Data-Driven Probabilistic Optimal Power Flow With Nonparametric Bayesian Modeling and Inference

In this paper, we propose a data-driven algorithm for probabilistic optimal power flow (POPF). In particular, we develop a nonparametric Bayesian framework based on the Dirichlet process mixture model (DPMM) and variational Bayesian inference (VBI) to establish a probabilistic model for capturing th...

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Bibliographic Details
Published in:IEEE transactions on smart grid 2020-03, Vol.11 (2), p.1077-1090
Main Authors: Sun, Weigao, Zamani, Mohsen, Hesamzadeh, Mohammad Reza, Zhang, Hai-Tao
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Computer simulation
Correlation
Decision making
Dirichlet problem
Dirichlet process mixture model
Estimation
Gaussian mixture model
Input variables
Monte Carlo simulation
Nonparametric statistics
Power flow
Power systems
Probabilistic inference
Probabilistic logic
Probabilistic models
Probabilistic optimal power flow
Probability theory
Sampling methods
Statistical inference
Uncertainty
Wind power generation
wind uncertainty
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Summary:In this paper, we propose a data-driven algorithm for probabilistic optimal power flow (POPF). In particular, we develop a nonparametric Bayesian framework based on the Dirichlet process mixture model (DPMM) and variational Bayesian inference (VBI) to establish a probabilistic model for capturing the uncertainties involved with wind generation and load power in power systems. In the proposed setup, the number of components in the mixture model can be automatically and analytically obtained from the consistently updated data. Moreover, we develop an efficient quasi-Monte Carlo sampling method to draw samples from the obtained DPMM, then propose the dynamic data-driven POPF algorithm. Performance of uncertainty modeling framework on publicly available datasets is examined by extensive numerical simulations. Furthermore, the proposed POPF algorithm is verified on multiple IEEE benchmark power systems. Numerical results show the feasibility and superiority of the proposed DPMM-based POPF algorithm for better informed decision-making in power systems with high level of uncertainties.
ISSN:1949-3053
1949-3061
1949-3061
DOI:10.1109/TSG.2019.2931160