Abstract
With the large-scale integration of distributed photovoltaic (DPV) power plants, the uncertainty of photovoltaic generation is intensively influencing the secure operation of power systems. Improving the forecast capability of DPV plants has become an urgent problem to solve. However, most of the DPV plants are not able to make generation forecast on their own due to the constraints of the investment cost, data storage condition, and the influence of microscope environment. Therefore, this paper proposes a master-slave forecast method to predict the power of target plants without forecast ability based on the power of DPV plants with comprehensive forecast system and the spatial correlation between these two kinds of plants. First, a characteristics pattern library of DPV plants is established with K-means clustering algorithm considering the time difference. Next, the pattern most spatially correlated to the target plant is determined through online matching. The corresponding spatial correlation mapping relationship is obtained by numerical fitting using least squares support vector machine (LS-SVM), and the short-term generation forecast for target plants is achieved with the forecast of reference plants and mapping relationship. Simulation results demonstrate that the proposed method could improve the overall forecast accuracy by more than 52% for univariate prediction and by more than 22% for multivariate prediction and obtain short-term generation forecast for DPV or newly built DPV plants with low investment.
Highlights
Electricity power consumption increases drastically in recent years, and with the decreasing supply of fossil fuels, the renewable generation, especially photovoltaic (PV) generation, has developed rapidly as well [1]
distributed photovoltaic (DPV) generation will greatly affect the stability of power systems [3, 4]. erefore, the accurate generation forecast of DPV is significant for the scheduling and stable operation of power systems
Several randomly chosen target plants are simulated, and the results show saddle-shaped curves similar to those in Figure 8, and the spatially correlated reference plants are usually located in time zones close to the target plants. ere exists a minimum among the standardized Euclidean distance (SED) values achieved with different time shifts, and the most spatially correlated reference plant may not be synchronous with the target plants
Summary
Jia Ning ,1,2 Guanghao Lu ,1,2 Sipeng Hao ,1,2 Aidong Zeng ,1,2 and Hualei Wang 3. Most of the DPV plants are not able to make generation forecast on their own due to the constraints of the investment cost, data storage condition, and the influence of microscope environment. The pattern most spatially correlated to the target plant is determined through online matching. E corresponding spatial correlation mapping relationship is obtained by numerical fitting using least squares support vector machine (LS-SVM), and the short-term generation forecast for target plants is achieved with the forecast of reference plants and mapping relationship. Simulation results demonstrate that the proposed method could improve the overall forecast accuracy by more than 52% for univariate prediction and by more than 22% for multivariate prediction and obtain short-term generation forecast for DPV or newly built DPV plants with low investment
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