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2,407 results on '"Wang, Qingyuan"'

77. Carbonation performances of steel fiber reinforced geopolymer concrete

Author

Luan, Chenchen, Shi, Xiaoshuang, Wang, Qingyuan, Utashev, Nodir, Tufail, Rana Faisal, and Maqsoom, Ahsen

Subjects
Dynamic testing -- Methods, Reinforced concrete -- Mechanical properties -- Testing, Permeability -- Analysis, Engineering and manufacturing industries
Abstract

Steel fiber reinforced geopolymer concrete (SFRGC) shows excellent mechanical properties comparable to steel fiber reinforced concrete. However, research on the durability of SFRGC is scarce. This paper presents the carbonation performances of SFRGC, including the carbonation depth and strength variation during carbonation. The results show that the permeability and carbonation depth of SFRGC are close to those of plain geopolymer concrete (GC). Their carbonation depths are much deeper than those of cement concrete because of the lower alkali content of the geopolymer matrix. Carbonation hardly influences the final flexural strength of plain GC, but the flexural strength of SFRGC declines significantly during carbonation because of the decrease in steel fiber-geopolymer matrix bond strength. The scanning electron microscope results show that the failure mode in uncarbonated SFRGC specimens is fiber rupture, while in carbonated SFRGC specimens, fiber pullout is the failure mode. These findings suggest that using SFRGC in the atmosphere is not recommended. Key words: geopolymer concrete, steel fiber, carbonation, flexural strength, 1. Introduction Conventional cement concrete (CC) is the most widely used construction material. The world produces about 4100 million tons of cement annually, while China's annual cement production exceeds 2400 [...]

Published
2023
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79. Peak load regulation pricing strategy of electric vehicle considering fast and slow charging characteristics

Author

WANG Qingyuan, CUI Li, WANG Mingshen, and GE Le

Subjects
electric vehicles, charging load guidance, peak cutting and valley filling, fast and slow charging constraint, deep reinforcement learning, net load, Applications of electric power, TK4001-4102
Abstract

With the widespread use of distributed photovoltaic large-scale power generation, the net load ′duck′ curve has become more apparent. However, electric vehicle charging during the day is unable to fully utilize the new energy, and charging at night only adds to the already existing load peak. To address the issue of net load ′peak-to-peak′ exacerbation, the charging load transfer process is facililated with the objective of minimizing the peak-valley difference of net load. To achieve this, statistical data on fast and slow charging behaviors are used to predict future charging load distribution through Monte Carlo simulations. Fast and slow charging load constraints are then established based on the network access characteristics of slow charging and the delayed charging characteristics of fast charging. Then, the load transfer rate is calculated using the gradient descent method, and the charging load price response model is constructed based on user consumption psychology. Finally, economic analysis of power grid peak shaving limits the constraint of electricity price change, and a charging guidance model is constructed with the goal of minimizing the peak-valley difference of net load. Deep reinforcement learning is used to solve the model and solution strategy. The simulation results show that the proposed model and solution strategy can effectively guide the charging load to avoid the peak period of the net load, determine a reasonable price, and reduce the peak-valley difference of the power grid.

Published
2023
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