Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: Challenges and future perspectives

Zhengxuan Liu; Ying Sun; Chaojie Xing; Jia Liu; Yingdong He; Yuekuan Zhou; Guoqiang Zhang

Energy and AI (Nov 2022)

Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: Challenges and future perspectives

Zhengxuan Liu,
Ying Sun,
Chaojie Xing,
Jia Liu,
Yingdong He,
Yuekuan Zhou,
Guoqiang Zhang

Affiliations

Zhengxuan Liu: College of Civil Engineering, National Center for International Research Collaboration in Building Safety and Environment, Hunan University, Changsha, 410082, China; Faculty of Architecture and the Built Environment, Delft University of Technology, Julianalaan 134, 2628 BL, Delft, Netherlands; Corresponding authors.
Ying Sun: Energy and Environment Group, Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Canada; Corresponding authors.
Chaojie Xing: College of Civil Engineering, National Center for International Research Collaboration in Building Safety and Environment, Hunan University, Changsha, 410082, China
Jia Liu: College of Civil Engineering, Guangzhou University, Guangzhou, China; Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China; Guangdong Provincial Key Laboratory of Building Energy Efficiency and Application Technologies, Guangzhou University, Guangzhou, China
Yingdong He: College of Civil Engineering, National Center for International Research Collaboration in Building Safety and Environment, Hunan University, Changsha, 410082, China
Yuekuan Zhou: Sustainable Energy and Environment Thrust, Function Hub, The Hong Kong University of Science and Technology (Guangzhou), Nansha, 511400, Guangdong, China; Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong Special Administrative Region, China; HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Shenzhen, China; Corresponding authors.
Guoqiang Zhang: College of Civil Engineering, National Center for International Research Collaboration in Building Safety and Environment, Hunan University, Changsha, 410082, China

Journal volume & issue: Vol. 10
p. 100195

Abstract

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The vigorous expansion of renewable energy as a substitute for fossil energy is the predominant route of action to achieve worldwide carbon neutrality. However, clean energy supplies in multi-energy building districts are still at the preliminary stages for energy paradigm transitions. In particular, technologies and methodologies for large-scale renewable energy integrations are still not sufficiently sophisticated, in terms of intelligent control management. Artificial intelligent (AI) techniques powered renewable energy systems can learn from bio-inspired lessons and provide power systems with intelligence. However, there are few in-depth dissections and deliberations on the roles of AI techniques for large-scale integrations of renewable energy and decarbonisation in multi-energy systems. This study summarizes the commonly used AI-related approaches and discusses their functional advantages when being applied in various renewable energy sectors, as well as their functional contribution to optimizing the operational control modalities of renewable energy and improving the overall operational effectiveness. This study also presents practical applications of various AI techniques in large-scale renewable energy integration systems, and analyzes their effectiveness through theoretical explanations and diverse case studies. In addition, this study introduces limitations and challenges associated with the large-scale renewable energy integrations for carbon neutrality transition using relevant AI techniques, and proposes further promising research perspectives and recommendations. This comprehensive review ignites advanced AI techniques for large-scale renewable integrations and provides valuable informational instructions and guidelines to different stakeholders (e.g., engineers, designers and scientists) for carbon neutrality transition.

Published in Energy and AI

ISSN: 2666-5468 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering; Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software
Website: https://www.journals.elsevier.com/energy-and-ai

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