The environmental crisis caused by fossil fuel energy and environmental pollution is one of the main challenges facing the world today. To alleviate and overcome this challenge, renewable energy resources are being widely exploited and integrated into power systems worldwide. As three of the most important renewable energy sources, hydropower, wind power, and solar power have been growing rapidly with a substantially increasing rate of generation capacity.

However, the inflow uncertainty, intermittent power, and randomness of new energy sources, as well as the large number of power plants needed, brings substantial operational complexity, and so the difficulty of operating hydro-wind-solar hybrid energy systems (HWSHESs) has a severe impact on the security, stability, and flexibility of power supply and regulation. It is therefore of great importance to solve the operational challenges of such energy systems. In recent decades, the rapid development of information technology has provided great potential to measure, transmit, store, and analyse the massive amounts of operational data generated by large-scale energy systems. These data can be effectively and efficiently used by advanced technologies, such as big data and deep learning, to establish data-driven theoretical methods and technical means to serve the generation scheduling, operation, and decision-making analysis for large-scale HWSHESs.

This Special Issue aims to gather research from both academia and industry on data-driven methods for the operation of HWSHESs. Submissions on data-based power prediction, operating rules, generation scheduling, decision-making analysis modes, decision support systems, and other relevant researches are welcome. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Uncertainty analysis in power generation
• Power-complementary mechanisms for multiple kinds of power sources
• Intelligent forecasting models for new energy systems
• Aggregation of large-scale wind and solar plants based on operation data
• Multi-objective optimisation for the operation of HWSHESs
• Decision-making analysis models for the operation of HWSHESs
• Operating rules of hydropower reservoirs in HWSHESs
• Deep learning methods for the operation of HWSHESs
• Cluster analysis for power generation of HWSHESs
• Decision support systems and applications for dispatching HWSHESs