Experiments on Waste Heat Thermoelectric Generation for Passenger Vehicles

In order to utilize waste heat from passenger vehicles by a thermoelectric generator (TEG), a lab-scale TEG with a sufficient low-pressure drop was designed and tested. The waste heat from a 2.0 L petrol engine was simulated by using an air-circulation channel with an adjustable electric heater and a speed control motor. The TEG consisted of an integrated molding designed aluminum-finned heat collector, twenty thermoelectric modules, and a set of water-cooled heat sinks. Experiments were conducted in terms of power load feature, pressure drop, heat collection efficiency, thermoelectric efficiency and overall efficiency. It was found that the hot-end temperature was much lower (46.9%) than the flue gas temperature because the trade-off between fin area and pressure drop had to be considered. The obtained maximum electric power was 36.4 W, and the corresponding pressure drop was 36 Pa. The corresponding heat collection efficiency was 46.5%, and the thermoelectric efficiency was 2.88%, which agreed well with the theoretical prediction of 3.38%. As a result, an overall efficiency of 1.21% was reached. The present work firstly demonstrated a waste-heat-recovering TEG prototype with a balanced overall efficiency of over 1%, and a pressure drop of less than 50 Pa. On the other hand, the maximum electric power was difficult to fully extract. The charging power to a battery with a maximum power point tracking direct current–direct current converter was experimentally verified to work at a much higher conversion efficiency (15.3% higher) than regular converters.

Residential Short-Term Load Forecasting during Atypical Consumption Behavior

Short-term load forecasting (STLF) is a fundamental tool for power networks’ proper functionality. As large consumers need to provide their own STLF, the residential consumers are the ones that need to be monitored and forecasted by the power network. There is a huge bibliography on all types of residential load forecast in which researchers have struggled to reach smaller forecasting errors. Regarding atypical consumption, we could see few titles before the coronavirus pandemic (COVID-19) restrictions, and afterwards all titles referred to the case of COVID-19. The purpose of this study was to identify, among the most used STLF methods—linear regression (LR), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN)—the one that had the best response in atypical consumption behavior and to state the best action to be taken during atypical consumption behavior on the residential side. The original contribution of this paper regards the forecasting of loads that do not have reference historic data. As the most recent available scenario, we evaluated our forecast with respect to the database of consumption behavior altered by different COVID-19 pandemic restrictions and the cause and effect of the factors influencing residential consumption, both in urban and rural areas. To estimate and validate the results of the forecasts, multiyear hourly residential consumption databases were used. The main findings were related to the huge forecasting errors that were generated, three times higher, if the forecasting algorithm was not set up for atypical consumption. Among the forecasting algorithms deployed, the best results were generated by ANN, followed by ARIMA and LR. We concluded that the forecasting methods deployed retained their hierarchy and accuracy in forecasting error during atypical consumer behavior, similar to forecasting in normal conditions, if a trigger/alarm mechanism was in place and there was sufficient time to adapt/deploy the forecasting algorithm. All results are meant to be used as best practices during power load uncertainty and atypical consumption behavior.