Electric vehicle capacity forecasting model with application to load levelling

There are many uncertainties in forecasting the charging and discharging capacity required by electric vehicles (EVs) often as a consequence of stochastic usage and intermittent travel. In terms of large-scale EV integration in future power networks this paper develops a capacity forecasting model which considers eight particular uncertainties in three categories. Using the model, a typical application of EVs to load levelling is presented and exemplified using a UK 2020 case study. The results presented in this paper demonstrate that the proposed model is accurate for charge and discharge prediction and a feasible basis for steady-state analysis required for large-scale EV integration.

Examining the presence of Arching-Action in Edge Stiffened Bridge Deck Cantilever Overhangs Subjected to a Wheel Load

Engineers have proposed the idea that there may be some arching action present in bridge deck cantilever overhangs stiffened along their longitudinal free edge, via a traffic barrier, subjected to a wheel load. This paper includes the details of a full-scale corrosion-free bridge deck with cantilever overhangs stiffened along their longitudinal free edge by a traffic barrier wall that has been constructed and tested under static and fatigue wheel loads at the University of Manitoba. It also reviews experimental test results and postulates various discussions that suggest the presence of arching-action in cantilever slab overhangs. Test results indicated static ultimate load capacities significantly greater than the ultimate capacity if the mode of failure and behavior of the cantilever overhang was completely flexural. These early results confirm and indicate the presence of arching-action resulting in a significant break-through in cantilever behavior when subjected to a wheel load. The theory to account for this arching-action is not yet developed and further research should be conducted.

Algorithmic method for scraper load-time optimization

Scrapers have established an important position in the earthmoving field as they are independently capable of accomplishing an earthmoving operation. Given that loading a scraper to its capacity does not entail its maximum production, optimizing the scraper’s loading time is an essential prerequisite for successful operations management. The relevant literature addresses the loading time optimization through a graphical method that is founded on the invalid assumption that the hauling time is independent of the load time. To correct this, a new algorithmic optimization method that incorporates the golden section search and the bisection algorithm is proposed. Comparison of the results derived from the proposed and the existing method demonstrates that the latter entails the systematic needless prolongation of the loading stage thus resulting in reduced hourly production and increased cost. Therefore, the proposed method achieves an improved modeling of scraper earthmoving operations and contributes toward a more efficient cost management.

Analysis of Load Impact on Small-signal Stability based on Load Active Power Sensitivity

While load flow conditions vary with different loads, the small-signal stability of the entire system is closely related with to the locations, capacities and models of loads. In this paper, load impacts with different capacities and models on the small-signal stability are analysed. In the real large-scale power system case, the load sensitivity which denotes the sensitivity of the eigenvalue with respect to the load active power is introduced and applied to rank the loads. The loads with high sensitivity are also considered.