The market penetration of Plug-in Electric Vehicles (PEVs) is escalating due to their energy saving and environmental benefits. In order to address PEVs impact on the electric networks, the aggregators need to accurately predict the PEV Travel Behavior (PEV-TB) since the addition of a great number of PEVs to the current distribution network poses serious challenges to the power system. Forecasting PEV-TB is critical because of the high degree of uncertainties in drivers' behavior. Existing studies mostly simplified the PEV-TB by mapping travel behavior from conventional vehicles. This could cause bias in power estimation considering the differences in PEV-TB because of charging pattern which consequently could bungle economic analysis of aggregators. In this study, to forecast PEV-TB an artificial intelligence-based method -feedforward and recurrent Artificial Neural Networks (ANN) with Levenberg Marquardt (LM) training method based on Rough structure -is developed. The method is based on historical data including arrival time, departure time and trip length. In this study, the correlation among arrival time, departure time and trip length is also considered. The forecasted PEV-TB is then compared with Monte Carlo Simulation (MCS) which is the main benchmarking method in this field. The results comparison

No abstract

An accurate Electricity Price Forecasting (EPF) plays a vital role in the deregulated energy markets and has a specific effect on optimal management of the power system. Considering all the potent factors in determining the electricity prices -some of which have stochastic nature -makes this a cumbersome task. In this paper, first, Grey Correlation Analysis (GCA) is applied to select the effective parameters in EPF problem and eliminate redundant factors based on low correlation grades. Then, a deep neural network with Stacked Denoising Auto-Encoders (SDAEs) has been utilized to denoise data sets from different sources individually. After that, to detect the main features of the input data and putting aside the unnecessary features, Dimension Reduction (DR) process is implemented. Finally, the rough structure Artificial Neural Network (ANN) has been executed to forecast the day-ahead electricity price. The proposed method is implemented on the data of Ontario, Canada, and the forecasted results are compared with different structures of ANN, Support Vector Machine (SVM), Long Short-Term Memory (LSTM) -benchmarking methods in this field-and forecasting data reported by Independent Electricity System Operator (IESO) to evaluate the efficiency of the proposed approach. Furthermore, the results of this study indicate that the proposed method is efficient in terms of reducing error criterion and improves the forecasting error about 5 to 10 percent in comparison with IESO. This is a remarkable achievement in EPF field.

The negative environmental impacts of using fossil fuel-powered vehicles underlined the need for inventing an alternative eco-friendly transportation fleet. Plug-in electrical vehicles (PEVs) are introduced to cut the continuing increase in energy use and carbon emission of the urban mobility. However, the increased demand for mobility, and therefore energy, can create constraints on the power network which can reduce the benefits of electrification as a certain and reliable source. Thus, the rise in the use of electric vehicles needs electric grids to be able to feed the increased energy demand while the current infrastructure supports it. In this paper, we introduce a methodological framework for scheduling smart PEVs charging by considering the uncertainties and battery degradation. This framework includes an economic model for charging and discharging of PEVs which has been implemented in a 21-node sample distribution network with a wind turbine as a distributed generation (DG) unit. Our proposed approach indicates that the optimal charging of the PEVs has a high impact on the distribution network operation, particularly under the high market penetration of PEVs. Thus, the smart grid to vehicle (G2V) charging mode is a potential solution to maximize the PEV’s owner profit, while considering the battery degradation cost of the PEVs. The simulation result indicates that smart charging effectuation is economical.

Hydrogen fuel cells have the potential to play a significant role in the decarbonization of the transportation sector globally and especially in California, given the strong regulatory and policy focus. Nevertheless, numerous questions arise regarding the environmental impact of the hydrogen supply chain. Hydrogen is usually delivered on trucks in gaseous form but can also be transported via pipelines as gas or via trucks in liquid form. This study is a comparative attributional life cycle analysis of three hydrogen production methods alongside truck and pipeline transportation in gaseous form. Impacts assessed include global warming potential (GWP), nitrogen oxide, volatile organic compounds, and particulate matter 2.5 (PM2.5). In terms of GWP, the truck transportation pathway is more energy and ecologically intensive than pipeline transportation, despite gaseous truck transport being more economical. A sensitivity analysis of pipeline transportation and life cycle inventories (LCI) attribution is included. Results are compared across multiple scenarios of the production and transportation pathways to discover the strongest candidates for minimizing the environmental footprint of hydrogen production and transportation. The results indicate the less ecologically intensive pathway is solar electrolysis through pipelines. For 1 percent pipeline attribution, the total CO2eq produced per consuming 1 MJ of hydrogen in a fuel cell pickup truck along this pathway is 50.29 g.