Dynamic Aggregation Strategy for Distributed Resources Considering Strong Coupling With Residential Behavior

The uncertainty of distributed resource (DR) aggregation responses has become a key bottleneck restricting the reliable scheduling of virtual power plants (VPPs). To mitigate this uncertainty, this paper explores the strong coupling between residential behavior and the adjustable capability of distributed resources, and proposes a dynamic aggregation strategy that accounts for such coupling. First, the concept of Behavior-sensitive Distributed Resources (BDRs) is proposed and defined, where resources whose adjustable capability is significantly influenced by the number of residential users are categorized as BDRs. By applying the Discrete Fourier Transform (DFT), this study, for the first time, reveals the daily periodic characteristics of BDRs. Subsequently, a segmented confidence interval estimation method is introduced, which, in conjunction with the daily periodicity of BDRs, enables precise characterization of adjustable capability across different time periods, thus improving the accuracy of response capability modeling. An improved two-stage greedy aggregation algorithm is then designed to simultaneously reduce the uncertainty of VPP adjustable capability after aggregation and optimize economic performance. Finally, the strategy is validated using real-world data from a resource set that includes three representative types of BDRs: electric vehicle (EV) charging stations, commercial lighting (CL), and office building heating, ventilation, and air conditioning (HVAC) systems. The results demonstrate that, compared to existing approaches, the proposed aggregation strategy reduces VPP response uncertainty by over 22.63% and lowers aggregation cost by an average of 8.44%, thereby confirming its advantages in enhancing both grid reliability and economic efficiency.