Multi-Dimensional Impact Assessment of Large-Scale Flexible Load Integration into Distribution Networks Based on Fine-Grained Behavioral Models

With the large-scale integration of flexible loads, including electric vehicles (EVs), distributed energy storage systems (DSTs), communication base stations (COMs), and internet data centers (IDCs), into distribution networks, the increasing diversity of their operating characteristics is producing increasingly complex impacts on capacity requirements, power flow conditions, and reactive power support capabilities. However, existing studies have predominantly focused on isolated analyses of individual load types and still lack a unified evaluation framework for multiple representative flexible loads, making it difficult to systematically reveal the heterogeneous impacts of their grid integration. To address this gap, this paper develops fine-grained behavioral models for four representative flexible load categories by incorporating their key operational constraints and behavioral characteristics. A multi-dimensional quantitative assessment framework is then established across three dimensions: capacity, power flow and operation, and reactive power support and disturbance. Under a unified distribution network scenario, the impacts of large-scale integration are compared across load types and graduated penetration levels. The results show that different flexible loads exert significantly heterogeneous effects on distribution network operating states: COMs and IDCs are more likely to intensify local capacity pressure, operational fluctuations, and reactive power support burdens; EVs are more prominently associated with peak-period migration and reverse power flow risks; and the overall impact of DSTs remains comparatively moderate. The proposed methodology provides a unified analytical framework for assessing the impacts of multiple flexible load types on distribution networks and offers a reference for subsequent distribution network planning and operational optimization.