Optimization of high-speed rail energy centralized management in smart grids using dynamic management algorithms
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Hongyan Gao
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Youqing Shi
Abstract
The incorporation of Hybrid Energy Storage Systems (HESS) into alternating current (AC) electrified railway systems has attracted considerable attention. Nevertheless, only a little focus has been placed on optimizing the dimensions and daily delivery of HESS throughout the entire project duration. This research introduces an innovative bi-level method for Railway Traction Substation Energy Management (RTSEM). At the master level, HESS dimensions are refined, whereas the slave level emphasizes daily allocation. The slave model utilizes Mixed-Integer Linear Programming (MILP) to control energy distribution, synchronizing HESS, renewable sources, and regenerative braking. An extensive cost evaluation takes into account battery deterioration and replacement expenses throughout the project duration. Using an integrated CPLEX solver, Ray optimisation is utilized to address the RTSEM challenge. The model is evaluated on an actual high-speed railway line scenario in China. Findings indicate that combining HESS with renewable energy yields considerable cost savings, as the suggested Ray Optimization-based model achieves a lowest fitness value of 0.016, in contrast to the highest fitness values of 0.033, 0.005, and 0.002 realized by BSO, GA, and GOA models, respectively.
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Research ethics: Not applicable.
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Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.
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Author contributions: All authors approved the manuscript and assume responsibility for its content. Hongyan Gao (Corresponding author) led the research, with key contributions as follows: H.G. (Corresponding author): led conceptualization of the bi-level HESS optimization framework; developed the algorithm and authored core model sections; coordinated data validation with real HSR cases. Y.Q. Shi: collected and preprocessed traction load/regenerative braking data; assisted in simulation modeling. Y.Xia: conducted literature review and identified research gaps; supported model validation against traditional algorithms. T. Yang: provided technical input on battery degradation and cost analysis; contributed to manuscript revision.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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Articles in the same Issue
- Frontmatter
- Research Articles
- Abnormal behaviour recognition technology in smart grid monitoring based on artificial intelligence
- Design of intelligent anti electricity theft monitoring device based on ARM computing modeling
- Translation and context adaptability study of renewable energy technology terms in English and Chinese
- Performance scoring model for new energy vehicles based on Hadoop
- Analysis of electronic product design schemes based on embedded systems
- Use of a Duffing chaotic oscillator with nonlinear stochastic resonance to retrieve faulty power equipment records
- Vocabulary selection methods of Chinese language in solar panel branding and promotion
- Application of distribution network monitoring information automatic verification platform driven by artificial intelligence in improving acceptance testing and power grid operation and maintenance management efficiency
- Data-driven wind power prediction model based on improved generative adversarial network
- Effects evaluation of English listening comprehension in new energy majors with multimedia assistance
- Enhancement of electricity theft detection model for electricity metering system using machine learning and Pearson’s correlation coefficient
- Fusion application of intelligent sensing technology and artificial intelligence algorithm in information verification of new energy grid connection monitoring
- Optimization of high-speed rail energy centralized management in smart grids using dynamic management algorithms
