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 modern power systems and introduction stochastic optimization for handling decision-making problems. These include unit commitment, optimal bidding, online supply–demand management, long-term planning integrated renewable energy systems. Simultaneously, installed capacity solar is increasing due its availability periodical characteristics, as well flexibility cost reduction photovoltaic (PV) technologies. This paper evaluates scenario methods context highlights their advantages limitations. Furthermore, it introduces taxonomies based on weather classification techniques temporal horizons. Fine-grained classifications can significantly improve overall quality generated sets. The performance different strongly related horizon target domain. also conducts a systematic review currently trending deep generative models assess introduced improvements, identify Finally, several research directions are proposed findings drawn conclusions address current challenges adapt future advancements