The increasing pressure from energy crisis and environment pollution has led to the urgency of energy structure and technology upgrade. As the future energy development trend, Cyber-Energy System (CES) focuses on deeply integrating multiple types of energy resources (including electricity power, heat, cool and gas, etc.) and advanced communication technology to improve the energy utilization efficiency, reduce the costs and emissions and increase the proportion of renewable energy resources. Since AI technology is both suitable for solving model-driven and data-driven research problems, it fits well with the feature of CES with diversified information date and interdependent infrastructures. By reasonably utilizing AI and multi-energy conversion technology, it is entirely possible for CES to achieve low (even zero) operation in the processes of energy generation, conversion, transmission, distribution and consumption, etc. Meanwhile, zero carbon of CES requires innovation in many aspects, including policy, markets, modeling, planning, control and operation, which brings many new challenges.The aim of the Research Topic is to address and disseminate state-of-the-art research and opportunities regarding application of AI and multi-energy conversion technology to achieve low/zero system operation for CES. Through a rigorous peer-review process, 10 articles have been accepted, which are summarized as follows.In the article "A Deep Learning Approach to the Transformer Life Prediction Considering Diverse Aging Factors," He et al. [1] investigated the aging phenomenon of power system transformers, whose representative degeneration variables are extracted from real transformer operational data. Combining with the average life of the equipment, the extracted features were used as indicators for the transformer reliability evaluations. A deep learning-based approach was developed by using a convolutional neural network for effective equipment life prediction. The performance of the transformer life prediction model was verified using field-test data.In the article "Autonomous underwater vehicle docking system for energy and data transmission in 36 cabled ocean observatory networks,"Sun et al. In the article "Energy Management Without Iteration-A Regional Dispatch Event-Triggered 75 Algorithm for Energy Internet", Tan [7] proposed a region scheduling event triggering algorithm 76 (RDETA). With RDETA, the energy management does not need to iterate with each asynchronous 77 communication and does not rely on a global synchronous clock. In addition, the RDETA is capable 78 of using regional communications and regional energy dispatching. Therefore, the size of the dispatch 79 area can be adjusted automatically according to the extent of the energy problem. Simulation results 80 and theoretical analysis demonstrate the effectiveness of the proposed algorithm.s 81
We thank all authors who submitted their high-quality article to the Special Issue, and all reviewers 107 who provided great efforts to accomplish their review tasks. 108
