A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models

Markos A. Kousounadis-Knousen, Ioannis K. Bazionis, Athina P. Georgilaki, Francky Catthoor and Pavlos S. Georgilakis ()
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Markos A. Kousounadis-Knousen: School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Ioannis K. Bazionis: School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Athina P. Georgilaki: School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Francky Catthoor: Interuniversity Microelectronics Centre (IMEC), 3001 Leuven, Belgium
Pavlos S. Georgilakis: School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece

Energies, 2023, vol. 16, issue 15, 1-29

Abstract: 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.

Keywords: scenario generation; solar power generation; uncertainty; weather classification; stochastic optimization; deep generative models; photovoltaic forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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