Electric Power ›› 2025, Vol. 58 ›› Issue (10): 71-81.DOI: 10.11930/j.issn.1004-9649.202503066

• Flexible Operation and Planning of Low-Carbon and High-Reliability Distribution Networks • Previous Articles     Next Articles

A Refined Time-Series Production Simulation Method for New Rural Power Systems

ZHENG Yongle1(), WEI Renbo2(), FENG Yuang2, ZHANG Yihan1, CUI Shichang2(), WU Zhenyu1, JIANG Xiaoliang1, LI Huixuan1, AI Xiaomeng2, FANG Jiakun2   

  1. 1. State Grid Henan Economic and Technical Research Institute, Zhengzhou 450052, China
    2. State Key Laboratory of Advanced Electromagnetic Technology (Huazhong University of Science and Technology), Wuhan 430074, China
  • Received:2025-03-20 Online:2025-10-23 Published:2025-10-28
  • Supported by:
    This work is supported by National Natural Science Foundation of China (General Program) (No.52177088), Science and Technology Project of State Grid Henan Electric Power Company (Research on the Evolutionary Technology of High-Proportion Renewable Energy Consumption in Lankao, No.5217L0240012).

Abstract:

Accurately assessing the renewable energy accommodation capacity of rural power systems is of significant guidance for the planning and development of new rural power systems. However, due to the low voltage levels, large impedance ratios, and higher network loss ratios in rural power systems, the traditional DC power flow-based time-series production simulations are prone to inaccuracies when evaluating their accommodation capacity. Therefor this paper proposes a time-series production simulation model and solution method based on refined AC power flow. Firstly, considering the voltage and network loss characteristics, a refined operational model for rural power systems based on AC power flow model is established. Then, to address the computational challenges of this model in annual time-series production simulation, the AC power flow is convexified and relaxed using second-order cone relaxation techniques to reduce model complexity. Furthermore, a time segmentation strategy that balances both grid scale and computational efficiency is introduced, It decomposes the time-series production model into multiple subproblems solved via rolling optimization, drastically improving computational performance. Finally, a county-level rural power system was used as an example for simulation testing. and the results verified the effectiveness of the method in accurately assessing the capacity for renewable energy integration.

Key words: rural power systems, time-series production simulation, renewable energy, accommodation capacity evaluation, second-order cone relaxation