Multi-time Scale Stochastic Optimization for Hybrid AC-DC Distribution Networks with PET Based on E-C-Kmeans Clustering

This paper addresses the uncertainty of distributed power supply in a hybrid AC-DC distribution network with multi-port Power Electronic Transformers (PET). A multi-time scale stochastic optimization model based on E-C-Kmeans clustering is proposed. First, the E-C-Kmeans clustering and reduction mod...

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Veröffentlicht in:2025 4th International Conference on Smart Grid and Green Energy (ICSGGE) S. 223 - 226
Hauptverfasser: Sun, Yaobin, Liu, Shourui, Lu, Jiashuo, Du, Yonggang, Zheng, Huankun
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 28.02.2025
Schlagworte:
Clustering algorithms
Costs
Distribution networks
E-C-Kmeans clustering algorithm
Hybrid AC-DC distribution network
Hybrid power systems
Optimization models
PET
Power electronics
Processor scheduling
stochastic optimization
Stochastic processes
Transformers
Uncertainty
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Zusammenfassung:This paper addresses the uncertainty of distributed power supply in a hybrid AC-DC distribution network with multi-port Power Electronic Transformers (PET). A multi-time scale stochastic optimization model based on E-C-Kmeans clustering is proposed. First, the E-C-Kmeans clustering and reduction model for new energy scenarios is constructed by integrating the CBFSAFODP algorithm (a fast search and density peak discovery algorithm) with the K-means algorithm. Then, the model aims to minimize wind curtailment penalties, operational and maintenance costs of energy storage devices, electricity purchasing costs, and micro gas turbine generation costs. A random optimization operation model for the day-ahead and intra-day scheduling of the hybrid AC-DC distribution network with PET based on E-C-Kmeans clustering is built. Finally, the case study on the hybrid AC-DC distribution network with PET verifies that the proposed model can reduce system operating costs and effectively address the uncertainty in the distribution of renewable energy generation.
DOI:10.1109/ICSGGE64667.2025.10984875