Validation of a Method to Select a Priori the Number of Typical Days for Energy System Optimisation Models
Studying a large number of scenarios is necessary to consider the uncertainty inherent to the energy transition. In addition, the integration of intermittent renewable energy sources requires complex energy system models. Typical days clustering is a commonly used technique to ensure the computation...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/6/2772 |
| _version_ | 1797612046377811968 |
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| author | Paolo Thiran Hervé Jeanmart Francesco Contino |
| author_facet | Paolo Thiran Hervé Jeanmart Francesco Contino |
| author_sort | Paolo Thiran |
| collection | DOAJ |
| description | Studying a large number of scenarios is necessary to consider the uncertainty inherent to the energy transition. In addition, the integration of intermittent renewable energy sources requires complex energy system models. Typical days clustering is a commonly used technique to ensure the computational tractability of energy system optimisation models, while keeping an hourly time step. Its capability to accurately approximate the full-year time series with a reduced number of days has been demonstrated (i.e., <i>a priori</i> evaluation). However, its impact on the results of the energy system model (i.e., <i>a posteriori</i> evaluation) is rarely studied and was never studied on a multi-regional whole-energy system. To address this issue, the multi-regional whole-energy system optimisation model, EnergyScope Multi-Cells, is used to optimise the design and operation of multiple interconnected regions. It is applied to nine diverse cases with different numbers of typical days. A bottom-up <i>a posteriori</i> metric, the design error, is developed and analysed in these cases to find trade-offs between the accuracy and the computational cost of the model. Using 10 typical days divides the computational time by 8.6 to 23.8, according to the case, and ensures a design error below 17%. In all cases studied, the time series error is a good prediction of the design error. Hence, this <i>a priori</i> metric can be used to select the number of typical days for a new case study without running the energy system optimisation model. |
| first_indexed | 2024-03-11T06:36:31Z |
| format | Article |
| id | doaj.art-154a406d0eda489cbe4c9ee09967160e |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T06:36:31Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-154a406d0eda489cbe4c9ee09967160e2023-11-17T10:50:46ZengMDPI AGEnergies1996-10732023-03-01166277210.3390/en16062772Validation of a Method to Select a Priori the Number of Typical Days for Energy System Optimisation ModelsPaolo Thiran0Hervé Jeanmart1Francesco Contino2Institute of Mechanics, Materials and Civil Engineering, Université Catholique de Louvain, 1348 Louvain-la-Neuve, BelgiumInstitute of Mechanics, Materials and Civil Engineering, Université Catholique de Louvain, 1348 Louvain-la-Neuve, BelgiumInstitute of Mechanics, Materials and Civil Engineering, Université Catholique de Louvain, 1348 Louvain-la-Neuve, BelgiumStudying a large number of scenarios is necessary to consider the uncertainty inherent to the energy transition. In addition, the integration of intermittent renewable energy sources requires complex energy system models. Typical days clustering is a commonly used technique to ensure the computational tractability of energy system optimisation models, while keeping an hourly time step. Its capability to accurately approximate the full-year time series with a reduced number of days has been demonstrated (i.e., <i>a priori</i> evaluation). However, its impact on the results of the energy system model (i.e., <i>a posteriori</i> evaluation) is rarely studied and was never studied on a multi-regional whole-energy system. To address this issue, the multi-regional whole-energy system optimisation model, EnergyScope Multi-Cells, is used to optimise the design and operation of multiple interconnected regions. It is applied to nine diverse cases with different numbers of typical days. A bottom-up <i>a posteriori</i> metric, the design error, is developed and analysed in these cases to find trade-offs between the accuracy and the computational cost of the model. Using 10 typical days divides the computational time by 8.6 to 23.8, according to the case, and ensures a design error below 17%. In all cases studied, the time series error is a good prediction of the design error. Hence, this <i>a priori</i> metric can be used to select the number of typical days for a new case study without running the energy system optimisation model.https://www.mdpi.com/1996-1073/16/6/2772energy system modellingtemporal aggregationtypical daysclusteringwhole-energy systemsector-coupling |
| spellingShingle | Paolo Thiran Hervé Jeanmart Francesco Contino Validation of a Method to Select a Priori the Number of Typical Days for Energy System Optimisation Models Energies energy system modelling temporal aggregation typical days clustering whole-energy system sector-coupling |
| title | Validation of a Method to Select a Priori the Number of Typical Days for Energy System Optimisation Models |
| title_full | Validation of a Method to Select a Priori the Number of Typical Days for Energy System Optimisation Models |
| title_fullStr | Validation of a Method to Select a Priori the Number of Typical Days for Energy System Optimisation Models |
| title_full_unstemmed | Validation of a Method to Select a Priori the Number of Typical Days for Energy System Optimisation Models |
| title_short | Validation of a Method to Select a Priori the Number of Typical Days for Energy System Optimisation Models |
| title_sort | validation of a method to select a priori the number of typical days for energy system optimisation models |
| topic | energy system modelling temporal aggregation typical days clustering whole-energy system sector-coupling |
| url | https://www.mdpi.com/1996-1073/16/6/2772 |
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