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The goal of carbon neutrality is an extensive and profound economic and social change, which will have far-reaching impacts on industrial structure, energy structure, and social consumption structure. Energy sectors will face in-depth adjustment, and it is essential to optimize major structures consequently due to the foresight of talent training. This research first employs Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, Analytic Network Process (ANP), and the weighted fuzzy Technique for Order Performance by Similarity to Ideal Solutions (TOPSIS) to formulate and analyze the structure optimization of academic disciplines, and finally, the universities featuring mining are taken as an example to verify the feasibility of the method. Results reveal that the integration of ANP, SWOT, and the fuzzy TOPSIS evaluation method is able to qualify the assessment for academic discipline optimization. The specialty structure optimization results should focus on clean, intelligent, and sustainable development of the coal industry. The first priority is to increase relevant research on sustainable development of the mining industry, with a priority value of 0.0435. The modern coal chemistry and intelligent coal mining are also highly valued as the options for achieving carbon neutrality. Adding natural gas-related majors is underestimated as the least recognized priority, with a priority value of 0.0133. Suggestions and implications are provided for structure optimization of academic disciplines in universities featuring energy.

Distribution automation (DA) and Internet of Things (IoT) all need the topology information of power distribution network to support some applications, such as fault diagnosis, network reconfiguration and optimization. IEC 61850 is a general communication model and standard for information exchange between intelligent electronic devices (IEDs). However, it has no mechanism for feeder topology information exchange. This paper solves this problem by developing the corresponding information model. Firstly, a feeder model is established as a container of the equipment along a distribution line. Secondly, logical models, such as terminal and connection nodes, are added to describe the physical connection relationship between the electrical equipment. Taking a circuit breaker as an example, this paper introduces how to add the terminal attribute to an existing logical node (XCBR). The physical connection between the circuit breaker and other electrical equipment is described by adding the logic node LCNN. Then, a new logical node LTPN is added to describe the logical connection between the devices. A new logical node, FTPA, is added to describe the status of the topology analysis and the topology results. Based on these new logical nodes, this paper proposes the mechanism of topology information exchange between IEDs. Three IEDs and the IEEE 13-node model are used to build an experimental environment. The result verifies the effectiveness of this method. More distributed applications can be used to test the validity and interoperability of the proposed model.