Overview of the Application of Artificial Intelligence in China’s Park-Level Integrated Energy System: Current Status, Challenges, and Future Paths

Shuangzeng Tian, Qifen Li (), Fanyue Qian (), Liting Zhang and Yongwen Yang
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Shuangzeng Tian: College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Qifen Li: The Science and Education Integration College of Energy and Carbon Neutralization, Zhejiang University of Technology, Hangzhou 310014, China
Fanyue Qian: College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Liting Zhang: College of Electric Power Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Yongwen Yang: College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 200090, China

Energies, 2025, vol. 18, issue 20, 1-23

Abstract: The global low-carbon energy transition relies on the orderly integration of a high proportion renewable energy. As an important carrier of demand-side energy systems, parks are responsible for local balancing and the accommodation of distributed renewable energy. However, the energy systems of parks exhibit the integrated characteristics of heterogeneous energy sources, including electricity, heat, and gas. It also encompasses the entire source–network–load–storage process, which renders it huge and complex. For this reason, as a systematic review article, this paper aims to summarize the overall application of artificial intelligence technology in China’s park-level comprehensive energy system. First, the current status of technology applications in the corresponding scenarios is analyzed based on three dimensions: prediction, scheduling, and security. Subsequently, key challenges in applying AI technologies to these scenarios are identified, including multi-temporal and spatial synergy issues in source–load forecasting, multi-agent equilibrium problems in dispatch optimization, and cross-modal matching challenges in security operation and maintenance (O&M). Thereafter, the feasible directions to solve these bottlenecks will be discussed comprehensively in light of the latest research advancements. Finally, we propose a phased roadmap for technological development and to identify the key gaps in this research field, such as the lack of publicly available benchmark datasets, data exchange standards, and cross-campus validation frameworks. This article aims to provide a systematic theoretical reference and development framework for the in-depth empowerment of AI technology in the integrated energy system of industrial parks.

Keywords: integrated energy system; multi-temporal prediction; multi-agent scheduling; cross-modal diagnosis (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: 2025
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