The penetration of renewable distributed generation (DG) sources has been increased in active distribution networks due to their unique advantages. However, non-dispatchable DGs such as wind turbines raise the risk of distribution networks. Such a problem could be eliminated using the proper application of energy storage units. In this paper, optimal planning of batteries in the distribution grid is presented. The optimal planning determines the location, capacity and power rating of batteries while minimizing the cost objective function subject to technical constraints. The optimal long-term planning is based on the short-term optimal power flow considering the uncertainties. The point estimate method (PEM) is employed for probabilistic optimal power flow. The batteries are scheduled optimally for several purposes to maximize the benefits. A hybrid Tabu search/particle swarm optimization (TS/PSO) algorithm is used to solve the problem. The numerical studies on a 21-node distribution system show the advantages of the proposed methodology. The proposed approach can also be applied to the realistic sized networks when some sensitive nodes are considered as candidate locations for installing the storage units.

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.