Abstract
With the increasing penetration of the photovoltaic (PV) generator, uncertainty surrounding the power system has increased simultaneously. The uncertainty of PV generation output has an impact on the load demand forecast due to the presence of behind-the-meter (BtM) PV generation. As it is hard to assess the amount of BtM PV generation, the load demand pattern can be distorted depending on the solar irradiation level. In several literature works, the influence of the load demand pattern from BtM PV generation is modeled using environmental data sets such as the level of solar irradiation, temperature, and past load demand data. The particulate matter is a severe meteorological event in several countries that can reduce the level of solar irradiation on the surface. The accuracy of the forecast model for PV generation and load demand can be exacerbated if the impact of the particulate matter is not properly considered. In this paper, the impact of particulate matter to load demand patterns is analyzed for the power system with high penetration of BtM PV generation. Actual meteorological data are gathered for the analysis and correlations between parameters are built using Gaussian process regression.
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