Advances in Modeling and Optimization of Intelligent Power Systems Integrating Renewable Energy in the Industrial Sector: A Multi-Perspective Review

Lei Zhang, Yuxing Yuan, Su Yan, Hang Cao and Tao Du ()
Additional contact information
Lei Zhang: Key Laboratory of Eco-Industry, Ministry of Ecology and Environment, Northeastern University, Shenyang 110819, China
Yuxing Yuan: Key Laboratory of Eco-Industry, Ministry of Ecology and Environment, Northeastern University, Shenyang 110819, China
Su Yan: Key Laboratory of Eco-Industry, Ministry of Ecology and Environment, Northeastern University, Shenyang 110819, China
Hang Cao: Key Laboratory of Eco-Industry, Ministry of Ecology and Environment, Northeastern University, Shenyang 110819, China
Tao Du: Key Laboratory of Eco-Industry, Ministry of Ecology and Environment, Northeastern University, Shenyang 110819, China

Energies, 2025, vol. 18, issue 10, 1-50

Abstract: With the increasing liberalization of energy markets, the penetration of renewable clean energy sources, such as photovoltaics and wind power, has gradually increased, providing more sustainable energy solutions for energy-intensive industrial sectors or parks, such as iron and steel production. However, the issues of the intermittency and volatility of renewable energy have become increasingly evident in practical applications, and the economic performance and operational efficiency of localized microgrid systems also demand thorough consideration, posing significant challenges to the decision and management of power system operation. A smart microgrid can effectively enhance the flexibility, reliability, and resilience of the grid, through the frequent interaction of generation–grid–load. Therefore, this paper will provide a comprehensive summary of existing knowledge and a review of the research progress on the methodologies and strategies of modeling technologies for intelligent power systems integrating renewable energy in industrial production.

Keywords: smart microgrid; renewable energy; modeling techniques of prediction; modeling techniques for microgrid scheduling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/10/2465/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/10/2465/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:10:p:2465-:d:1653434

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().