Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources
Abstract Uncertainties of renewable energy sources (RESs) such as wind turbine (WT) and photovoltaic (PV) units are one of the considerable challenges of prosumer microgrids (PMGs) for the optimal day‐ahead operation. In this study, a new probabilistic scenario‐based method of optimal scheduling and...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-11-01
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| Series: | Energy Science & Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ese3.788 |
| _version_ | 1828794555837710336 |
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| author | Jamal Faraji Hamed Hashemi‐Dezaki Abbas Ketabi |
| author_facet | Jamal Faraji Hamed Hashemi‐Dezaki Abbas Ketabi |
| author_sort | Jamal Faraji |
| collection | DOAJ |
| description | Abstract Uncertainties of renewable energy sources (RESs) such as wind turbine (WT) and photovoltaic (PV) units are one of the considerable challenges of prosumer microgrids (PMGs) for the optimal day‐ahead operation. In this study, a new probabilistic scenario‐based method of optimal scheduling and operation of PMGs is developed. In this regard, different scenarios are generated using Monte Carlo Simulations (MCS). Furthermore, k‐means, k‐medoids, and differential evolution algorithms (DEA) are deployed to cluster the scenarios in the proposed method. A realistic commercial PMG in Iran is selected to apply the introduced method. The validity of the developed probabilistic optimization method for PMG operation is examined by comparing the results under various scenario reduction algorithms and MCS ones. The comparison of the obtained results and those of other existing deterministic methods highlights the advantages of the presented method. Furthermore, the sensitivity analyses are carried out to investigate the robustness of the developed method against the increase in the system uncertainty level. According to the test results, it is concluded that the k‐medoids algorithm has the best performance in comparison with the k‐means and the DEA‐based clustering under various conditions. |
| first_indexed | 2024-12-12T03:47:31Z |
| format | Article |
| id | doaj.art-0cd7dd80c2a848fc889b5c0e4fdab906 |
| institution | Directory Open Access Journal |
| issn | 2050-0505 |
| language | English |
| last_indexed | 2024-12-12T03:47:31Z |
| publishDate | 2020-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Energy Science & Engineering |
| spelling | doaj.art-0cd7dd80c2a848fc889b5c0e4fdab9062022-12-22T00:39:28ZengWileyEnergy Science & Engineering2050-05052020-11-018113942396010.1002/ese3.788Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sourcesJamal Faraji0Hamed Hashemi‐Dezaki1Abbas Ketabi2Energy Research Institute University of Kashan Kashan IranDepartment of Electrical and Computer Engineering University of Kashan Kashan IranEnergy Research Institute University of Kashan Kashan IranAbstract Uncertainties of renewable energy sources (RESs) such as wind turbine (WT) and photovoltaic (PV) units are one of the considerable challenges of prosumer microgrids (PMGs) for the optimal day‐ahead operation. In this study, a new probabilistic scenario‐based method of optimal scheduling and operation of PMGs is developed. In this regard, different scenarios are generated using Monte Carlo Simulations (MCS). Furthermore, k‐means, k‐medoids, and differential evolution algorithms (DEA) are deployed to cluster the scenarios in the proposed method. A realistic commercial PMG in Iran is selected to apply the introduced method. The validity of the developed probabilistic optimization method for PMG operation is examined by comparing the results under various scenario reduction algorithms and MCS ones. The comparison of the obtained results and those of other existing deterministic methods highlights the advantages of the presented method. Furthermore, the sensitivity analyses are carried out to investigate the robustness of the developed method against the increase in the system uncertainty level. According to the test results, it is concluded that the k‐medoids algorithm has the best performance in comparison with the k‐means and the DEA‐based clustering under various conditions.https://doi.org/10.1002/ese3.788differential evolution algorithm (DEA)k‐means algorithmk‐medoids algorithmMonte Carlo simulation (MCS)optimal scenario‐based operation and schedulingprosumer microgrids (PMGs) |
| spellingShingle | Jamal Faraji Hamed Hashemi‐Dezaki Abbas Ketabi Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources Energy Science & Engineering differential evolution algorithm (DEA) k‐means algorithm k‐medoids algorithm Monte Carlo simulation (MCS) optimal scenario‐based operation and scheduling prosumer microgrids (PMGs) |
| title | Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources |
| title_full | Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources |
| title_fullStr | Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources |
| title_full_unstemmed | Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources |
| title_short | Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources |
| title_sort | optimal probabilistic scenario based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources |
| topic | differential evolution algorithm (DEA) k‐means algorithm k‐medoids algorithm Monte Carlo simulation (MCS) optimal scenario‐based operation and scheduling prosumer microgrids (PMGs) |
| url | https://doi.org/10.1002/ese3.788 |
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