Version 1
: Received: 17 January 2024 / Approved: 18 January 2024 / Online: 18 January 2024 (12:33:33 CET)
How to cite:
Wu, J.; Wang, K.; Wang, T.; Ma, S.; Gong, H.; Hu, Z.; Gong, Q. Active Distribution Grid Exceeding Testing and Risk Planning Based on Carbon Capture and Multi-Source Data of Power Internet of Things. Preprints2024, 2024011420. https://doi.org/10.20944/preprints202401.1420.v1
Wu, J.; Wang, K.; Wang, T.; Ma, S.; Gong, H.; Hu, Z.; Gong, Q. Active Distribution Grid Exceeding Testing and Risk Planning Based on Carbon Capture and Multi-Source Data of Power Internet of Things. Preprints 2024, 2024011420. https://doi.org/10.20944/preprints202401.1420.v1
Wu, J.; Wang, K.; Wang, T.; Ma, S.; Gong, H.; Hu, Z.; Gong, Q. Active Distribution Grid Exceeding Testing and Risk Planning Based on Carbon Capture and Multi-Source Data of Power Internet of Things. Preprints2024, 2024011420. https://doi.org/10.20944/preprints202401.1420.v1
APA Style
Wu, J., Wang, K., Wang, T., Ma, S., Gong, H., Hu, Z., & Gong, Q. (2024). Active Distribution Grid Exceeding Testing and Risk Planning Based on Carbon Capture and Multi-Source Data of Power Internet of Things. Preprints. https://doi.org/10.20944/preprints202401.1420.v1
Chicago/Turabian Style
Wu, J., Zhijian Hu and Qingwu Gong. 2024 "Active Distribution Grid Exceeding Testing and Risk Planning Based on Carbon Capture and Multi-Source Data of Power Internet of Things" Preprints. https://doi.org/10.20944/preprints202401.1420.v1
Abstract
In order to achieve its carbon peak and neutrality targets, a high proportion of distributed power sources are connected to the distribution network in China, which greatly increases the risk of distribution network operation. Aiming at the above problems, this paper proposes an active distribution network risk planning model based on multi-source data from carbon capture and power internet of things, and conducts exceeding testing of distribution network based on the stochastic load flow algorithm with semi-invariant and level expansion, calculates the semi-invariant of each order of node state vectors and branch current vectors, and then utilizes the Gram-Charlier level expansion to obtain the exceeding probability density function and probability distribution function of the node voltages and line powers of the distribution network. The probability density function and probability distribution function of the exceeding are obtained using the Gram-Charlier series expansion. Combined with multi-source data, the active distribution network with integrated energy system considering carbon capture is modeled. According to the risk scenario of the distribution network, the nonconvex constraints in the model are simplified by the second-order cone relaxation, and the optimal planning scheme of the distribution network is solved by combining the gurobi solver with the risk index as the first-level objective and the economic benefit as the second-level objective. The simulation results of a coupled network consisting of a 39-node distribution network and an 11-node transportation network verify the effectiveness of the proposed model.
Keywords
active distribution networks; exceeding testing and risk planning; power internet of things; semi-invariant method; integrated energy; second-order cone
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
