Topic: Cooperative Control and Intelligent Decision-Making for Complex Energy Systems

This special issue focuses on cooperative control and intelligent decision-making for safe, efficient, low-carbon operation of complex energy systems. The increasing penetration of renewables, power electronic devices, and the need for source-grid-load-storage coordination pose disruptive challenges to traditional control paradigms. Achieving effective cooperative control and intelligent decision-making is crucial for managing such complexity. This special issue explores how advanced technologies are reshaping the system through integrated advances in several key areas: grid-forming control and virtual synchronous machine algorithms for high renewable integration; multi-agent cooperative control and distributed decision-making; cloud/edge computing, energy IoT, and data governance for situational awareness; multi-timescale optimization and virtual power plant dispatch; reinforcement learning-based intelligent decision-making and adaptive dynamic programming; large language models (LLMs) for energy system forecasting and intelligent decision; security and reliability assurance for AI-driven decision-making in power and energy systems; intelligent distribution networks and flexible demand-side interaction; and resilient defense against extreme events. Together, these advances mark a fundamental shift from passive response to proactive prediction, autonomous coordination, and intelligent decision-making, enabling an efficient, resilient, low-carbon closed-loop system.

This special issue provides a platform for sharing recent advances in complex energy systems and cooperative control and intelligent decision-making. The topic has attracted growing attention and represents a timely and valuable contribution. Original research and high-quality review articles are especially welcome.

Cooperative control, intelligent decision-making, complex energy systems, grid-forming control, virtual synchronous machine, multi-agent cooperative control, reinforcement learning, edge-cloud computing, virtual power plant, renewable energy integration