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The Operating Principle and Equilibrium Optimization Algorithm of a New Type of Green Digital Intelligent Passenger Station |
SHI Tianyun1, SHEN Haiyan1, LU Yulong2, YANG Guoyuan1, LI Jun2, LI Chao1, YANG Lingling2, DUAN Jiaying1, YAO Jian1 |
1. China Academy of Railway Sciences Corporation Limited, Beijing 100081, China; 2. Beijing Jingwei Information Technology Co. Ltd, Beijing 100081, China |
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Abstract Research purposes: Railway passenger stations are high-density large public places and energy consuming large households. In order to implement the national "dual carbon" strategy, focusing on passenger stations is an important breakthrough point for the green transformation of railways. At present, passenger stations prioritize "energy conservation" over "clean energy", and weaken "empowerment". The development of "digitalization, greening, and low-carbon" is each focused on, which restricts the pace of green transformation. The new theory and method of "new green digital intelligent passenger stations" proposed in this article fills the industry gap, to break through the evolution of " Three modernizations and Three energies" from "clear differentiation" to "integrated symbiosis", and promote the green and high-quality development of stations, providing guidance, reference, and demonstration. Research conclusions: (1) The characteristic model and transformation path of "Three Modernizations and Three Energies" provide new ideas for strengthening the supply of digital intelligence empowerment technology. (2) The operational principles of "Three Modernizations Collaboration, Digital Intelligence Empowerment, Green Traction, and Low Carbon Drive" and the theory of "Three Elements" synergy provide new methods for the transformation of green stations. (3) The BMCEO comprehensive balance optimization control algorithm proposed has been validated to improve efficiency by 20%~40% compared to traditional single factor optimization, providing a technical means to solve the problem of efficient operation of station balance under multi-objective constraints and dynamic uncertain complex influencing factors.
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Received: 16 October 2023
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