TY  - EJOU
AU  - Li, Yukai 
AU  - Zhang, Ruixue 
AU  - Ni, Yongfeng 
AU  - Qiu, Hongkai 
AU  - Zhang, Yuning 
AU  - Liu, Chunming 

TI  - Two-Stage Scheduling Model for Flexible Resources in Active Distribution Networks Based on Probabilistic Risk Perception
T2  - Energy Engineering

PY  - 2025
VL  - 122
IS  - 2
SN  - 1546-0118

AB  - Aiming at the problems of increasing uncertainty of low-carbon generation energy in active distribution network (ADN) and the difficulty of security assessment of distribution network, this paper proposes a two-phase scheduling model for flexible resources in ADN based on probabilistic risk perception. First, a full-cycle probabilistic trend sequence is constructed based on the source-load historical data, and in the day-ahead scheduling phase, the response interval of the flexibility resources on the load and storage side is optimized based on the probabilistic trend, with the probability of the security boundary as the security constraint, and with the economy as the objective. Then in the intraday phase, the core security and economic operation boundary of the ADN is screened in real time. From there, it quantitatively senses the degree of threat to the core security and economic operation boundary under the current source-load prediction information, and identifies the strictly secure and low/high-risk time periods. Flexibility resources within the response interval are dynamically adjusted in real-time by focusing on high-risk periods to cope with future core risks of the distribution grid. Finally, the improved IEEE 33-node distribution system is simulated to obtain the flexibility resource scheduling scheme on the load and storage side. The scheduling results are evaluated from the perspectives of risk probability and flexible resource utilization efficiency, and the analysis shows that the scheduling model in this paper can promote the consumption of low-carbon energy from wind and photovoltaic sources while reducing the operational risk of the distribution network.
KW  - Core operation boundary; probabilistic power flow; risk perception; optimize scheduling; flexible resource

DO  - 10.32604/ee.2024.058981