Privacy-preserving distributed parameter estimation for probability distribution of wind power forecast error

Building the conditional probability distribution of wind power forecast errors benefits both wind farms (WFs) and independent system operators (ISOs). Establishing the joint probability distribution of wind power and the corresponding forecast data of spatially correlated WFs is the foundation for...

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Veröffentlicht in:Renewable energy Jg. 163; S. 1318 - 1332
Hauptverfasser: Jia, Mengshuo, Huang, Shaowei, Wang, Zhiwen, Shen, Chen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.01.2021
Schlagworte:
algorithms
data collection
Data privacy
Distributed parameter estimation
Expectation-maximization algorithm
Gaussian mixture model
Probability distribution
wind
wind farms
wind power
Wind power forecast error
Distributed parameter estimation
Data privacy
Probability distribution
Expectation-maximization algorithm
Gaussian mixture model
Wind power forecast error
ISSN:0960-1481, 1879-0682
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Zusammenfassung:Building the conditional probability distribution of wind power forecast errors benefits both wind farms (WFs) and independent system operators (ISOs). Establishing the joint probability distribution of wind power and the corresponding forecast data of spatially correlated WFs is the foundation for deriving the conditional probability distribution. Traditional parameter estimation methods for probability distributions require the collection of historical data of all WFs. However, in the context of multi-regional interconnected grids, neither regional ISOs nor WFs can collect the raw data of WFs in other regions due to privacy or competition considerations. Therefore, based on the Gaussian mixture model, this paper first proposes a privacy-preserving distributed expectation-maximization algorithm to estimate the parameters of the joint probability distribution. This algorithm consists of two original methods: (1) a privacy-preserving distributed summation algorithm and (2) a privacy-preserving distributed inner product algorithm. Then, we derive each WF’s conditional probability distribution of forecast error from the joint one. By the proposed algorithms, WFs only need local calculations and privacy-preserving neighboring communications to achieve the whole parameter estimation. These algorithms are verified using the wind integration data set published by the NREL. •A novel privacy-preserving distributed expectation-maximization algorithm.•Building joint probability distributions of vertically partitioned wind powers.•Only neighboring communication among correlated wind farms is required.•Data of wind farms belonging to different stakeholders is strictly protected.•Basis for conditional probability distributions of wind power forecast errors.
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ISSN:0960-1481
1879-0682
DOI:10.1016/j.renene.2020.06.102