Journal of Engineering, Project, and Production Management, 2027, 17(1), 2026-71
Power Grid Repair Time Prediction and Scheduling Using GA‑BP for Emergency Power Supply Operations
1 Senior Economist, Marketing
Service Center, State Grid Hebei Electric Power Co., Ltd., Xingtai Power
Supply Branch, Xingtai, 054000, China, E-mail: lijian_lij@126.com
(corresponding author).
2 Engineer, Marketing Department, State Grid Hebei Electric
Power Co., Ltd., Longyao County Power Supply Branch, Xingtai, 053350,
China
3 Senior Engineer, Marketing Service Center, State Grid Hebei
Electric Power Co., Ltd., Xingtai Power Supply Branch, Xingtai, 054000,
China
4 Senior Engineer, Marketing Department, State Grid Hebei
Electric Power Co., Ltd., Xingtai Power Supply Branch, Xingtai, 054000,
China
5 Senior Engineer, Marketing Service Center, State Grid Hebei
Electric Power Co., Ltd., Xingtai Power Supply Branch, Xingtai, 054000,
China
6 Senior Engineer, Marketing Service Center, State Grid Hebei
Electric Power Co., Ltd. Xingtai Power Supply Branch, Xingtai, 054000,
China
7 Senior Engineer, Bridge West Power Supply and Distribution
Center, State Grid Hebei Electric Power Co., Ltd., Xingtai Power Supply
Branch, Xingtai, 054000, China
Project Management
Received January 21, 2026; revised March 31, 2026; accepted September 5, 2026
Available online October 5, 2026
Abstract: Accurate prediction of power grid repair time and efficient allocation of resources are critical to improving the response capability of emergency power supply operations and infrastructure resilience under extreme events. However, current methods still show clear limitations in prediction accuracy, scheduling efficiency, and the coordination between these two aspects. This manuscript presents a prediction and scheduling strategy for emergency power supply scenarios. It develops a repair-time prediction method by combining a Genetic Algorithm (GA) with a Backpropagation (BP) neural network, and constructs a coordinated scheduling model using Improved Ant Colony Optimization (ACO). Experimental results show that the prediction method achieves a coefficient of determination of 0.958 for the repair time task, with a Mean Absolute Error of only 0.181 h. The prediction accuracy is higher than that of the comparison models. In addition, the scheduling model restores the load in 5.98 h under severe fault conditions. The average repair radius stays within 8.46 km, the resource use rate reaches 76.39%, and the load loss is more than 41.43% lower than that of the comparison models. These results show that the method proposed in this manuscript provides stable prediction performance and effective scheduling ability in emergency power supply operations. It offers a reliable technical path for an intelligent emergency command system and helps improve response speed and resource utilization efficiency in complex power grid environments.
Keywords: Genetic algorithm–backpropagation (GA-BP); power grid repair; time prediction; resource scheduling; improved Ant Colony Optimization (ACO); infrastructure resilience; emergency management.
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Citation: Li, J., Dong, X., Shao, D., Gu, S., Zhang, H., Meng, G., and Li, Y. (2027). Power Grid Repair Time Prediction and Scheduling Using GA‑BP for Emergency Power Supply Operations. Journal of Engineering, Project, and Production Management, 17(1), 2026-71.
