Optimization Control of Distributed Renewable Energy Systems

Dear Colleagues,

The global transition from fossil-fuel-based economies to sustainable energy systems is an urgent necessity to mitigate the escalating risks of climate change. Distributed Renewable Energy Systems (DRES), such as rooftop photovoltaics, small-scale wind turbines, and battery energy storage systems, are at the forefront of this transformation. However, the inherent intermittency and stochastic nature of renewable resources, coupled with the complex nonlinear dynamics of power electronic interfaces, pose significant challenges to the stability, reliability, and economic efficiency of modern power grids.

To address these challenges, advanced optimization and intelligent control techniques are essential. Artificial Intelligence (AI), including machine learning, reinforcement learning, and deep neural networks, offers powerful tools for modeling, forecasting, and managing the complexities of DRES. This Special Issue aims to provide a platform for researchers to present cutting-edge developments in the optimization and control of distributed energy resources, with a focus on enhancing system resilience and sustainability under changing climatic conditions.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• AI-based modeling and identification of distributed renewable energy sources.
• Advanced optimization algorithms for microgrid energy management.
• Intelligent control strategies for battery degradation mitigation.
• Climate-resilient optimization of distributed energy systems.
• Demand response and energy trading in smart grids.
• Integration of electric vehicles (EVs) with distributed renewable systems.

We look forward to receiving your contributions.

Dr. Haochi Wu
Dr. Mai Shi
Dr. Ming Yi
Dr. Jinghui Meng
Guest Editors

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