Intermittent and stochastic character of renewable energy sources: Consequences, cost of intermittence and benefit of forecasting

By G. Notton, Marie-Laure Nivet, Cyril Voyant, Christophe Paoli, Christophe Darras, F. Motte, A. Fouilloy. 2018

489 citations recorded by the source corpus.

Abstract Solar and wind energy are inherently time-varying sources of energy on scales from minutes to seasons. Thus, the incorporation of such intermittent and stochastic renewable energy systems (ISRES) into an electricity grid provides some new challenges in managing a stable and safe energy supply, in using energy storage and/or 'back-up' energy from other sources. In such cases, the ability to accurately forecast the output of “unpredictable” energy facilities is essential for ensuring an optimal management of the energy production means. This review syntheses the reasons to predict solar or wind fluctuations, it shows that variability and stochastic variation of renewable sources have a cost, sometimes high. It provides useful information on the intermittence cost and on the decreasing of this cost due to an efficient forecasting of the source fluctuation; this paper is for engineers and researchers who are not necessarily familiar with the issue of the notions of cost and economy and justify future investments in the ISRES production forecasting.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/94bdb89f0c9166c57d0e2fc52e5a6d14e85bf117

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. C. D. de Silva
2. Min Xia
3. Xiandong Ma
4. Haidong Shao

In Electrical and Electronic Engineering

1. C. D. de Silva
2. Min Xia
3. Xiandong Ma
4. Haidong Shao

Citations in the corpus

• A Stacked GRU-RNN-Based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation (2021, background): “On the one side, the prediction of energy production faces new challenges due to the large-scale integration of renewable energy (RE) sources, such as solar and wind energy [3].”