On-road or dynamic wireless charging systems constitute electrified highways on which electricity from the electric grid is supplied to electric vehicles wirelessly as they travel along the road, rather than the vehicles solely relying on the storage capacity of batteries. Electrification of highways can contribute to decarbonization in the transport sector and provide a solution to range anxiety, high battery costs and long charging times of electric vehicles. However, installing the wireless charging infrastructure along highways is costly. This paper presents a modeling approach that has been developed based on key variables of dynamic wireless charging systems to minimize the infrastructure cost so that the deployment of electrified highways could be economically viable. The overall investment for the dynamic wireless charging systems consists of different types of costs, including those for inverters, road-embedded power transmitter devices, control devices and grid connections. The costs of the different components depend on traffic flows but to different extents, resulting from the amount of energy demanded in a specific section of the electrified highway (i.e. the traffic flows are section-dependent). It is shown that the charging power level that could vary from 165 kW to 400 kW and road coverage ratio of an electrified highway are interrelated with regard to the economic context. Based on the developed model, the configuration and deployment of a proposed electrified highway in Eastern Canada are designed with an optimal charging power level and road coverage ratio or intermittency, thus achieving the best cost effectiveness. Intermittent electrified highways have the potential to reduce overall investment cost over fully electrified highways. In addition, the cost break-up of various components of the dynamic wireless charging system is estimated.
Increasing shares of renewables in the energy matrix is linked to increased power price fluctuations, which, in turn, increases the financial risks for electricity market participants. In this context, understanding the key factors driving the power prices and thereby improving price forecasts is increasingly important. Here we analyze the main drivers of power prices with the help of machine learning. We show how the selection of the predictors set and length of historical data affect the forecast accuracy of the power prices. Using the developed model, we project how high energy and carbon prices may affect future electricity prices.
When a transformer fault occurs, the transformer oil will decompose and produce a large amount of dissolved gas in the oil, based on the dissolved gas in the oil to diagnose whether there is a fault in the transformer, known as dissolved gas analysis (DGA), in order to effectively predict whether a transformer fault will occur in the future, so as to prevent the development of the fault in time at the early stage of the fault, proposed A model for predicting the dissolved gas concentration in transformer oil based on the firefly algorithm (FA) optimized random forest (RF), which uses the random forest as the prediction model and adjusts the parameters in the RF by means of the firefly algorithm. The experimental results show that the FA algorithm can effectively optimize the parameters in the RF and improve the prediction accuracy of the model, overcoming the shortcomings of the traditional RF algorithm which uses random parameters with low accuracy, and the model can predict the dissolved gas concentration in oil more accurately than the existing methods.
Thermal fatigue in a T-junction is of crucial importance issue for the coolant system of nuclear energy plants. The dynamic mode decomposition (DMD) is employed to analyze the snapshot data from simulation results with applying large eddy simulation (LES). The thermal mixing flow in a square T-junction is simulated at the impinging jet (MR = 0.2). The temperature difference of hot and cold fluids is 15 K. The corresponding Reynolds number is about 20000. The results show that the frequency of the velocity modes is not equal to that of the temperature mode. The frequency of the temperature mode 1 is more than 40% higher than that of the velocity mode 1. The main spatial structures of the temperature field and the velocity field are alternately arranged along the trajectory of the branch fluid entering the main duct. The main coherent structure of the velocity field arrives at the bottom wall of x/Dm = 1, whereas for the temperature field, it basically appears in the region of x/Dm = 0.6 – 0.8. The negative structures of the velocity mode 1 induces the positive structures of the temperature mode 1. Also, the coherent structures of Modes 1 and 2 grow along the normal direction during downstream propagation.
The application of an internally cooled desiccant enhanced evaporative cooling system (ICDEVap) in Hong Kong is a promising scheme for energy saving and emission reduction. It consists of liquid desiccant dehumidification (LDD) and regenerative indirect evaporative cooling (RIEC) and can operate without a power-intensive compressor. The hot and humid air is first dehumidified by the internal cooling-LDD, and then sensibly cooled by the RIEC. To ensure efficient energy utilization and better indoor air quality simultaneously, the return air is indirectly utilized in the internal cooling of the LDD, which alleviates the efficiency deterioration of the desiccant. The influence of the return air ratio on the system performance is analyzed. The results show that the ICDEVap system operating at the optimal return air ratio saves 48% of the energy consumption compared to the mechanical vapor compressor refrigeration (MVCR) system in Hong Kong summer.