Hybrid simulation model for navigation performance evaluation of the Three Gorges–Gezhouba Dams under novel regulations. (August 2022)

• Record Type: Journal Article
• Authors: Zhang, Yu; Tian, Hongwei; Li, Ran; Liang, Xiaolei; Li, Jun
• Is Part Of: Simulation. Volume 98:Number 8(2022)
• Page Start: 677
• Page End: 698
• Publication Date: 2022-08
• Subjects: Three Gorges–Gezhouba Dams -- multi-agent -- discrete event -- hybrid simulation; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/00375497211072536
• Languages: English
• ISSNs: 0037-5497

Abstract

As an important project on the golden waterway of the Yangtze River in China, the Three Gorges–Gezhouba Dams (TGGD) plays a pivotal role in the construction of the Yangtze River Economic Belt. To improve the efficiency and safety of ship traffic, some novel navigation regulations have been implemented that change the TGGD operation obviously. For example, a piecewise control strategy proposed in the regulations is applied to control the traffic flow of ships under a sectional manner. With the implementation of these regulations, how to understand the dynamic effects of new changes on TGGD has been an important problem. The purpose of this work is to evaluate the navigation performance of the TGGD via a data- and event-driven hybrid simulation model developed by multi-agent and discrete-event modeling theories. The model simulates the three significant navigable scenarios inherent in the actual operating environment: dry season, wet season, and flood season, reflecting the real situations. The input data come from the statistical analysis of the actual navigation data provided by the Three Gorges Navigation Administration. The validity and reliability of the model are verified by comparing the output results with actual data. Moreover, a set of test experiments are designed to explore the TGGD navigation limit and analyze the key factors that restrict the navigation capacity of the TGGD system. The work is expected to provide a certain decision support for the future cooperative scheduling optimization of the TGGD.