Applications of Probabilistic Forecasting in Smart Grids: A Review

Author:

Khajeh HosnaORCID,Laaksonen HannuORCID

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.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Optimal Energy Management System Using Probabilistic Day-ahead Forecasting;2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE);2023-10-23

2. Forecasting Methods for Photovoltaic Energy in the Scenario of Battery Energy Storage Systems: A Comprehensive Review;Energies;2023-09-15

3. Determination of Stochastic Load Flow by Applying Probabilistic Collocation Method Itegrated Wind Generation;2023 IEEE 3rd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA);2023-05-21

4. Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities;Computer Modeling in Engineering & Sciences;2023

5. A Review on Machine Learning Models in Forecasting of Virtual Power Plant Uncertainties;Archives of Computational Methods in Engineering;2022-11-30

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