Control, Optimization and Schedule Strategy in Power Electronic Distribution Networks

Dear Colleagues,

The ongoing transition toward low-carbon energy systems has led to a rapid increase in distributed generation, renewable energy sources, energy storage systems, and power-electronic-interfaced loads within distribution networks. As a result, modern power distribution systems are progressively evolving into converter-dominated, actively managed networks characterized by low inertia, fast electromagnetic dynamics, bidirectional power flows, and high operational uncertainty.

Conventional planning and control frameworks are no longer sufficient to guarantee stable, secure, and economically efficient operations under these conditions. Advanced control strategies, real-time optimization frameworks, coordinated scheduling mechanisms, and accurate load and resource forecasting methods are therefore essential to enhance flexibility, resilience, and power quality in power electronic distribution networks.

This Special Issue aims to provide a forum for researchers and practitioners to present innovative theoretical developments, practical implementations, and case studies that address emerging challenges in control, optimization, scheduling, and forecasting for next-generation active distribution systems.

• Modeling, identification, and dynamic analysis of converter-dominated distribution networks;
• Stability assessment and enhancement in low-inertia power electronic systems;
• Coordinated control of distributed energy resources (DERs);
• Energy management and optimal scheduling of microgrids and multi-microgrid systems;
• Load forecasting techniques for active distribution networks;
• Renewable resource forecasting (solar, wind) and uncertainty quantification;
• Optimal sizing, placement, and dispatch of energy storage systems;
• Electric vehicle charging coordination and vehicle-to-grid (V2G) scheduling;
• Demand response modeling and optimal participation strategies;
• Multi-energy system coordination (electricity–heat–gas integration);
• Voltage, frequency, and power quality control in inverter-dominated grids;
• Distributed optimization algorithms and consensus-based control;
• Data-driven, artificial intelligence, and machine learning approaches for control and scheduling.

Dr. John Licari
Dr. Alexander Micallef
Guest Editors

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