Research on nonlinear constraint assessment model of power grid based on generalized Benders decomposition
Abstract Aiming at the problem of distribution network assessment for energy storage and distributed generation, this paper proposes a nonlinear constraint assessment model for power grid based on generalized Benders decomposition. With the optimization objective of minimizing the present value of t...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-08-01
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| Series: | Sustainable Energy Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40807-025-00195-7 |
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| Summary: | Abstract Aiming at the problem of distribution network assessment for energy storage and distributed generation, this paper proposes a nonlinear constraint assessment model for power grid based on generalized Benders decomposition. With the optimization objective of minimizing the present value of total cost, the nonlinear constraints of storage power, capacity, SVG construction are constructed by considering the costs of grid construction, power generation, maintenance, and dismantling, as well as the economic factors of energy storage and distributed power generation, and the constraints of grid operation under different load operation modes are incorporated into the model for peak, waist, and valley operation modes. The generalized Benders decomposition method is used to decompose the model into main problem and sub-problems, the main problem is solved iteratively by linear evaluation, and the sub-problems are solved iteratively by interior point method. The results show that the model and algorithm are effective in terms of planning cost and computational efficiency, taking the 24-node distribution network as the experimental object. Considering the energy storage access makes the construction cost increase slightly, but the operation cost is reduced significantly, and the total operation cost of the grid is reduced by more than 10%, and the computation process converges to the optimal value after 6 iterations in 2.4 s, which provides a more scientific and efficient decision-making support tool for the grid planning. |
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| ISSN: | 2731-9237 |