Research On The Chronological And Spatial Modeling Methods Of Photovoltaic Generation And Their Application In The Probabilistic Analysis Of Power Systems

Solar photovoltaic (PV) generation with green and unpolluted strengths has kept upthe rapid pace of development in recent years, but it is an intermittent source of energy,whose power output depends seriously on the climate factors like solar irradiation,temperature and so on. Consequently the PV power is random. A large number of PVgenerators connected into power systems are a new challenge for the secure andeconomic operation of power system. The probabilistic analysis methods under thesufficient consideration of PV’s uncertain are thus employed for quantified evaluatingthe impact of PV generation on power system planning and operation. Consequently, therich, all-rounds, and comprehensive information can also be provided for power systemsoperators.This dissertation is supported by “Research on the basic theories of mid-long termprobabilistic modeling and evaluation of transmission power system”, subsidized by theNational Natural Science Foundation of China, No.51177178, and “The key technologyresearch and development of micro-grid incorporating distributed generators”,subsidized by The National High Technology Research and Development of China863Program, No.2011AA05A107. The study is carried out for probabilistic modelingmethods considering the randomness/chronological/spatial correlation of PV generation,probabilistic power flow and probabilistic optimal power flow calculation methods forpower system containing PV generators. And finally they are applied into the secure andeconomic analysis of power system or virtual power plant. The detailed study includes:1) The probabilistic modeling method of PV generation based on nonparametrickernel density estimation theoryThe present probabilistic PV power modeling methods are usually based onparametric density estimations which rest on some probabilistic assumption, and cannotconsider the effects of all the stochastic factors involved. A new probabilistic modelingmethod of PV power is proposed based on nonparametric kernel density estimation andan improved optimal bandwidth selecting method without depending on the realdistribution is also developed. The comprehensive test index is also presented based onthe goodness-of-fit and posteriori tests. The proposed probabilistic modeling andbandwidth selection methods are verified using PV power data of Chongqing andHangzhou, which are quite different in sunshine condition. The test results also demonstrate the adaptability of the new probabilistic modeling method to PV powerwith distinct stochastic characteristics.2) The probabilistic modeling method for PV generation considering the spatialcorrelationThe power outputs of PV at the adjacent locations are correlated with each otherand also with the loads which are sensitive to weather conditions. Thus, this dissertationproposes a probabilistic modeling method considering such correlation. The correlationsbetween PV outputs and loads obeying different probability distributions can beconsidered by the proposed method. And a probabilistic power flow analysis methodbased on Monte Carlo is also developed to deal with the randomness and correlation ofPV outputs and loads. The Latin Hypercube Sampling method, an efficient samplingmethod, is also incorporated to decrease the computation burden. The measured powerdata of PV generators in USA and the69-node distribution network are used todemonstrate the correctness and effectiveness of the presented method and analyze theimpacts of different PV probabilistic models and correlations between PV outputs andloads on the probabilistic power flow of distribution networks.3) The chronological probability modeling method for PV generationA chronological probability modeling method for PV generation is proposed on thebasis of conditional probability and nonparametric kernel density estimation. In additionto randomness of PV power, the correlation of PV powers between adjacent time pointsand the uncertainty of start and end moments of PV output can be represented. Thestochastic sampling method of PV time series is also proposed based on the rejectionsampling technique. The power data of three PV generators in different regions withdistinct weather conditions and34-node distribution network are used to demonstratethe correctness, effectiveness and adaptability of the presented method and itsapplication in the probabilistic evaluation of daily network loss of distribution network.4) The probabilistic power flow analysis method based on stochastic responsesurface method for power systems incorporating PV generationBased on stochastic response surface method, a probabilistic power flow analysismethod for power system incorporating PV generation is proposed. The collocationpoint selecting method based on linearly independent is also introduced to chooseoptimal collocation points. The proposed method can rapidly solve the probabilisticpower flow with high accuracy as well as considering the nonparametric model of PVgeneration and the correlations between PV outputs and loads. The34-node distribution system and IEEE39-node test system are used to verify the correctness andeffectiveness of the proposed method. The impacts of different probabilistic models ofPV generation and correlations between PV outputs and loads on the probabilisticpower flow are also analyzed.5) The probabilistic optimal power flow analysis method for virtual power plantcontaining PV generationIn order to reflect the impacts of random factors, like PV power and loads, on theeconomic operation of virtual power plant, a probabilistic optimal power flow analysismethod is proposed by combining the stochastic response surface method and interiorpoint method. The optimal power flow model minimizing the generation cost for virtualpower plant containing PV generation is established. The interior point method isemployed to solve the optimal power flow and the stochastic response surface method isused to deal with the randomness of PV outputs and loads. The modified IEEE30-bustest system is used to verify the correctness and effectiveness of the proposed method.The impacts of the PV generation on virtual power plant are also analyzed.