A digital twin of a local energy system based on real smart meter data
Abstract The steadily increasing usage of smart meters generates a valuable amount of high-resolution data about the individual energy consumption and production of local energy systems. Private households install more and more photovoltaic systems, battery storage and big consumers like heat pumps....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-03-01
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| Series: | Energy Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42162-023-00263-6 |
| _version_ | 1797853558668787712 |
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| author | Daniel Bayer Marco Pruckner |
| author_facet | Daniel Bayer Marco Pruckner |
| author_sort | Daniel Bayer |
| collection | DOAJ |
| description | Abstract The steadily increasing usage of smart meters generates a valuable amount of high-resolution data about the individual energy consumption and production of local energy systems. Private households install more and more photovoltaic systems, battery storage and big consumers like heat pumps. Thus, our vision is to augment these collected smart meter time series of a complete system (e.g., a city, town or complex institutions like airports) with simulatively added previously named components. We, therefore, propose a novel digital twin of such an energy system based solely on a complete set of smart meter data including additional building data. Based on the additional geospatial data, the twin is intended to represent the addition of the abovementioned components as realistically as possible. Outputs of the twin can be used as a decision support for either system operators where to strengthen the system or for individual households where and how to install photovoltaic systems and batteries. Meanwhile, the first local energy system operators had such smart meter data of almost all residential consumers for several years. We acquire those of an exemplary operator and discuss a case study presenting some features of our digital twin and highlighting the value of the combination of smart meter and geospatial data. |
| first_indexed | 2024-04-09T19:52:34Z |
| format | Article |
| id | doaj.art-fa8411084eb348348625d072b22ebee6 |
| institution | Directory Open Access Journal |
| issn | 2520-8942 |
| language | English |
| last_indexed | 2024-04-09T19:52:34Z |
| publishDate | 2023-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj.art-fa8411084eb348348625d072b22ebee62023-04-03T05:42:20ZengSpringerOpenEnergy Informatics2520-89422023-03-016112610.1186/s42162-023-00263-6A digital twin of a local energy system based on real smart meter dataDaniel Bayer0Marco Pruckner1Modeling and Simulation, University of WürzburgModeling and Simulation, University of WürzburgAbstract The steadily increasing usage of smart meters generates a valuable amount of high-resolution data about the individual energy consumption and production of local energy systems. Private households install more and more photovoltaic systems, battery storage and big consumers like heat pumps. Thus, our vision is to augment these collected smart meter time series of a complete system (e.g., a city, town or complex institutions like airports) with simulatively added previously named components. We, therefore, propose a novel digital twin of such an energy system based solely on a complete set of smart meter data including additional building data. Based on the additional geospatial data, the twin is intended to represent the addition of the abovementioned components as realistically as possible. Outputs of the twin can be used as a decision support for either system operators where to strengthen the system or for individual households where and how to install photovoltaic systems and batteries. Meanwhile, the first local energy system operators had such smart meter data of almost all residential consumers for several years. We acquire those of an exemplary operator and discuss a case study presenting some features of our digital twin and highlighting the value of the combination of smart meter and geospatial data.https://doi.org/10.1186/s42162-023-00263-6Digital twinSimulationLocal energy systemDecision support systemSmart meter data utilizationFuture energy grid exploration |
| spellingShingle | Daniel Bayer Marco Pruckner A digital twin of a local energy system based on real smart meter data Energy Informatics Digital twin Simulation Local energy system Decision support system Smart meter data utilization Future energy grid exploration |
| title | A digital twin of a local energy system based on real smart meter data |
| title_full | A digital twin of a local energy system based on real smart meter data |
| title_fullStr | A digital twin of a local energy system based on real smart meter data |
| title_full_unstemmed | A digital twin of a local energy system based on real smart meter data |
| title_short | A digital twin of a local energy system based on real smart meter data |
| title_sort | digital twin of a local energy system based on real smart meter data |
| topic | Digital twin Simulation Local energy system Decision support system Smart meter data utilization Future energy grid exploration |
| url | https://doi.org/10.1186/s42162-023-00263-6 |
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