Computing the feasible operating region of active distribution networks: Comparison and validation of random sampling and optimal power flow based methods
Abstract The feasible operating region (FOR) indicates the operation points that an active distribution network can achieve at the interconnection point with the transmission grid when operating flexible assets within it; without disturbing the stability of the grid itself. Even though the concept i...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-05-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.12120 |
| _version_ | 1811261112176345088 |
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| author | Daniel A. Contreras Krzysztof Rudion |
| author_facet | Daniel A. Contreras Krzysztof Rudion |
| author_sort | Daniel A. Contreras |
| collection | DOAJ |
| description | Abstract The feasible operating region (FOR) indicates the operation points that an active distribution network can achieve at the interconnection point with the transmission grid when operating flexible assets within it; without disturbing the stability of the grid itself. Even though the concept is not new, many novel methods to compute the FOR efficiently have been proposed in recent years, resulting in two main schools of thought: random sampling (RS) and optimal power flow (OPF) methods. Both approaches have their merits, yet no wide‐ranging analysis regarding scenarios in which each method could be best applied has been done so far. This paper focuses on performing such a comparison; however, capability charts of flexibility providing units and grids are usually modelled as irregular convex polygons, requiring some adaptation of the RS‐methods to allow for a proper comparison of the resulting feasible operating region. Correspondingly, new methods to adapt the extraction of random samples from generic capability charts are proposed in the paper. Using models of two radial distribution grids, both OPF‐ and RS‐based methods are compared and validated. Results show that RS methods are adequate for assessing small grids, especially with the proposed improvements, while OPF‐based methods excel in larger grids. |
| first_indexed | 2024-04-12T18:58:44Z |
| format | Article |
| id | doaj.art-5e42ba0dc3b045d1ac6d3b602947a2f0 |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-04-12T18:58:44Z |
| publishDate | 2021-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-5e42ba0dc3b045d1ac6d3b602947a2f02022-12-22T03:20:15ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952021-05-0115101600161210.1049/gtd2.12120Computing the feasible operating region of active distribution networks: Comparison and validation of random sampling and optimal power flow based methodsDaniel A. Contreras0Krzysztof Rudion1Institute for Power Transmission and High Voltage Technology University of Stuttgart Pfaffenwaldring 47 Stuttgart 70569 GermanyInstitute for Power Transmission and High Voltage Technology University of Stuttgart Pfaffenwaldring 47 Stuttgart 70569 GermanyAbstract The feasible operating region (FOR) indicates the operation points that an active distribution network can achieve at the interconnection point with the transmission grid when operating flexible assets within it; without disturbing the stability of the grid itself. Even though the concept is not new, many novel methods to compute the FOR efficiently have been proposed in recent years, resulting in two main schools of thought: random sampling (RS) and optimal power flow (OPF) methods. Both approaches have their merits, yet no wide‐ranging analysis regarding scenarios in which each method could be best applied has been done so far. This paper focuses on performing such a comparison; however, capability charts of flexibility providing units and grids are usually modelled as irregular convex polygons, requiring some adaptation of the RS‐methods to allow for a proper comparison of the resulting feasible operating region. Correspondingly, new methods to adapt the extraction of random samples from generic capability charts are proposed in the paper. Using models of two radial distribution grids, both OPF‐ and RS‐based methods are compared and validated. Results show that RS methods are adequate for assessing small grids, especially with the proposed improvements, while OPF‐based methods excel in larger grids.https://doi.org/10.1049/gtd2.12120Optimisation techniquesPower system management, operation and economicsDistribution networks |
| spellingShingle | Daniel A. Contreras Krzysztof Rudion Computing the feasible operating region of active distribution networks: Comparison and validation of random sampling and optimal power flow based methods IET Generation, Transmission & Distribution Optimisation techniques Power system management, operation and economics Distribution networks |
| title | Computing the feasible operating region of active distribution networks: Comparison and validation of random sampling and optimal power flow based methods |
| title_full | Computing the feasible operating region of active distribution networks: Comparison and validation of random sampling and optimal power flow based methods |
| title_fullStr | Computing the feasible operating region of active distribution networks: Comparison and validation of random sampling and optimal power flow based methods |
| title_full_unstemmed | Computing the feasible operating region of active distribution networks: Comparison and validation of random sampling and optimal power flow based methods |
| title_short | Computing the feasible operating region of active distribution networks: Comparison and validation of random sampling and optimal power flow based methods |
| title_sort | computing the feasible operating region of active distribution networks comparison and validation of random sampling and optimal power flow based methods |
| topic | Optimisation techniques Power system management, operation and economics Distribution networks |
| url | https://doi.org/10.1049/gtd2.12120 |
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