A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models
Scenario generation has attracted wide attention in recent years owing to the high penetration of uncertainty sources in modern power systems and the introduction of stochastic optimization for handling decision-making problems. These include unit commitment, optimal bidding, online supply–demand ma...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/15/5600 |
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| author | Markos A. Kousounadis-Knousen Ioannis K. Bazionis Athina P. Georgilaki Francky Catthoor Pavlos S. Georgilakis |
| author_facet | Markos A. Kousounadis-Knousen Ioannis K. Bazionis Athina P. Georgilaki Francky Catthoor Pavlos S. Georgilakis |
| author_sort | Markos A. Kousounadis-Knousen |
| collection | DOAJ |
| description | Scenario generation has attracted wide attention in recent years owing to the high penetration of uncertainty sources in modern power systems and the introduction of stochastic optimization for handling decision-making problems. These include unit commitment, optimal bidding, online supply–demand management, and long-term planning of integrated renewable energy systems. Simultaneously, the installed capacity of solar power is increasing due to its availability and periodical characteristics, as well as the flexibility and cost reduction of photovoltaic (PV) technologies. This paper evaluates scenario generation methods in the context of solar power and highlights their advantages and limitations. Furthermore, it introduces taxonomies based on weather classification techniques and temporal horizons. Fine-grained weather classifications can significantly improve the overall quality of the generated scenario sets. The performance of different scenario generation methods is strongly related to the temporal horizon of the target domain. This paper also conducts a systematic review of the currently trending deep generative models to assess introduced improvements, as well as to identify their limitations. Finally, several research directions are proposed based on the findings and drawn conclusions to address current challenges and adapt to future advancements in modern power systems. |
| first_indexed | 2024-03-11T00:28:06Z |
| format | Article |
| id | doaj.art-32b4999f8fb641ff97e416ae0123ad30 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T00:28:06Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-32b4999f8fb641ff97e416ae0123ad302023-11-18T22:50:20ZengMDPI AGEnergies1996-10732023-07-011615560010.3390/en16155600A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative ModelsMarkos A. Kousounadis-Knousen0Ioannis K. Bazionis1Athina P. Georgilaki2Francky Catthoor3Pavlos S. Georgilakis4School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, GreeceSchool of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, GreeceSchool of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, GreeceInteruniversity Microelectronics Centre (IMEC), 3001 Leuven, BelgiumSchool of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, GreeceScenario generation has attracted wide attention in recent years owing to the high penetration of uncertainty sources in modern power systems and the introduction of stochastic optimization for handling decision-making problems. These include unit commitment, optimal bidding, online supply–demand management, and long-term planning of integrated renewable energy systems. Simultaneously, the installed capacity of solar power is increasing due to its availability and periodical characteristics, as well as the flexibility and cost reduction of photovoltaic (PV) technologies. This paper evaluates scenario generation methods in the context of solar power and highlights their advantages and limitations. Furthermore, it introduces taxonomies based on weather classification techniques and temporal horizons. Fine-grained weather classifications can significantly improve the overall quality of the generated scenario sets. The performance of different scenario generation methods is strongly related to the temporal horizon of the target domain. This paper also conducts a systematic review of the currently trending deep generative models to assess introduced improvements, as well as to identify their limitations. Finally, several research directions are proposed based on the findings and drawn conclusions to address current challenges and adapt to future advancements in modern power systems.https://www.mdpi.com/1996-1073/16/15/5600scenario generationsolar power generationuncertaintyweather classificationstochastic optimizationdeep generative models |
| spellingShingle | Markos A. Kousounadis-Knousen Ioannis K. Bazionis Athina P. Georgilaki Francky Catthoor Pavlos S. Georgilakis A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models Energies scenario generation solar power generation uncertainty weather classification stochastic optimization deep generative models |
| title | A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models |
| title_full | A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models |
| title_fullStr | A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models |
| title_full_unstemmed | A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models |
| title_short | A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models |
| title_sort | review of solar power scenario generation methods with focus on weather classifications temporal horizons and deep generative models |
| topic | scenario generation solar power generation uncertainty weather classification stochastic optimization deep generative models |
| url | https://www.mdpi.com/1996-1073/16/15/5600 |
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