uncertainty of wind and wind powerpredicted results from two aspects, one is to calculate the probability of actual wind speed within acertain range by the conditional probability calculation method, and the other is to calculate theconfidence intervals of predictive wind power corresponding to a certain confidence level by meansof non-parametric confidence interval estimation on basis of analyzing the statistical regularities of power forecast errors and power fluctuation values.4. For the power flow prediction of microgrid, the influence of the wind randomness on theoperation characteristic of a microgrid is studied firstly, and then Markov process and Monte Carlosimulation methods are combined together to carry out probabilistic forecasting of power flow.Finally, the impact of wind speed on the microgrid power flow is analyzed under the operationmodes of both grid-connection and off-grid. In additional, a conditional joint probability model inview of the randomness of wind and light is established to research the probabilistic power flow ofa microgrid.