A generic multi‐period optimal power flow framework for combating operational constraints via residential flexibility resources
Abstract In low voltage networks, the majority of distributed energy resources are customer owned. As such, it is harder for the distribution system operator to control its system and maintain acceptable operating conditions. Even if residential flexibility is available, it should be employed withou...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.12022 |
| _version_ | 1811273397620965376 |
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| author | Iason I. Avramidis Florin Capitanescu Geert Deconinck |
| author_facet | Iason I. Avramidis Florin Capitanescu Geert Deconinck |
| author_sort | Iason I. Avramidis |
| collection | DOAJ |
| description | Abstract In low voltage networks, the majority of distributed energy resources are customer owned. As such, it is harder for the distribution system operator to control its system and maintain acceptable operating conditions. Even if residential flexibility is available, it should be employed without significant disturbances to customer‐driven device profile patterns. Here a generic tool is developed to assist the distribution system operator in making informed decisions regarding its best course of action to combat operational issues with a limited amount of customer‐driven flexibility. The proposed tool relies on a novel and versatile multi‐period optimal power flow model for centralised control of low voltage distribution systems. Various scenarios of flexibility resources controllability are examined, coupled with a number of novel modelling approaches and customer‐driven restrictions for the distribution system operator. For most scenarios, the multi‐period optimal power flow model is amenable to nonlinear programming (NLP) problems, though there are cases that end up as mixed‐integer nonlinear programming (MINLP) problems. For the latter, a heuristic approach is employed to approximate the MINLP into a sequence of size‐decreasing mixed‐integer linear programming (MILP) and a final NLP problem. The proposed formulation and approximation are applied to two low voltage networks of 18 nodes and 308 nodes, respectively. |
| first_indexed | 2024-04-12T22:58:24Z |
| format | Article |
| id | doaj.art-e608bef44b934f1aa1eb41a71d553c1f |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-04-12T22:58:24Z |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-e608bef44b934f1aa1eb41a71d553c1f2022-12-22T03:13:08ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952021-01-0115230632010.1049/gtd2.12022A generic multi‐period optimal power flow framework for combating operational constraints via residential flexibility resourcesIason I. Avramidis0Florin Capitanescu1Geert Deconinck2Luxembourg Institute of Research and Technology (LIST) Belvaux LuxembourgLuxembourg Institute of Research and Technology (LIST) Belvaux LuxembourgESAT‐Electa KU Leuven Leuven BelgiumAbstract In low voltage networks, the majority of distributed energy resources are customer owned. As such, it is harder for the distribution system operator to control its system and maintain acceptable operating conditions. Even if residential flexibility is available, it should be employed without significant disturbances to customer‐driven device profile patterns. Here a generic tool is developed to assist the distribution system operator in making informed decisions regarding its best course of action to combat operational issues with a limited amount of customer‐driven flexibility. The proposed tool relies on a novel and versatile multi‐period optimal power flow model for centralised control of low voltage distribution systems. Various scenarios of flexibility resources controllability are examined, coupled with a number of novel modelling approaches and customer‐driven restrictions for the distribution system operator. For most scenarios, the multi‐period optimal power flow model is amenable to nonlinear programming (NLP) problems, though there are cases that end up as mixed‐integer nonlinear programming (MINLP) problems. For the latter, a heuristic approach is employed to approximate the MINLP into a sequence of size‐decreasing mixed‐integer linear programming (MILP) and a final NLP problem. The proposed formulation and approximation are applied to two low voltage networks of 18 nodes and 308 nodes, respectively.https://doi.org/10.1049/gtd2.12022Optimisation techniquesOptimisation techniquesPower system management, operation and economicsDistribution networks |
| spellingShingle | Iason I. Avramidis Florin Capitanescu Geert Deconinck A generic multi‐period optimal power flow framework for combating operational constraints via residential flexibility resources IET Generation, Transmission & Distribution Optimisation techniques Optimisation techniques Power system management, operation and economics Distribution networks |
| title | A generic multi‐period optimal power flow framework for combating operational constraints via residential flexibility resources |
| title_full | A generic multi‐period optimal power flow framework for combating operational constraints via residential flexibility resources |
| title_fullStr | A generic multi‐period optimal power flow framework for combating operational constraints via residential flexibility resources |
| title_full_unstemmed | A generic multi‐period optimal power flow framework for combating operational constraints via residential flexibility resources |
| title_short | A generic multi‐period optimal power flow framework for combating operational constraints via residential flexibility resources |
| title_sort | generic multi period optimal power flow framework for combating operational constraints via residential flexibility resources |
| topic | Optimisation techniques Optimisation techniques Power system management, operation and economics Distribution networks |
| url | https://doi.org/10.1049/gtd2.12022 |
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