# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 8 Conclusions and Future Direction  
This paper discussed the roadmap towards making decisions under uncertainty in a smart grid environment. This roadmap started with introducing different types of probabilistic forecasting and continued with discussing for which uncertain variables the literature uses probabilistic forecasting. In this regard, the main focus of the literature was on obtaining the probabilistic forecasting models for renewable generation (both wind and PV generation), electricity loads, and electricity prices. Afterward, the paper described how probabilistic forecasting models were applied in the literature. For this purpose, it reviewed some papers adopting scenario generation techniques in smart grids. Two important methods of scenario generation, i.e., Monte Carlo simulation and generative adversarial Network, were introduced, and the paper explained in what way they are related to decision making. In addition, a limited number of papers utilizing introduced scenario generation methods in smart grids have been reviewed. In the next step, decision-making under uncertainty was discussed. It was stated that energy management systems of smart grids need to solve optimization problems in order to operate and plan their resources. In fact, uncertainties should be taken into account in the optimization problem. Accordingly, stochastic, robust, and chance-constrained programming that consider uncertainties in the optimization problem were briefly introduced. It was discussed how these problems can be used in smart grid decision making by reviewing the most recent papers. Furthermore, two more applications of probabilistic forecasting were reviewed. Although there exist a wide range of uncertain parameters in smart grids, the probabilistic forecasting models were only developed for a limited number of these variables. For example, there are very few papers that proposed to develop probabilistic forecasting models for uncertain parameters of BESS and EVs (such as their SOC, charging power, etc.). However, decision-makers need the distributions of these variables along with those of loads and renewable output. Thus, future studies need to be conducted to obtain and develop distributions of EV's and BESS's parameters. Finally, it should be mentioned that future power systems are heading toward hosting a high share of renewable generation. However, at the moment, power grids are not flexible enough to tolerate this situation and need more flexibility from different flexible energy resources and flexibility solutions (e.g., related to active network management). As a result, operators need to know:  
* What are the available flexible energy resources in the current system? * What are the potential flexible energy resources that should be activated? * How much flexibility is needed for the future power system? To answer these questions, the system operators need to forecast:
* The flexibility required to deal with a high share of renewables in the future such as the reserves
* The available flexibility (related to active power \(P\) and reactive power \(Q\)) of the system at different levels of the systems (flexibility at TSO, DSO, and customer levels)
* Potential congestions (voltage and/or thermal limit violations) of lines and other passive power system key components  
Point forecasts of flexibility do not give operators a comprehensive insight in order to deal with the uncertainties of the future. However, with the help of probabilistic forecasts, decision-makers can assess different operational and planning decisions considering different renewable injection scenarios. Conceptualization, investigation, visualization: H.K. ; writing--original draft preparation: H.K. and H.L. ; writing--review and editing: H.K. and H.L. ; supervision: H.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The work of [PERSON] was supported by the Ella and Georg Ehrnrooth Foundation. The authors declare no conflict of interest.