Research on the Optimization of Park Microgrids Based on Renewable Energy Utilization and Storage Systems

Authors

  • Wenzhe Lu
  • Jun'ang Zhao
  • Ziling Fang
  • Xianting Liu

DOI:

https://doi.org/10.54097/zd410h53

Keywords:

Optimal Allocation of Energy Storage, Multi-objective Optimization Model, Genetic Algorithm, Wind and Solar Energy Storage Synergy.

Abstract

In recent years, renewable energy has been widely used, meeting global energy demands and domestic challenges. However, its intermittency and instability remain debated. For this reason, to obtain the allocation of renewable energy scenarios for different scenarios, this study simulates the microgrid reserve optimization problem for three parks. Optimize the strategy and determine the total power supply cost for the purchased electricity. Use power consumption as the transmission medium to optimize electric capacity. In conclusion, the initial objective was to integrate an energy storage apparatus to optimize energy storage and reduce power consumption as a fundamental premise. Optimize the energy storage system by considering equipment layout cost and minimum power consumption. Build a math model for comprehensive optimization of wind and PV reserves. A genetic algorithm was employed to optimize the energy storage system, resulting in an optimal energy storage power of 146 kW. This methodology ensures the lowest average power consumption for the energy storage system. Compared to pre-optimized energy storage, the average electricity cost for the three parks is significantly lower. The marginal contribution of the three parks ABC is ¥4409, ¥5423, and ¥4853, respectively, and the total marginal contribution is $14685, which shows good economic efficiency.

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References

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Published

11-12-2024

How to Cite

Lu, W., Zhao, J., Fang, Z., & Liu, X. (2024). Research on the Optimization of Park Microgrids Based on Renewable Energy Utilization and Storage Systems. Highlights in Science, Engineering and Technology, 119, 207-215. https://doi.org/10.54097/zd410h53