Construction and Optimization of Dynamic Assessment Indicators for Distribution Grid Carrying Capacity with High Proportion of Distributed Resource Connections
Author:
Zhou Zhongqiang1, Wen Yuan2, Jiang Dan3, Deng Ruifeng4, Ma Jianwei1, Meng Jingrong5
Affiliation:
1. Department of Automation , Dispatching & Control Center of Guizhou Power Grid , Guiyang , Guizhou , , China . 2. Power Dispatching & Control Center, Kaili Power Supply Bureau of Guizhou Power Grid , Kaili , Guizhou , , China . 3. Power Dispatching & Control Center, Tongren Power Supply Bureau of Guizhou Power Grid , Tongren , Guizhou , , China . 4. Power Dispatching & Control Center, Xingyi Power Supply Bureau of Guizhou Power Grid , Xingyi , Guizhou , , China . 5. Shanghai Jiao Tong University Sichuan Research Institute , Chengdu , Sichuan , , China .
Abstract
Abstract
In this study, we evaluate the enhanced carrying capacity of the modified IEEE 33-node distribution system, which incorporates a high percentage of distributed resource (DR) integration, utilizing the whale optimization algorithm. The evaluation employs a fuzzy comprehensive evaluation (FCE) method, wherein we construct FCE indices for factors influencing the distribution network’s carrying capacity. These indices involve calculating weight coefficients and establishing an affiliation function, culminating in the output of the final fuzzy evaluation result vector. The analysis identifies three nodes—11, 18, and 31—within the IEEE 33-node system for decentralized DR installation. Based on initial scenario data, the maximum carrying capacity of the distribution network is determined to be 40.68 MW. The analysis further reveals that the utilization rate of distributed resources reaches 100% at specific times, specifically at 2:00 AM and 6:00 AM. During the 3:00 to 5:00 AM interval, both the system voltage excursion index and the composite score for distribution network losses are calculated to be low. These findings suggest the feasibility of integrating a single regional distribution network with other energy networks, thereby facilitating multi-regional resource interconnection and enhancing the flexibility of system operations. This approach offers significant implications for improving the robustness and efficiency of electrical distribution systems.
Publisher
Walter de Gruyter GmbH
Reference16 articles.
1. Huang, W., Li, N., Zhang, Z., Hu, H., Huang, X., & Guo, F. (2021). Study on the influence of current on 500 kv ac marine cable based on numerical simulation. AIP Advances, 11(8), -. 2. Giannitrapani, A., Paoletti, S., Vicino, A., & Zarrilli, D. (2017). Optimal allocation of energy storage systems for voltage control in lv distribution networks. IEEE Transactions on Smart Grid, 8(6), 2859-2870. 3. Bletterie, Benoit, Hatziargyriou, Nikos, Varela, & Jesus, et al. (2017). The igreengrid project increasing hosting capacity in distribution grids. IEEE Power & Energy Magazine. 4. Dsouza, K., Halbe, S., Thomas, M., Baran, M., Chowdhury, B., & Schwarz, P., et al. (2021). A comprehensive methodology for assessing the costs and benefits of renewable generation on utility operations. Renewable Energy, 177. 5. Tan, Q., Ding, Y., & Zhang, Y. (2017). Optimization model of an efficient collaborative power dispatching system for carbon emissions trading in china. Energies.
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