To achieve sustainable development, renewable energies including solar, wind, nuclear, and fuel cells have become emerging choices in many applications. However, the guarantee of stable energy generation rate and safe system operation is not easy, because of their intermittent characteristics and the spatial complexity of renewable energy generation and transmission plants.

In general, accurate mathematical models for renewable energy systems are difficult to derive due to the existence of unavoidable parameter uncertainties, nonsmooth dynamics, and external disturbances. In this respect, developing efficient yet applicable learning and adaptation methods for modeling, optimization, and control of complex renewable energy systems could provide a new way to improve the system efficacy and efficiency. This has attracted significant attention worldwide.

The aim of this Special Issue is to collect the latest research results on the relevant topics of learning and adaptation for modelling, optimization, and control to promote the awareness of the related research methodologies of complex renewable energy systems. Authors are invited to present new modelling, optimization and control algorithms, hardware configuration, software architectures, experiments, and applications, which can bring new information about relevant theories and techniques of complex energy systems. All papers related to the theoretical methods and their application for optimization and control of complex energy systems are welcome. In particular, we encourage authors to submit their original research and review articles with either theoretical and methodological development or practical focus, such as simulation models, algorithms, experiments, and applications about advanced control and optimization techniques for complex energy systems.

Potential topics include but are not limited to the following:

• Modelling, simulation and validation for complex renewable energy systems
• Design and dynamic analysis for renewable energy systems with multiple energy storage components, generators, and motors
• Modelling and compensation of nonsmooth dynamics in renewable energy generation systems
• Bio-inspired optimization and optimal control for renewable energy systems with generators, storage, and motors
• Artificial intelligence methods for learning, adaptation, and optimization
• Data-driven modeling and control for renewable energy systems
• Deep learning and integrative learning-based optimization and control designs
• Adaptive parameter estimation for modeling of renewable energy systems
• Learning and adaptation approaches for renewable energy generation, storage, and distribution
• Adaptive dynamic programming for renewable energy generation and transmission
• Intelligent control technique (e.g., neural network and fuzzy logic control) for renewable systems
• Adaptive observer design and estimation for complex energy systems
• Iterative learning for optimization and control with applications to renewable energy systems