A Multi-Timescale Cooperative Scheduling Method for Flexible Load in Power Distribution System Considering Dynamic Transformer Rating

With the large-scale integration of new energy, electric vehicles, and other new loads, disorderly electricity consumption has led to surging peak loads and heightened overload risks for distribution transformers. Particularly in aging, high-density urban areas constrained by the cost and space limitations of upgrading distribution facilities, there is an urgent need to tap into the flexible load control potential of existing power distribution systems to ensure system safety. This paper proposes a multi-timescale cooperative scheduling framework for flexible loads in distribution systems, deeply integrating the dynamic load capacity of transformers with the dispatchable characteristics of a flexible load. First, a day-ahead scheduling layer based on multi-agent reinforcement learning is constructed to optimize electricity plans and smooth peak–valley loads in the distribution system. Second, a dynamic transformer-rating model for distribution transformers is established to uncover their dynamic load capabilities under varying environmental conditions. Finally, an intraday scheduling layer for flexible loads is developed. It dynamically matches the regulation demands of distribution transformers and flexible loads via real-time optimization of consumption strategies to address electricity price fluctuations and user behavior randomness. Case study results demonstrate that the methods described in this paper effectively reduce power load fluctuations, ensuring the safe and stable operation of distribution and power supply systems.