4.7 Article

A Versatile Probability Model of Photovoltaic Generation Using Pair Copula Construction

Journal

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
Volume 6, Issue 4, Pages 1337-1345

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2015.2434934

Keywords

Multiple dependence; pair copula; photovoltaic (PV) generation; probabilistic load flow (PLF); simplification

Funding

  1. National Natural Science Foundation of China [51307107, 51477098]
  2. National High-Tech R&D Program of China 863 program [2014AA052003]
  3. SRFDP [20130073120034]
  4. Key Laboratory of Control of Power Transmission and Transformation, Ministry of Education
  5. State Energy Smart Grid R&D Center, Shanghai, China
  6. Directorate For Engineering
  7. Div Of Electrical, Commun & Cyber Sys [1068996] Funding Source: National Science Foundation

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Photovoltaic (PV) generation is increasingly popular in power systems. The nonlinear dependence associated with a large number of distributed PV sources adds the complexity to construct an accurate probability model and negatively affects confidence levels and reliability, thereby resulting in a more challenging operation of the systems. Most probability models have many restrictions when constructing multiple PV sources with complex dependence. This paper proposes a versatile probability model of PV generation on the basis of pair copula construction. In order to tackle the computational burden required to construct pair copula in high-dimensional cases, a systematic simplification technique is utilized that can significantly reduce the computational effort while preserving satisfactory precision. The proposed method can simplify the modeling procedure and provide a flexible and optimal probability model for the PV generation with complex dependence. The proposed model is tested using a set of historical data from colocated PV sites. It is then applied to the probabilistic load flow (PLF) study of the IEEE 118-bus system. The results demonstrate the effectiveness and accuracy of the proposed model.

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