@article{GUO2026, 
author = {Ning GUO and Tuo JI and Yubo YUAN and Chuang ZHOU and Xiaolong XIAO and Shufeng DONG},
title = {Three-phase autonomous optimization method for distribution transformer areas based on power distribution and utilization edge agents},
year = {2026},
journal = {Electric Power Engineering Technology},
volume = {45},
number = {8},
pages = {46-56},
keywords = {edge intelligence, agent, three-phase distribution network, distribution transformer area autonomy, event-driven, convex relaxation},
url = {https://www.sciopen.com/article/10.12158/j.2096-3203.2026.08.005},
doi = {10.12158/j.2096-3203.2026.08.005},
abstract = {The introduction of edge intelligence technology to offload partial power services to the distribution station side serves as an effective technical pathway for distributed smart grid construction at the medium- and low-voltage distribution network level. This paper proposes a power service-oriented distribution and consumption edge intelligence agent, along with a three-phase autonomous optimization method for photovoltaic-storage integrated distribution transformer areas. Firstly, the definition of the distribution and consumption edge intelligence agent is established, with its architecture designed through domain-driven business modeling and event-driven state service mechanisms. Secondly, to address prominent three-phase asymmetry in distribution transformer areas, an improved three-phase distribution network branch power flow model is derived via convex relaxation. Finally, a three-phase coordinated multi-objective optimization model for photovoltaic-storage distribution transformer areas is developed based on the improved power flow model, integrated with the edge agent's state service mechanism for dynamic service processing. This achieves autonomous edge-side management encompassing localized state perception, event identification, optimized control, and closed-loop feedback. Case studies on a 21-node distribution transformer area and engineering validations demonstrate that the proposed edge intelligence agent effectively handles service requirements, while the proposed model and optimization strategy exhibit engineering-applicable accuracy and control effectiveness.}
}