Multi-dimensional scenario forecast for generation of multiple wind farms

doi:10.1007/s40565-015-0110-6

Home > Archive>Volume 3, Issue 3, 2015 >361-370. DOI:10.1007/s40565-015-0110-6

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Multi-dimensional scenario forecast for generation of multiple wind farms

DOI:

10.1007/s40565-015-0110-6

Author:

Ming YANG
Ming YANG
Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Laboratory of Electric Vehicles Engineering of Shandong Province, Shandong University, Jinan, 250061, China
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You LIN
You LIN
Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Laboratory of Electric Vehicles Engineering of Shandong Province, Shandong University, Jinan, 250061, China
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Simeng ZHU
Simeng ZHU
Zaozhuang Power Supply Company, Zaozhuang, 277800, China
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Xueshan HAN
Xueshan HAN
Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Laboratory of Electric Vehicles Engineering of Shandong Province, Shandong University, Jinan, 250061, China
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Hongtao WANG
Hongtao WANG
Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Laboratory of Electric Vehicles Engineering of Shandong Province, Shandong University, Jinan, 250061, China
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Affiliation:

1. Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Laboratory of Electric Vehicles Engineering of Shandong Province, Shandong University, Jinan, 250061, China 2. Zaozhuang Power Supply Company, Zaozhuang, 277800, China

Fund Project:

National Natural Science Foundation of China (No. 51007047, No. 51077087), Shandong Provincial Natural Science Foundation of China (No. 20100131120039), and National High Technology Research and Development Program of China (863 Program) (No. 2011AA05A101)

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Abstract:

A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is proposed. In the proposed approach, support vector machine (SVM) is applied for the spot forecast of wind power generation. The probability density function (PDF) of the SVM forecast error is predicted by sparse Bayesian learning (SBL), and the spot forecast result is corrected according to the error expectation obtained. The copula function is estimated using a Gaussian copula-based dynamic conditional correlation matrix regression (DCCMR) model to describe the correlation among the errors. And the multi-dimensional scenario is generated with respect to the estimated marginal distributions and the copula function. Test results on three adjacent wind farms illustrate the effectiveness of the proposed approach.

Key words:Wind power generation forecast, Multi-dimensional scenario forecast, Support vector machine (SVM), Sparse Bayesian learning (SBL), Gaussian copula, Dynamic conditional correlation matrix

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Online: August 26,2015
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