Study on the Evolutionary Process and Balancing Mechanism of Net Load in Renewable Energy Power Systems

Sile Hu, Jiaqiang Yang (), Yu Guo, Yue Bi and Jianan Nan
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Sile Hu: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Jiaqiang Yang: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Yu Guo: Inner Mongolia Power (Group) Co., Ltd., Hohhot 010020, China
Yue Bi: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Jianan Nan: Inner Mongolia Power (Group) Co., Ltd., Hohhot 010020, China

Energies, 2024, vol. 17, issue 18, 1-19

Abstract: With the rapid development of renewable energy sources such as wind and solar, the net load characteristics of power systems have undergone fundamental changes. This paper defines quantitative analysis indicators for net load characteristics and examines how these characteristics evolve as the proportion of wind and solar energy increases. By identifying inflection points in the system’s adjustment capabilities, we categorize power systems into low, medium, and high renewable energy penetration. We then establish adjustment models that incorporate traditional coal power, hydropower, natural gas generation, adjustable loads, system interconnections, pumped-storage hydroelectricity, and new energy storage technologies. A genetic algorithm is employed to optimize and balance the net load curves under varying renewable energy proportions, analyzing the mechanism behind net load balance. A case study, based on real operational data from 2023 for a provincial power grid in western China, which is rich in renewable resources, conducts a quantitative analysis of the system’s adjustment capability inflection point and net load balancing strategies. The results demonstrate that the proposed method effectively captures the evolution of the system’s net load and reveals the mechanisms of net load balancing under different renewable energy penetration levels.

Keywords: net load; quantitative analysis indicators; evolutionary process; adjustment inflection point; adjustment model; genetic algorithm (GA); balance mechanism (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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