Research on power grid overall planning based on branch bound algorithm

Lei Yang; Sun Yunling; Yu Jiang

doi:10.1117/12.3025157

13 May 2024 Research on power grid overall planning based on branch bound algorithm

Lei Yang, Sun Yunling, Yu Jiang

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 131598Z (2024) https://doi.org/10.1117/12.3025157
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

As an important infrastructure of modern society, the power system is crucial to the reliability and efficiency of energy supply. The overall planning of power network is one of the key links to ensure the long-term sustainable operation of power system. The purpose of this thesis is to study a branch-bound algorithm based grid planning method to optimize the performance and reliability of power systems. First, we conduct a comprehensive survey and analysis of the current state of the power system, including key parameters such as power distribution, load demand, transmission lines and substations. Then, we propose an overall grid planning model based on branch-bound algorithm, which takes into account a variety of constraints, such as power system scalability, environmental impact and economic costs. Branch-bound algorithms are applied to power grid planning problems to efficiently search for optimal decision paths. The algorithm achieves efficient solution by continuously dividing the problem space and evaluating the upper and lower bounds of each subproblem. The effectiveness of the proposed method is verified by numerical experiments and simulations.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lei Yang, Sun Yunling, and Yu Jiang "Research on power grid overall planning based on branch bound algorithm", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 131598Z (13 May 2024); https://doi.org/10.1117/12.3025157

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