SSNN-Based Energy Management Strategy in Grid Connected System for Load Scheduling and Load Sharing

The proposed research work focused on energy management strategy (EMS) in a grid connected system working in islanding mode with the connected renewable energy resources and battery storage system. The energy management strategy developed provides a balancing operation at its output by utilizing perfect load sharing strategy. The EMS technique using smart superficial neural network (SSNN) is simulated, and numerical analyses are presented to validate the effectiveness of the centralized energy management strategy in a grid connected islanded system. A SSNN prediction model is unified to forecast the associated household load demand, PV generation system under various time horizons (including the disaster condition), EV availability, and status on EV section and distance. SSNN is one the most reliable forecasting methods in many of the applications. The developed system is also accounted for degradation battery model and its associated cost. The incorporation of energy management strategy (EMS) reduces the amount of energy drawn from the grid connected system when compared with the other optimized systems.

Experimental study on load sharing characteristics of long-short CFG pile composite foundation adjacent to rigid retaining wall rotating about its base

Practicing geoengineers and researchers generally consider the load sharing behavior in multi-type pile composite foundation as an important design aspect. On the other hand, due to urbanization, such foundation system in cities will inevitably appear next to supported excavation. This paper discusses the result from relatively large-scale indoor experiment conducted to investigate the load sharing behavior of loaded long-short CFG pile composite foundation behind a neighboring rigid retaining wall undergoing rotation around the bottom. It was found that with progression of wall movement, the hidden load from soil displacement was borne by the piles with marked reduction in soil load sharing. At the end of wall rotation, the percentage of long piles’ head load increment needed to arrive at a new static equilibrium was about 12.57~32.22% while the end bearing increased by more than 97%. The consequences on the short piles, however, were manifested with an increasing pile head (13.42%) and toe (28.9%) load for the pile far from the wall whereas the closest one experienced a certain increment up to 15×10-4rad wall rotation and finally the head load and end bearing decreased to 8.28% and 12.63%, respectively. The 3D numerical back analysis conducted using FE software ABAQUS yielded the pile – soil stress ratio lower than the value obtained from the experiment but provided great insight into pile settlement characteristics during wall rotation.

Multi-Factor Modeling Method of the Load Sharing Ratio under Moving Train Loads

In railway engineering, the load sharing ratio (LSR) is the ratio of the rail seat load (RSL) to the axle load, which is affected by many factors. The LSR can be used in the design and analysis of railway track structures as well as in the research of predicting the dynamic influence of railway tunnels and the environment. The “static loading method” commonly used to study the LSR does not conform to reality; using it, it is difficult to obtain a complete LSR curve, limiting its application. Besides, there is currently a lack of LSR prediction methods considering the impact of multiple factors. Therefore, this paper proposes a “moving loading method” for investigating the LSR under moving train excitation, verified to be rational by comparing with the experimental results. At the same time, a procedure for establishing the LSR multi-factor prediction model is put forward, namely, we (1) determine the LSR function form and the fitting algorithm; (2) perform parameter sensitivity analysis to determine the main influencing parameters of the LSR function; and (3) design a quadratic regression orthogonal test to obtain the prediction formula of the LSR function coefficients. Once establishing the prediction model for a type of train-track system, the LSR of similar systems can be calculated by adjusting the main parameters of the model. Shijiazhuang Metro Line 1 using the A-type vehicle and the monolithic trackbed is taken as a case study to develop a corresponding LSR multi-factor prediction model by the moving loading method and the procedure mentioned above. The results indicate that the proposed method performs well and can be adopted to enhance the accuracy of track design or tunnel and environmental vibration prediction.