2018/11/07 by Mehdi Shafiei, Shafiei, Mehdi, Aaron Liu +9
Engineering · Environmental Science · #Electric Power System Optimization #Energy and Environment Impacts #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Power System Reliability and Maintenance #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1811.12911
openalex publication_date 2018/11/07 · openalex created_date 2022/08/02 · openalex updated_date 2026/07/28
The increasing quantity of PV generation connected to distribution networks\nis creating challenges in maintaining and controlling voltages in those\ndistribution networks. Determining the maximum hosting capacity for new PV\ninstallations based on the historical data is an essential task for\ndistribution networks. Analyzing all historical data in large distribution\nnetworks is impractical. Therefore, this paper focuses on how to time\nefficiently identify the critical cases for evaluating the voltage impacts of\nthe new large PV applications in medium voltage (MV) distribution networks. A\nsystematic approach is proposed to cluster medium voltage nodes based on\nelectrical adjacency and time blocks. MV nodes are clustered along with the\nvoltage magnitudes and time blocks. Critical cases of each cluster can be used\nfor further power flow study. This method is scalable and can time efficiently\nidentify cases for evaluating PV investment on medium voltage networks.\n