Search

This paper analyzes the feasibility of optimally utilizing Ontario's grid potential for charging plug-in hybrid electric vehicles (PHEVs) during off-peak periods. Based an a simplified zonal model of Ontario's electricity-transmission network and a zonal pattern of base-load generation capacity from 2009 to 2025, an optimization model is developed to find the optimal, as well as maximum, penetrations of PHEVs into Ontario's transport sector. The results of this paper demonstrate that the present and projected electricity grid in Ontario can be optimally exploited for charging almost 6% of the total vehicles in Ontario or 12.5% of the vehicles in Toronto's transport sector by 2025; this corresponds to approximately 500 000 PHEVs that can be charged from the grid without any additional transmission or power-generation investments beyond those currently planned. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]