- Title
- A nonparametric bayesian approach for probabilistic representation of power uncertainties
- Creator
- Sun, Weigao; Zamani, Mohsen
- Relation
- 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). Proceedings of 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2019 (Beijing, China 21-23 October, 2019) p. 1-6
- Publisher Link
- http://dx.doi.org/10.1109/SmartGridComm.2019.8909785
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2019
- Description
- This paper develops a nonparameteric Bayesian approach for the probabilistic representation of power system uncertainties involved with wind, solar and load power. The developed approach based on Dirichlet process mixture model (DPMM) analytically formulates the probability distributions of power uncertainties without prior knowledge of the number of mixture components. This provides a great improvement in probabilistic representation of power uncertainties as the proposed model can accommodate the ever growing power data. A computationally efficient VBI method is exploited to estimate the parameters involved with DPMM. Moreover, a novel truncated DPMM is designed to fit the special truncation feature of wind power distributions. The performance of proposed probabilistic representation approach for power uncertainties on real datasets of wind, solar and load power are validated and illustrated in the numerical simulations.
- Subject
- uncertainty modelling; Dirichlet process mixture model; probability representation; power uncertainty; SDG 7; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1458517
- Identifier
- uon:45445
- Identifier
- ISBN:9781538680995
- Language
- eng
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Goal 7: Ensure access to affordable, reliable, sustainable and modern energy