This paper investigates the uncertainty of the dayahead distribution system scheduling considering the random variations of both Photovoltaic-based distributed generator (PVDG) output power and load. Instead of Monte-Carlo simulation (MCS), a two-point estimation method (2PEM) is applied to obtain accurate and computation-efficient analysis results. Based on the two-year real-world hourly weather and load data in the city of Ottawa, the estimation accuracy of the 2PEM has been verified in an equivalent 44 kV distribution feeder system. In terms of computational efficiency, the 2PEM can significantly reduce the computation burden with comparison to MCS. By using the 2PEM, the impact of PV-DG output power and load variations on the uncertainty of the distribution system scheduling under different seasons is thoroughly studied. The analytical results show that the range of standard deviation of optimally scheduled DG generation for this distribution feeder system is larger in summer than that in winter.

Additional Metadata
Keywords Correlation, Distribution system, Photovoltaic, Point estimation, Probabilistic optimal power flow
Persistent URL dx.doi.org/10.1109/SMC.2017.8122705
Conference 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Citation
Liu, S. (Shichao), Shen, H. (Haikuo), Wang, H. (Huanqing), & Liu, P. (2017). Investigations of distribution system scheduling with photovoltaic power and load variations. In 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 (pp. 793–797). doi:10.1109/SMC.2017.8122705