Rock bursts are a serious geological disaster occurring in deep underground engineering operations, which will cause casualties and economic loss. The quantitative threshold of energy fractal dimension for immediate rock burst warning in a deep tunnel was studied. The study was conducted based on the immediate rock burst cases in the deep tunnels of Jinping Hydropower Station, China. Firstly, a fractal dimension calculation method was proposed for deep linear tunnels associated with microseismic monitoring to explore the energy fractal dimension during the 37 immediate rock bursts and their development to events of different intensities (intense and moderate events). On this basis, a mechanism analysis was undertaken to assess the distribution range and evolution of the energy fractal dimension in the development of the immediate rock bursts. Then, the energy fractal dimension, as a quantitative threshold, was taken as a criterion for judging the rock burst risk. Furthermore, the corresponding warning index and dynamic control method were established. This index and method were applied in the subsequent construction process. The results can be used as a guide to establish a dynamic warning system based on the microseismicity monitored and provide a scientific basis for the prediction, warning, and risk-control standard of rock burst disasters during excavation of deep tunnels.

This study integrates microseismic data and data from hundreds of rockbursts of different intensities that occurred in a water drainage tunnel and four deep headrace tunnels at Jinping II Hydropower Station in Sichuan Province, China. The tunnels with overburden depths between 1,900 and 2,525 m, the maximum principal stress of which reaches 63 MPa in a rock mass, is composed mainly of marble. The tunnels have a total length of 12.4 km. The microseismic energy produced during the development of immediate rockbursts induced by the excavation by the drill-and-blast method (DBM) and a tunnel-boring machine (TBM) were studied. The results indicate that the daily maximum microseismic energy can be used as a basis for estimating rockburst intensity. The daily maximum microseismic energy (logarithm) corresponds to intense rockbursts (>6), moderate rockbursts (between 5 and 6), weak rockbursts (between 4 and 5), and no rockbursts (<4). Before the occurrence of intense rockbursts, there were numerous weak a...

Improving the efficiency of renewable energy and electricity utilization is an urgent problem for China under the objectives of carbon peaking and carbon neutralization. This paper proposes an optimization scheduling method of electric vehicles (EV) combined with wind and photovoltaic power based on the Frank-Copula-GlueCVaR. First, a joint output model based on copula theory was built to describe the correlation between wind and photovoltaic power output. Second, the Frank-Copula-GlueCVaR index was introduced in a novel way. Operators can now predetermine the future wind-photovoltaic joint output range based on this index and according to their risk preferences. Third, an optimal scheduling model aimed at reducing the group charging cost of EVs was proposed, thereby encouraging EV owners to participate in the demand response. Fourth, this paper: proposes the application of a Variant Roth–Serve algorithm; regards the EV group as a multi-intelligent group; and finds the Pareto optimal strategy of the EV group through continuous learning. Finally, case study results are shown to effectively absorb more renewable energy, reduce the consumption cost of the EV group, and suppress the load fluctuation of the whole EV group, which has a practical significance and theoretical value.

With the implementation of the carbon neutral policy, the number of electric vehicles (EVs) is increasing. Thus, it is urgently needed to manage the charging and discharging behavior of EVs scientifically. In this paper, EVs are regarded as agents, and a multiagent cooperative optimization scheduling model based on Roth–Erev (RE) algorithm is proposed. The charging and discharging behaviors of EVs will influence each other. The charging and discharging strategy of one EV owner will affect the choice of others. Therefore, the RE algorithm is selected to obtain the optimal charging and discharging strategy of the EV group, with the utility function of the prospect theory proposed to describe EV owners’ different risk preferences. The utility function of the prospect theory has superior effectiveness in describing consumers’ utility. Finally, in the case of residential electricity, the effectiveness of the proposed method is verified. Compared with that of random charging, this method reduces the total EV group cost of EVs by 52.4%, with the load variance reduced by 26.4%.