Application of information gap decision theory in practical energy problems: A comprehensive review

TL;DR

This paper reviews how information gap decision theory is applied to model uncertainty in energy systems, addressing challenges like renewable integration, market variability, and energy consumption forecasting.

Contribution

It provides a comprehensive review of existing research on using information gap decision theory for uncertainty modeling in energy and power systems.

Findings

Highlights various approaches using information gap decision theory

Summarizes applications in renewable energy integration

Identifies gaps and future directions in uncertainty modeling

Abstract

The uncertainty quantification and risk modeling are hot topics in the operation and planning of energy systems. The system operators and planners are decision-makers that need to handle the uncertainty of input data of their models. As an example, energy consumption has always been a critical problem for operators since the forecasted values, and the actual consumption is never expected to be the same. The penetration of renewable energy resources is continuously increasing in recent and upcoming years. These technologies are not dispatch-able and are highly dependent on natural resources. This would make real-time energy balancing more complicated. Another source of uncertainty is related to energy market prices which are determined by the market participants behaviors. To consider these issues, uncertainty modeling should be performed. Various approaches have been previously utilized to model the uncertainty of these parameters such as probabilistic approaches, possibilistic approaches, hybrid possibilistic-probabilistic approach, information gap decision theory, robust and interval optimization techniques. This paper reviews the research works that used information gap decision theory for uncertainty modeling in energy and power systems.