A data‐driven network optimisation approach to coordinated control of distributed photovoltaic systems and smart buildings in distribution systems
Abstract The increasing integration of distributed energy resources, including demand‐side resources and distributed photovoltaics (PVs), into distribution systems has resulted in more complicated power system operation. A data‐driven network optimisation approach is proposed to coordinate the contr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-09-01
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| Series: | IET Energy Systems Integration |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/esi2.12025 |
| _version_ | 1811180253641441280 |
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| author | Linquan Bai Yaosuo Xue Guanglin Xu Jin Dong Mohammed M. Olama Teja Kuruganti |
| author_facet | Linquan Bai Yaosuo Xue Guanglin Xu Jin Dong Mohammed M. Olama Teja Kuruganti |
| author_sort | Linquan Bai |
| collection | DOAJ |
| description | Abstract The increasing integration of distributed energy resources, including demand‐side resources and distributed photovoltaics (PVs), into distribution systems has resulted in more complicated power system operation. A data‐driven network optimisation approach is proposed to coordinate the control of distributed PVs and smart buildings in distribution networks considering the uncertainties of solar power, outdoor temperature and heat gain associated with building thermal dynamics. These uncertain parameters have a significant impact on the operation and control of distributed PVs and smart buildings, bringing challenges to the distribution system operation. In the proposed data‐driven distributionally robust optimisation (DRO) approach, the Wasserstein ball is used to construct an ambiguity set for the uncertain parameters, which does not require the probability distributions to be known. Furthermore, a conditional value‐at‐risk is incorporated into the Wasserstein‐based DRO model and converted into a computationally tractable mixed‐integer convex optimisation problem. Benchmarked with robust optimisation and chance‐constrained programming, the proposed data‐driven model can give a less conservative robust solution. |
| first_indexed | 2024-04-11T06:47:21Z |
| format | Article |
| id | doaj.art-aee97958dadf4cbc9ce6ceb888682088 |
| institution | Directory Open Access Journal |
| issn | 2516-8401 |
| language | English |
| last_indexed | 2024-04-11T06:47:21Z |
| publishDate | 2021-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Energy Systems Integration |
| spelling | doaj.art-aee97958dadf4cbc9ce6ceb8886820882022-12-22T04:39:19ZengWileyIET Energy Systems Integration2516-84012021-09-013328529410.1049/esi2.12025A data‐driven network optimisation approach to coordinated control of distributed photovoltaic systems and smart buildings in distribution systemsLinquan Bai0Yaosuo Xue1Guanglin Xu2Jin Dong3Mohammed M. Olama4Teja Kuruganti5Systems Engineering and Engineering Management University of North Carolina at Charlotte Charlotte North Carolina USAElectrification and Energy Infrastructures Division Oak Ridge National Laboratory Oak Ridge Tennessee USASystems Engineering and Engineering Management University of North Carolina at Charlotte Charlotte North Carolina USAElectrification and Energy Infrastructures Division Oak Ridge National Laboratory Oak Ridge Tennessee USAComputational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge Tennessee USAComputational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge Tennessee USAAbstract The increasing integration of distributed energy resources, including demand‐side resources and distributed photovoltaics (PVs), into distribution systems has resulted in more complicated power system operation. A data‐driven network optimisation approach is proposed to coordinate the control of distributed PVs and smart buildings in distribution networks considering the uncertainties of solar power, outdoor temperature and heat gain associated with building thermal dynamics. These uncertain parameters have a significant impact on the operation and control of distributed PVs and smart buildings, bringing challenges to the distribution system operation. In the proposed data‐driven distributionally robust optimisation (DRO) approach, the Wasserstein ball is used to construct an ambiguity set for the uncertain parameters, which does not require the probability distributions to be known. Furthermore, a conditional value‐at‐risk is incorporated into the Wasserstein‐based DRO model and converted into a computationally tractable mixed‐integer convex optimisation problem. Benchmarked with robust optimisation and chance‐constrained programming, the proposed data‐driven model can give a less conservative robust solution.https://doi.org/10.1049/esi2.12025building management systemsconvex programmingdistributed power generationdistribution networksinteger programminglinear programming |
| spellingShingle | Linquan Bai Yaosuo Xue Guanglin Xu Jin Dong Mohammed M. Olama Teja Kuruganti A data‐driven network optimisation approach to coordinated control of distributed photovoltaic systems and smart buildings in distribution systems IET Energy Systems Integration building management systems convex programming distributed power generation distribution networks integer programming linear programming |
| title | A data‐driven network optimisation approach to coordinated control of distributed photovoltaic systems and smart buildings in distribution systems |
| title_full | A data‐driven network optimisation approach to coordinated control of distributed photovoltaic systems and smart buildings in distribution systems |
| title_fullStr | A data‐driven network optimisation approach to coordinated control of distributed photovoltaic systems and smart buildings in distribution systems |
| title_full_unstemmed | A data‐driven network optimisation approach to coordinated control of distributed photovoltaic systems and smart buildings in distribution systems |
| title_short | A data‐driven network optimisation approach to coordinated control of distributed photovoltaic systems and smart buildings in distribution systems |
| title_sort | data driven network optimisation approach to coordinated control of distributed photovoltaic systems and smart buildings in distribution systems |
| topic | building management systems convex programming distributed power generation distribution networks integer programming linear programming |
| url | https://doi.org/10.1049/esi2.12025 |
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