Review of Artificial Intelligence Applications in the Digital Energy and Renewable Energy Infrastructures

The increasing use of integrated renewable energy sources (RESs) is undoubtedly reshaping the structure of power systems. In such conditions, achieving energy efficiency and sustainability requires the development and integration of digital solutions to manage energy flows and assets optimization. This paper aims to provide a comprehensive review of the successful integration of artificial intelligence (AI) in the energy sector, particularly in relation to the high penetration of renewable energy. The paper presents trends and potential scenarios in the digitalization of energy, along with the associated challenges. It analyzes particular applications of AI tools in strategic areas of the energy sector. Five key areas of the energy sector are identified where AI tools are applied: forecasting electricity generation from RES; forecasting demand and price fluctuations on the electricity spot market; the real-time management of energy flows and assets in active microgrids; and data processing and analyzing, and general industrial direction. The article also attempts to summarize the current status, goals, key areas, and activities in the irreversible transformation of power structures into digital intelligent ones. This digital transformation is a gradual process with consecutive steps. To improve understanding and clarity, the authors present a three-phase roadmap of AI adoption. To develop an adequate AI integration strategy, it is necessary to understand the technologies, algorithms, hierarchical structure, and connections within this structure. Accordingly, the article presents a taxonomy of the hierarchical structure of AI. The subsequent step involves the sequential construction of a digitalization model. Here, the authors consider it necessary to present a 4-layer structure model of AI energy democracy. Finally, through a comparative analysis of different types of intelligent applications for energy problem solving, guidelines are provided for successful decision making in compliance with the specified harmonized standards and protocols.

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Publication Details

Journal
Energies
Published
2026-03-02
DOI
https://doi.org/10.3390/en19051250
Primary Topic
Energy Load and Power Forecasting
Type
article
Field-Weighted Citation Impact
0.00

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article

Review of Artificial Intelligence Applications in the Digital Energy and Renewable Energy Infrastructures

Ralena Kutkarska, Dimitrina Koeva, Plamen Tsankov, Vladimir Zinoviev
Energies
Energy Load and Power Forecasting
article

Review of Artificial Intelligence Applications in the Digital Energy and Renewable Energy Infrastructures

Ralena Kutkarska, Dimitrina Koeva, Plamen Tsankov, Vladimir Zinoviev
article en

Abstract

The increasing use of integrated renewable energy sources (RESs) is undoubtedly reshaping the structure of power systems. In such conditions, achieving energy efficiency and sustainability requires the development and integration of digital solutions to manage energy flows and assets optimization. This paper aims to provide a comprehensive review of the successful integration of artificial intelligence (AI) in the energy sector, particularly in relation to the high penetration of renewable energy. The paper presents trends and potential scenarios in the digitalization of energy, along with the associated challenges. It analyzes particular applications of AI tools in strategic areas of the energy sector. Five key areas of the energy sector are identified where AI tools are applied: forecasting electricity generation from RES; forecasting demand and price fluctuations on the electricity spot market; the real-time management of energy flows and assets in active microgrids; and data processing and analyzing, and general industrial direction. The article also attempts to summarize the current status, goals, key areas, and activities in the irreversible transformation of power structures into digital intelligent ones. This digital transformation is a gradual process with consecutive steps. To improve understanding and clarity, the authors present a three-phase roadmap of AI adoption. To develop an adequate AI integration strategy, it is necessary to understand the technologies, algorithms, hierarchical structure, and connections within this structure. Accordingly, the article presents a taxonomy of the hierarchical structure of AI. The subsequent step involves the sequential construction of a digitalization model. Here, the authors consider it necessary to present a 4-layer structure model of AI energy democracy. Finally, through a comparative analysis of different types of intelligent applications for energy problem solving, guidelines are provided for successful decision making in compliance with the specified harmonized standards and protocols.

EnergiesVol. 19(5)
University of National and World Economy (BG), Technical University of Gabrovo (BG)
European Regional Development Fund
Openalex Percentile: Top 39%
Energy Load and Power Forecasting
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