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With the development of new artificial intelligence and data science (DS) technologies, their applications for implementing analysis in the Internet of Things (IoT) energy systems are becoming more important for smart grids (SGs). DS approaches integrated with IoT data collection protocols in energy sectors can help in improving efficiency in such areas. It will give insight to administrators to manage the system in a data-driven way. Data gathering by sensors is an important step in IoT-based energy grids when it comes to a large amount of data. In the real-time data collection process, the frequency of data rises to become big data and the analysis needs new methods to manage and evaluate this data. The outcome of these analytics comes up as SG intelligence so the system becomes smart, which is depicted as demographics, figures, and informative dashboards. In this chapter, all these kinds of tools and analytics are discussed with attention to data-driven decision-making in smart energy systems.

Over the past few decades, with the increasing influence of renewable energy sources, power systems have undergone a tremendous transformation, and the management of such systems has become increasingly difficult due to the intermittent nature of their production capacity. The increase in the influence of renewable energy sources has changed the overall behavior of the market. In the off-peak hours, renewable energy sources reduce the price of energy due to their unpredictability. It is necessary to use energy storage systems to add alternating sources to the network and transfer the energy produced by these sources from nonpeak hours to peak hours. Despite the aforementioned, today, due to the uncertainties in energy prices, the issue of profitability of energy storage systems for its owners is debatable. This chapter focuses on the optimal planning of energy storage systems with WT, PV, and dispatchable distributed generation, to participate in the energy market and increase the profitability of its related resources. In this chapter, a model is presented for optimal planning of energy storage systems along with related resources to participate in the energy market and increase their profits. The impact of energy price uncertainty on the profitability of energy storage systems and related resources has also been investigated. Also, the proposed model has been implemented on the IEEE 33-bus network using the Teaching–learning-based optimization algorithm, and the simulation results have been analyzed to evaluate the proposed model.