Editorial for the Special Issue on Next-Generation Distribution System Planning, Operation, and Control

The past years have seen a progressive process of urbanization and upgrading, along with the intelligentialization and popularity of supply-demand sides through advanced information and communication technologies. Aiming at a more intelligent, efficient, and sustainable network, next-generation distribution systems encompass various innovative technologies, including co-/poly-generation [1], soft open points [2], multiple DC voltage levels [3], networked control [4], etc. With the large-scale integration of renewable energy sources and electric vehicles, the distribution system will continue to play a crucial role in the future [5].

This Special Issue of Technologies, “Next-Generation Distribution System Planning, Operation, and Control”, serves as a forum for the dissemination of the latest research findings and developments in strategies for next-generation distribution systems in the context of “CO₂ peaking and neutrality”. It comprises original research articles and reviews that investigate innovative artificial intelligence-based, cyber-physical, and advanced optimization and control techniques for distribution systems.

Working in the realm of renewable energy, Ochoa-Correa et al. [contribution 1] conduct a systematic review of the technical and operational challenges associated with transitioning island energy systems to fully renewable generation. This study highlights the critical need for advanced control strategies and energy management solutions. Targeting applications such as electric vehicles, robotics, and industrial automation, El Bazi et al. [contribution 2] compare various artificial neural network algorithms for permanent magnet temperature prediction using common performance indicators. This study aims to strike a balance between algorithm accuracy and computational cost. Hu et al. [contribution 3] propose an optimal transient control scheme for a grid-forming permanent magnet synchronous generator-based wind farm. Cheng et al. [contribution 4] propose a three-level service quality index system for wind turbines based on the fuzzy comprehensive evaluation method. While the previous contributions focus on wind farm control, Hao et al. [contribution 5] propose an MPC-based control method for the combined wind–storage frequency response. Both of these contributions significantly improve the overall system frequency stability.

Focusing on the solid-state transformer, Predescu et al. [contribution 6] conduct a systematic review of its fundamental topologies based on the number of conversion stages, while Predescu et al. [contribution 7] focus on its modular construction from the perspectives of its applications and available constructive degrees of freedom. Ibrahim et al. [contribution 8] propose a feature-enhanced multi-experimental methodology for oil-immersed transformer diagnostics using interpretable ensemble learning and multi-model evaluation. This study not only strikes a balance between interpretability and complexity but also outperforms previous regression-based approaches.

Focusing on the distribution network, Lou et al. [contribution 9] propose a voltage-constrained, differentiated resource-sharing framework to maximize economic benefits and renewable energy accommodation. Gao et al. [contribution 10] propose a structural–functional robustness assessment method for cyber-physical power systems. This work indicates that strong cyber network performance can improve the overall robustness of the cyber-physical system. Hebala et al. [contribution 11] present a microlevel, multicriteria assessment framework to compare fuel cell, battery, and hybrid electric vehicles in terms of energy consumption, drive systems, and emissions.

Focusing on the virtual power plant, Huang et al. [contribution 12] conduct a systematic review of its fundamental framework and dispatch optimization strategies. As an important responsive resource, Liu et al. [contribution 13] propose a frequency regulation strategy for thermostatically controlled loads in a renewable-rich power system. To improve the load frequency control performance, Pu et al. [contribution 14] propose a model predictive control method for a virtual power plant based on a mixed time/event-triggered mechanism. These contributions collectively ensure fundamental control performance and enhance frequency stability in low-inertia power systems.

To conclude, we would like to acknowledge all authors, reviewers, and the editorial support team for their contributions to the success of this Special Issue of Technologies. Although these published contributions do not cover all aspects of distribution system planning, operation, and control, this Special Issue has provided valuable insights for researchers, engineers, and decision-makers in the field, offering a solid reference point for advancing the next-generation distribution systems.