Uncertainty modeling of renewable energy sources, load demand, electricity price, etc. create a high volume of data in smart grids. Accordingly, in this article, a precise forecasting method based on a deep learning concept with microclustering (MC) task is presented. The MC method is structured based on hybrid unsupervised and supervised clustering tasks by $K$ -means and Gaussian support vector machine, respectively. In the proposed method, the input data sequence is clustered by the MC task, and then the forecasting process is employed. By applying the MC, input data in each hour are categorized into different groups, and a distinctive forecasting unit is allocated to each one. In this way, more clusters and forecasting networks are earmarked for the hours with higher fluctuation rates. The bi-directional long short-term memory (B-LSTM), which is one of the newest recurrent artificial neural networks, is proposed as the forecasting unit. The B-LSTM has bidirectional memory—feedforward and feedback loops—that helps us to investigate both previous and future hidden layers data. The optimal number of clusters in each hour is determined based on the Davies–Bouldin index. To evaluate the performance of the proposed method, in this study, three forecasting tasks including the wind speed, load demand, and electricity price are studied in different periods using the Ontario province, Canada, data set. The results are compared with other benchmarking methods to verify the robustness and effectiveness of the proposed method. In fact, the proposed method, which is equipped with the MC technique and B-LSTM networks, significantly promotes the forecasting results, especially in spike points.

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.

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 (PM_2.5). 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 CO_2eq produced per consuming 1 MJ of hydrogen in a fuel cell pickup truck along this pathway is 50.29 g.

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.