Advanced Probabilistic Power Flow Method Using Vine Copulas for Wind Power Capacity Expansion
As the use of renewable energy is continuously increasing, power systems are currently exposed to greater uncertainty and variability, which can lead to severe power system stability issues. Therefore, a power system analysis tool should be devised to assess the impact of renewable energy integratio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9933734/ |
| _version_ | 1811231288874500096 |
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| author | Ryungyeong Lee Gyeongmin Kim Jin Hur Hunyoung Shin |
| author_facet | Ryungyeong Lee Gyeongmin Kim Jin Hur Hunyoung Shin |
| author_sort | Ryungyeong Lee |
| collection | DOAJ |
| description | As the use of renewable energy is continuously increasing, power systems are currently exposed to greater uncertainty and variability, which can lead to severe power system stability issues. Therefore, a power system analysis tool should be devised to assess the impact of renewable energy integration along with an accurate modeling of their stochastic characteristics. In this study, an advanced probabilistic power flow (PPF) method is developed using vine copulas that captures the complex dependency of the stochastic wind power generated from multiple wind sites. The proposed method also involves the use of a function for selecting the probability models of wind speeds by regions in a sophisticated manner. The effectiveness of the proposed method is tested on an IEEE bus system as well as, on a South Korean power system with thousands of buses and transmission lines using PSS/E with Python API. The simulations demonstrate that the proposed method can more accurately evaluate the power system risks with the sophisticated modeling of wind power in multiple sites as compared to the deterministic approach or the PPF with independent sampling. |
| first_indexed | 2024-04-12T10:43:08Z |
| format | Article |
| id | doaj.art-fc672f10b9584fd49b4fba0ece515982 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T10:43:08Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-fc672f10b9584fd49b4fba0ece5159822022-12-22T03:36:33ZengIEEEIEEE Access2169-35362022-01-011011492911494110.1109/ACCESS.2022.32186449933734Advanced Probabilistic Power Flow Method Using Vine Copulas for Wind Power Capacity ExpansionRyungyeong Lee0https://orcid.org/0000-0002-2667-7103Gyeongmin Kim1https://orcid.org/0000-0002-7204-1659Jin Hur2https://orcid.org/0000-0003-2239-3602Hunyoung Shin3https://orcid.org/0000-0003-0200-4008Department of Electronic and Electrical Engineering, Hongik University, Seoul, South KoreaDepartment of Climate and Energy Systems Engineering, College of Engineering, Ewha Womans University, Seoul, South KoreaDepartment of Climate and Energy Systems Engineering, College of Engineering, Ewha Womans University, Seoul, South KoreaDepartment of Electronic and Electrical Engineering, Hongik University, Seoul, South KoreaAs the use of renewable energy is continuously increasing, power systems are currently exposed to greater uncertainty and variability, which can lead to severe power system stability issues. Therefore, a power system analysis tool should be devised to assess the impact of renewable energy integration along with an accurate modeling of their stochastic characteristics. In this study, an advanced probabilistic power flow (PPF) method is developed using vine copulas that captures the complex dependency of the stochastic wind power generated from multiple wind sites. The proposed method also involves the use of a function for selecting the probability models of wind speeds by regions in a sophisticated manner. The effectiveness of the proposed method is tested on an IEEE bus system as well as, on a South Korean power system with thousands of buses and transmission lines using PSS/E with Python API. The simulations demonstrate that the proposed method can more accurately evaluate the power system risks with the sophisticated modeling of wind power in multiple sites as compared to the deterministic approach or the PPF with independent sampling.https://ieeexplore.ieee.org/document/9933734/Probabilistic power flowwind powervine copulaWasserstein distancebulk power systems |
| spellingShingle | Ryungyeong Lee Gyeongmin Kim Jin Hur Hunyoung Shin Advanced Probabilistic Power Flow Method Using Vine Copulas for Wind Power Capacity Expansion IEEE Access Probabilistic power flow wind power vine copula Wasserstein distance bulk power systems |
| title | Advanced Probabilistic Power Flow Method Using Vine Copulas for Wind Power Capacity Expansion |
| title_full | Advanced Probabilistic Power Flow Method Using Vine Copulas for Wind Power Capacity Expansion |
| title_fullStr | Advanced Probabilistic Power Flow Method Using Vine Copulas for Wind Power Capacity Expansion |
| title_full_unstemmed | Advanced Probabilistic Power Flow Method Using Vine Copulas for Wind Power Capacity Expansion |
| title_short | Advanced Probabilistic Power Flow Method Using Vine Copulas for Wind Power Capacity Expansion |
| title_sort | advanced probabilistic power flow method using vine copulas for wind power capacity expansion |
| topic | Probabilistic power flow wind power vine copula Wasserstein distance bulk power systems |
| url | https://ieeexplore.ieee.org/document/9933734/ |
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