Dual Layer Source Grid Load Storage Collaborative Planning Model Based on Benders Decomposition: Distribution Network Optimization Considering Low-Carbon and Economy
Jun Guo1,*, Maoyuan Chen1, Yuyang Li1, Sibo Feng2,3, Guangyu Fu3
1 North China Branch of State Grid Corporation of China, Beijing, 100032, China
2 NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing, 211100, China
3 Beijing Kedong Electric Power Control System Co., Ltd., Beijing, 100089, China
* Corresponding Author: Jun Guo. Email: 
Energy Engineering https://doi.org/10.32604/ee.2025.068894
Received 09 June 2025; Accepted 10 October 2025; Published online 08 January 2026
Abstract
The author proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network. The model plans the configuration of photovoltaic (3.8 MW), wind power (2.5 MW), energy storage (2.2 MWh), and SVC (1.2 Mvar) through interaction between upper and lower layers, and modifies lines 2–3, 8–9, etc. to improve transmission capacity and voltage stability. The author uses normal distribution and Monte Carlo method to model load uncertainty, and combines Weibull distribution to describe wind speed characteristics. Compared to the traditional three-layer model (TLM), Benders decomposition-based two-layer model (BLBD) has a 58.1% reduction in convergence time (5.36 vs. 12.78 h), a 51.1% reduction in iteration times (23 vs. 47 times), a 8.07% reduction in total cost (12.436 vs. 13.528 million yuan), and a 9.62% reduction in carbon emissions (12,456 vs. 13,782 t). After optimization, the peak valley difference decreased from 4.1 to 2.9 MW, the renewable energy consumption rate reached 93.4%, and the energy storage efficiency was 87.6%. The model has been validated in the IEEE 33 node system, demonstrating its superiority in terms of economy, low-carbon, and reliability.
Keywords
Benders decomposition; source grid load storage; distribution network planning; low-carbon economy; optimization model
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