Research on Optimization and Scheduling Control Strategy of Renewable Energy Grid-Connection Based on Intelligent Control

Authors

  • Guo Li College of Intelligent Manufacturing and electrical engineering, Nanyang Normal University, Nanyang, Henan 473000, China, Collaborative Innovation Center of Intelligent Explosion-proof Equipment, Henan Province, China
  • Shaowei Ji Jiangsu Provincial Internet Industry Management Service Center, Nanjing, JiangSu, 210000, China
  • MingHua Wang Shandong Gete Aviation Technology Co., Ltd, Jinan, ShanDong 250000, China
  • Chenyuan Guo Faculty of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, Henan, China

DOI:

https://doi.org/10.13052/dgaej2156-3306.4114

Keywords:

Renewable energy grid connection, intelligent control, intelligent optimization algorithm, power grid scheduling strategy

Abstract

With the rapid development of renewable energy, the fluctuation of its output poses huge challenges to the stability and reliability of power systems. To improve the efficiency of renewable energy grid-connection, this paper studies an optimization and scheduling control strategy for renewable energy grid-connection based on intelligent control. First, the limitations of traditional scheduling strategies in the face of large-scale renewable energy integration are analyzed, and an optimized scheduling model combining intelligent control algorithms is proposed. This model responds in real time to the dynamic changes of the power system, optimizes the consumption of renewable energy generation, and effectively reduces the volatility in system scheduling. On this basis, an intelligent optimization control framework is proposed. By intelligently adjusting the scheduling strategies of power generation units, it ensures system load balance while maximizing the proportion of green energy use. Real-time load forecasting and power generation forecasting information are used to optimize scheduling decisions, thereby enhancing the economy, flexibility, and reliability of the system. Experimental results show that the proposed strategy has significant advantages over traditional scheduling methods in terms of system operation costs, energy consumption efficiency, and renewable energy utilization rate.

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Author Biographies

Guo Li, College of Intelligent Manufacturing and electrical engineering, Nanyang Normal University, Nanyang, Henan 473000, China, Collaborative Innovation Center of Intelligent Explosion-proof Equipment, Henan Province, China

Guo Li was bom in HeNan, China, in 1980. From 1999 to 2009, he studied in Airforce Engineering University and received his bachelor’s degree in 2003, and received his Master’s degree in 2006, and received his Doctor’s degree in 2009. Currently, he works in Nanyang Normal University. He has published ten papers, five of which has been indexed by SCI and EI. His research interests are included intelligent information processing and loT.

Shaowei Ji, Jiangsu Provincial Internet Industry Management Service Center, Nanjing, JiangSu, 210000, China

Shaowei Ji was born in Anhui, China, in 1974. From 1993 to 1997, he studied in Airforce Telecom College of PLA and received bachelor’s degree in 1997. From 2002 to 2005, he studied in the Southeast University and received Master’s degree in 2005. He has published many papers and one book in the army. His research interests are included microwave communication and Internet Technology.

MingHua Wang, Shandong Gete Aviation Technology Co., Ltd, Jinan, ShanDong 250000, China

MingHua Wang was bom in AnHui, China, in 1974. From 1992 to 1996, he studied in Air Force Telecommunications Engineering College and received his bachelor’s degree in 1996. From 2005 to 2007, he studied in ShanDong University and received his Master’s degree in 2007. Currently, he works in Shandong Gete Aviation Technology Co., Ltd. His research interests are included flight information processing and loT.

Chenyuan Guo, Faculty of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, Henan, China

Chenyuan Guo, born on September 18, 2003. Majored in mechanical design and automation. Mainly researching mechanical system control algorithms and automatic control principles.

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Published

2026-02-17

How to Cite

Li, G. ., Ji, S. ., Wang, M. ., & Guo, C. . (2026). Research on Optimization and Scheduling Control Strategy of Renewable Energy Grid-Connection Based on Intelligent Control. Distributed Generation &Amp; Alternative Energy Journal, 41(01), 83–100. https://doi.org/10.13052/dgaej2156-3306.4114

Issue

Section

Renewable Power & Energy Systems