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.

Growing penetration of Plug-in Electric Vehicles (PEVs) in the transportation fleet and their subsequent charging demands introduce substantial intermittency to the electric load profile which imposes techno-economic challenges on power distribution networks. To address the uncertainty in demand, a novel deep learning-based approach equipped with a hybrid classification task is developed which can take into account the travel characteristics of the PEV owners. The classification structure helps us scrutinize the PEVs demand by allocating a specific forecasting network to each cluster of travel behavior patterns. In our hybrid classification task, first, an unsupervised classifier discerns hidden travel-behavior patterns between the historical PEVs data by clustering them; then, a supervised classifier directs each new PEV data to its appropriate clusterspecific forecasting network. The deep learning-based forecasting and classification networks are constructed based on the Long Short-Term Memory networks to investigate long-and short term features in PEV behaviors. The data-driven structure of our proposed method enables us to observe and preserve the correlation between PEV travel data parameters (departure time, arrival time and traveled distance) and avoid the generation of unrealistic travel samples found in scenario-based approaches. To verify the effectiveness of the proposed method in practical environments, we have studied the impact of the precise forecasting of the PEVs demand in an aggregator’s financial profit in the energy market of the California Independent System Operator market. The numerical results confirm the outstanding performance of our proposed deep learning-based method in forecasting PEVs demand against benchmark approaches in this field such as Monte Carlo, Quasi-Monte Carlo, and Copula with only a 6.77% error in comparison with real data.

In this paper, a multi-stage model for expansion co-planning of transmission lines, Battery Energy Storages (BESs), and Wind Farms (WFs) is presented considering resilience against extreme weather events. In addition to High Voltage Alternating Current (HVAC) lines, Multi-Terminal Voltage Source Converter (MTVSC) based High Voltage Direct Current (HVDC) lines are planned to reduce the impact of high-risk events. To evaluate the system resilience against hurricanes, probable hurricane speed (HS) scenarios are generated using Monte Carlo Simulation (MCS). The Fragility Curve (FC) concept is utilized for calculating the failure probability of lines due to extreme hurricanes. Based on each hurricane damage, the probable scenarios are incorporated in the proposed model. Renewable Portfolio Standard (RPS) policy is modeled to integrate high penetration of WFs. To deal with the wind power and load demand uncertainties, a Chronological Time-Period Clustering (CTPC) algorithm is introduced for extracting representative hours in each planning stage. A deep learning approach based on Bi-directional Long Short-Term Memory (B-LSTM) networks is presented to forecast the yearly peak loads. The Mixed-Integer Linear Programming (MILP) formulation of the proposed model is solved using a Benders Decomposition (BD) algorithm. A modified IEEE RTS test system is used to evaluate the proposed model effectiveness.

Electric Vehicle (EV) demand modeling constitutes the cornerstone of studies aiming to facilitate the integration of EVs into the power system. The different characteristics of the EV demand (departure time, arrival time, and electric demand), as well as the correlation between thereof, render EV demand modeling a complex task. The Majority of previous methods, which were developed based on the Monte Carlo simulation, are unable to observe and preserve the correlation between EV demand characteristics; because, in these methods, the EV demand characteristics are generated separately in an unsupervised manner. This study proposes a novel semi-supervised EV demand modeling approach by mapping the different EV demand characteristics into a three-dimensional (3D) space as a 3D image. To effectively realize the 3D EV demand modeling, we have employed Generative Adversarial Networks (GANs) with a 3D convolutional structure to develop EV-GANS network-a GANs structure tailored to the needs of EV demand modeling in environments hosting high demand diversity such as EV charging stations. Numerical results confirmed the effectiveness of the proposed EV-GANS in estimating the trend of the actual EV demand on the test day with a small error margin compared to the existing benchmark generation-based methods (Monte Carlo and Copula).

In Generation Expansion Planning (GEP), the power plants lifetime is one of the most important factors which to the best knowledge of the authors, has not been investigated in the literature. In this article, the power plants lifetime effect on GEP is investigated. In addition, the deep learning-based approaches are widely used for time series forecasting. Therefore, a new version of Long short-term memory (LSTM) networks known as Bi-directional LSTM (BLSTM) networks are used in this paper to forecast annual peak load of the power system. For carbon emissions, the cost of carbon is considered as the penalty of pollution in the objective function. The proposed approach is evaluated by a test network and then applied to Iran power system as a large-scale grid. The simulations by GAMS (General Algebraic Modeling System, Washington, DC, USA) software show that due to consideration of lifetime as a constraint, the total cost of the GEP problem decreases by 5.28% and 7.9% for the test system and Iran power system, respectively.