No abstract

The need for water safety management has increased in the transition zone between the Qinling Mountains and the Loess Plateau, China due to streamflow decline over the past 30 years. Vegetation greening, largely due to the result of the ‘Grain for Green’ program implemented in the Loess Plateau, is affecting regional streamflow together with climate change and direct human impacts. There is thus an urgent need to evaluate the relative importance of causes of streamflow variation in this region. A Hydrological Model of L′École de Technologie Supérieure (HMETS)-based segment identification analysis framework was presented to quantify the impacts of climate and human-driven changes on runoff under regional vegetation greening. Results showed that climate change and human interference were alternately dominant in the hydrological cycle from 1976 to 2015. Climate change played a major role in affecting runoff variation before 2000, and then human interference dominated. It is worth noting that temperature increases resulted in runoff reduction and induced more changes in streamflow when precipitation was high. Vegetation greening contributed highly to streamflow attenuation, and its impact on runoff variation was more significant after 2007. Generally, understanding the effects of temperature increases and vegetation greening on streamflow is important for the development of appropriate adaptation strategies for the planning and management of regional water resources.

The utilization of machine learning advantages in solving engineering problems has proven to be beneficial and accurate. Dealing with partial differential equations (PDEs), on the other hand, PINN could be enhanced using additional information from the physical mathematics of the problems. In this regard, a physics-informed neural network (PINN) is exploited in the current study to extract phase and group velocities of the multi-scale hybrid composite plate structure. In doing so, the exact differential equations of homogenized plate structure are extracted using Hamilton’s principle based on modified couple stress theory and shear deformation displacement field. The boundary conditions along with initial conditions are also considered in the equations. The PINN tries to minimize the loss function which is defined using extracted PDEs, boundary conditions, and initial conditions. Moreover, the results of PINN are further compared with the analytical solution using harmonic expansion of the displacement variables. The results show highly accurate and reliable results obtained using PINN in comparison to analytical results.

With the sharply increased development of variable renewable energy resources (VRERs) in recent years, the hydro-wind-photovoltaic (PV) hybrid system (HWPHS) has the prospective to enhance the grid integration of VRERs. Nevertheless, the intense variation associated with wind and PV generation causes uncertainties in the long-term operation of the HWPHS. To overcome this drawback, this paper develops a novel method to derive adaptive operating rules for a cascade HWPHS. First, a scenario-generating method coupling Kernel density estimation with the copula function is proposed to characterize the wind and PV forecast errors. Second, based on the power generation scenarios, an optimal scheduling model for the cascade HWPHS considering transmission section constraints is proposed to simulate the hydro-wind-PV complementary operation; finally, the long-term operating rules for the cascade HWPHS are extracted by grey relational analysis and BP neural network. As a case study, the HWPHS of the Wu River basin in China is chosen. Results demonstrate that the proposed model can effectively utilize the flexibility of cascade hydropower stations, improve transmission section utilization efficiency, and promote clean energy absorption.