Topology Planning and Optimization of DC Distribution Network Based on Mixed Integer Programming and Genetic Algorithm

In the current situation of rapid development of the power industry, DC distribution network topology planning and optimization are of vital importance. This research studies the shortcomings of existing methods in terms of computational efficiency and optimization effect. Based on the real data of a medium-sized DC distribution network in a large city with 200 nodes and 350 lines, an innovative method combining mixed integer programming (MIP) and genetic algorithm (GA) is adopted. MIP is used to accurately describe physical constraints and optimization objectives, and GA efficiently searches for the best solution in the solution space with its global search capability. Experimental results show that the MIP-GA model has the lowest power transmission loss at different load levels. For example, at high load, it is 32% lower than the baseline, 16% lower than the MIP model, and 12.5% lower than the ACO model. It also performs best in terms of node voltage deviation, reliability, power quality and other indicators. Cost-benefit analysis shows that although the MIP-GA model has a relatively high investment cost for topology adjustment, it has the lowest annual power loss and maintenance cost, a reasonable total annual cost, a benefit-cost ratio of 1.5, and a payback period of only 3 years. Research has shown that this hybrid model has significant advantages in DC distribution network topology planning and optimization, and can effectively improve system performance and economic benefits.

@article{Cheng2025,
  title     = {Topology Planning and Optimization of DC Distribution Network Based on Mixed Integer Programming and Genetic Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Ran Cheng and Chong Gao and Hao Li and Junxiao Zhang and Ye Huang},
  doi       = {10.14569/IJACSA.2025.0160537},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160537}
}