Optimal Scheduling Method of Medium and Low Voltage AC-DC Hybrid Distribution Network Based on Edge Cloud Cooperation

The rapid advancement of power electronics technology has endowed VSC-interconnected AC/DC hybrid distribution networks with superior operational advantages, including enhanced power supply capacity and improved compatibility with renewable energy integration. Furthermore, the progressive development of cloud computing and edge computing architectures has significantly developed cloud-edge collaborative control technologies. Moreover, to synergistically leverage these dual technological advancements, this paper proposes a coordinated cloud-edge control methodology for medium/low-voltage VSC-based AC/DC hybrid distribution systems.

This paper proposes a cloud-edge coordinated control methodology for VSC-interconnected medium/low-voltage AC/DC hybrid distribution networks. The methodological framework comprises three principal phases: First, a comprehensive analysis is conducted on the interaction mechanisms and regulatory capabilities between cloud servers and edge computing nodes. Subsequently, a hierarchical control strategy is developed through cloud-edge coordination, where the cloud layer optimizes network loss minimization while edge layers simultaneously minimize the weighted sum of power losses and three-phase imbalance levels. Finally, the multi-objective optimization model is systematically transformed into a second-order cone programming (SOCP) formulation, establishing an efficient convex optimization framework.

A comprehensive case study was conducted on a representative AC/DC hybrid medium/low-voltage distribution network topology to validate the proposed methodology. The numerical results demonstrate that the medium-voltage (MV) side dispatch strategy achieves 52% network loss reduction compared to pre-dispatch conditions through active-reactive power coupling-enhanced photovoltaic accommodation. Furthermore, the cloud-edge coordinated framework enables deep exploitation of operational potential in low-voltage (LV) AC/DC interconnected feeder sections, effectively mitigating voltage violations while maintaining three-phase equilibrium constraints. Particularly, the synergistic optimization mechanism reduces power losses to 48% of baseline values through coordinated control of converter stations and intelligent edge devices.

The results of this paper show that the proposed method can promote the photovoltaic consumption of medium and low voltage AC / DC hybrid distribution network with high proportion of renewable energy generation access, make full use of the advantages of DC lines in new energy access capacity and the advantages of flexible equipment in flexible regulation and control ability, and ensure that the voltage of distribution network does not exceed the limit and the three-phase unbalance degree does not exceed the limit in the period of high photovoltaic output. However, it should be noted that the method proposed in this paper has certain limitations. The method proposed in this paper has higher requirements for global communication. For the low-voltage distribution station area with incomplete communication and incomplete measurement, distributed control and other methods are more suitable.

This study addresses the challenge of insufficient photovoltaic (PV) hosting capacity caused by large-scale distributed PV integration in medium/low-voltage distribution networks. Physically, we develop a hybrid AC/DC distribution network topology leveraging the flexible power dispatch capabilities of voltage source converters (VSCs), thereby overcoming the conventional radial topology constraints. Computationally, a cloud-edge coordinated control architecture driven by distributed computing paradigms is proposed, which synergistically exploits the regulation potential of low-voltage (LV) feeder sections through two coordinated mechanisms: 1) A hierarchical optimization framework that decouples system-level objectives (cloud layer) and local constraints (edge layer), significantly enhancing computational efficiency; 2) Dynamic resource allocation that fully utilizes edge computing nodes for real-time adjustment while maintaining global optimality through cloud-based coordination.