# Supporting Future Electrical Utilities: Using Deep Learning Methods in EMS and DMS Algorithms

## TL;DR

This paper reviews recent deep learning techniques for power system monitoring and optimization, addressing the challenges of increasing system complexity and renewable integration by enabling near real-time algorithms with lower computational demands.

## Contribution

It provides a comprehensive review of deep learning applications in EMS and DMS, highlighting potential improvements for future electrical utility operations.

## Key findings

- Deep learning enhances real-time power system monitoring.
- Deep learning improves optimization in energy management.
- Potential for re-implementing traditional algorithms with deep learning.

## Abstract

Electrical power systems are increasing in size, complexity, as well as dynamics due to the growing integration of renewable energy resources, which have sporadic power generation. This necessitates the development of near real-time power system algorithms, demanding lower computational complexity regarding the power system size. Considering the growing trend in the collection of historical measurement data and recent advances in the rapidly developing deep learning field, the main goal of this paper is to provide a review of recent deep learning-based power system monitoring and optimization algorithms. Electrical utilities can benefit from this review by re-implementing or enhancing the algorithms traditionally used in energy management systems (EMS) and distribution management systems (DMS).