Joined Probabilistic Load Flow and Sensitivity Analysis of Distribution Networks Based on Polynomial Chaos Method
Due to the statistical uncertainty of loads and power sources found in smart grids, effective computational tools for probabilistic load flow analysis and planning are now becoming indispensable. In this paper, we describe a unified simulation framework that allows quantifying the probability distri...
| Main Authors: | , , , |
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| Format: | Article |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/130949 |
| _version_ | 1811087043267133440 |
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| author | Gruosso, Giambattista Netto, Roberto S. Daniel, Luca Maffezzoni, Paolo |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Gruosso, Giambattista Netto, Roberto S. Daniel, Luca Maffezzoni, Paolo |
| author_sort | Gruosso, Giambattista |
| collection | MIT |
| description | Due to the statistical uncertainty of loads and power sources found in smart grids, effective computational tools for probabilistic load flow analysis and planning are now becoming indispensable. In this paper, we describe a unified simulation framework that allows quantifying the probability distributions of a set of observation variables as well as evaluating their sensitivity to potential variations in the power demands. The proposed probabilistic technique relies on the generalized polynomial Chaos algorithm and on a regionwise aggregation/description of the time-varying load profiles. It is shown how detailed statistical distributions of some important figures of merit, which includes voltage unbalance factor in distribution networks, can be calculated with a two orders of magnitude acceleration compared to standard Monte Carlo analysis. In addition, it is highlighted how the associated sensitivity analysis is of guidance for the optimal allocation and planning of new loads. |
| first_indexed | 2024-09-23T13:38:53Z |
| format | Article |
| id | mit-1721.1/130949 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T13:38:53Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1309492022-10-01T16:17:41Z Joined Probabilistic Load Flow and Sensitivity Analysis of Distribution Networks Based on Polynomial Chaos Method Gruosso, Giambattista Netto, Roberto S. Daniel, Luca Maffezzoni, Paolo Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Due to the statistical uncertainty of loads and power sources found in smart grids, effective computational tools for probabilistic load flow analysis and planning are now becoming indispensable. In this paper, we describe a unified simulation framework that allows quantifying the probability distributions of a set of observation variables as well as evaluating their sensitivity to potential variations in the power demands. The proposed probabilistic technique relies on the generalized polynomial Chaos algorithm and on a regionwise aggregation/description of the time-varying load profiles. It is shown how detailed statistical distributions of some important figures of merit, which includes voltage unbalance factor in distribution networks, can be calculated with a two orders of magnitude acceleration compared to standard Monte Carlo analysis. In addition, it is highlighted how the associated sensitivity analysis is of guidance for the optimal allocation and planning of new loads. 2021-06-15T20:12:30Z 2021-06-15T20:12:30Z 2020-01 Article http://purl.org/eprint/type/JournalArticle 0885-8950 1558-0679 https://hdl.handle.net/1721.1/130949 Gruosso, Giambattista et al. "Joined Probabilistic Load Flow and Sensitivity Analysis of Distribution Networks Based on Polynomial Chaos Method." IEEE Transactions on Power Systems 35, 1 (January 2020): 618 - 627. © 2020 IEEE http://dx.doi.org/10.1109/tpwrs.2019.2928674 IEEE Transactions on Power Systems Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Luca Daniel |
| spellingShingle | Gruosso, Giambattista Netto, Roberto S. Daniel, Luca Maffezzoni, Paolo Joined Probabilistic Load Flow and Sensitivity Analysis of Distribution Networks Based on Polynomial Chaos Method |
| title | Joined Probabilistic Load Flow and Sensitivity Analysis of Distribution Networks Based on Polynomial Chaos Method |
| title_full | Joined Probabilistic Load Flow and Sensitivity Analysis of Distribution Networks Based on Polynomial Chaos Method |
| title_fullStr | Joined Probabilistic Load Flow and Sensitivity Analysis of Distribution Networks Based on Polynomial Chaos Method |
| title_full_unstemmed | Joined Probabilistic Load Flow and Sensitivity Analysis of Distribution Networks Based on Polynomial Chaos Method |
| title_short | Joined Probabilistic Load Flow and Sensitivity Analysis of Distribution Networks Based on Polynomial Chaos Method |
| title_sort | joined probabilistic load flow and sensitivity analysis of distribution networks based on polynomial chaos method |
| url | https://hdl.handle.net/1721.1/130949 |
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