Abstract
This paper reviews the recent studies and works dealing with probabilistic forecasting models and their applications in smart grids. According to these studies, this paper tries to introduce a roadmap towards decision-making under uncertainty in a smart grid environment. In this way, it firstly discusses the common methods employed to predict the distribution of variables. Then, it reviews how the recent literature used these forecasting methods and for which uncertain parameters they wanted to obtain distributions. Unlike the existing reviews, this paper assesses several uncertain parameters for which probabilistic forecasting models have been developed. In the next stage, this paper provides an overview related to scenario generation of uncertain parameters using their distributions and how these scenarios are adopted for optimal decision-making. In this regard, this paper discusses three types of optimization problems aiming to capture uncertainties and reviews the related papers. Finally, we propose some future applications of probabilistic forecasting based on the flexibility challenges of power systems in the near future.
Highlights
The roadmap includes probabilistic forecasting models of uncertain parameters, scenario generation based on probabilistic forecasts, and solving stochastic, robust, and chance-constrained optimization problems according to the results of the previous steps
This paper introduces probabilistic forecasting models and reviews tributions of uncertain parameters are predicted based on previous works
This roadmap started with introducing different types of probabilistic forecasting and continued with discussing for which uncertain variables the literature uses probabilistic forecasting
Summary
Smart grids’ operation and planning deal with different types of forecasts. The recent advances in information and communication technology (ICT) facilitate the real-time control of devices and resources based on the real-time system states. To the best of the authors’ knowledge, there is no comprehensive review paper proposing a comprehensive framework for smart grid decision-makers and stakeholders based on the probabilistic forecasting of all uncertain inputs (wind, PV, price, load, etc.). The roadmap includes probabilistic forecasting models of uncertain parameters, scenario generation based on probabilistic forecasts, and solving stochastic, robust, and chance-constrained optimization problems according to the results of the previous steps It tries to guide upcoming similar works by introducing the smart grid’s needs in the future. In this regard, probabilistic forecasting models should be developed and for a wide range of uncertain parameters and not be limited to loads, prices, This paper renewable generations
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