Power system optimization analysis considering power prediction of PV power station
DOI：10.7667/PSPC170035
Key Words:solar irradiance prediction  improved BP neural network  adaptive adjustment learning rate  double objective optimization  predictive value comparison
Author NameAffiliation
YANG Qiuxia School of Electrical Engineering, Yanshan University, Qinhuangdao 066000, China 
LIU Tongxin China Resources Power Cangzhou Yun Dong Co., Ltd, Cangzhou 061004, China 
GAO Chen School of Electrical Engineering, Yanshan University, Qinhuangdao 066000, China 
LI Maolin State Grid Zouping Power Supply Company, Binzhou 256200, China 
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Abstract:The indirect prediction method is used to forecast the PV power, and the solar irradiance forecast is combined with the light-to-electric model to predict the PV output. In order to solve the shortcomings that the traditional BP algorithm in the short-term solar irradiance prediction is easy to fall into the local optimum and has slow convergence rate, the adaptive adjustment learning rate and the steepness factor are introduced to establish the solar irradiance prediction model. In the bipolar Sigmoid function, the steepness factor is added to improve the convergence speed of the BP algorithm, the normalized input data is limited to [-1, 1] to facilitate data processing, and the adaptive adjustment learning rate is introduced to adjust network weight and to improve convergence performance. In order to study the optimization of power system with PV power station, a double objective optimization model to minimize daily composite cost and daily waste gas emission of system is established. And the two-target bacterial population chemotaxis algorithm is used to optimize the system. Examples show that the improved BP neural network algorithm can effectively improve the prediction accuracy and enhance the generalization ability of the neural network model, and it has good practicability. Forecasting the PV output can arrange the unit output and rationally absorb the PV resources. This work is supported by National Natural Science Foundation of China (No. 61573303) and Natural Science Foundation of Hebei Province (No. E2016203092).
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