Model Predictive Control in Industrial Power Systems
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".
Deadline for manuscript submissions: 5 March 2025 | Viewed by 246
Special Issue Editors
Interests: multiphase flow; population balance modelling; machine Learning; Digital Twin; particle dynamics; high performance computing
Special Issues, Collections and Topics in MDPI journals
Interests: multiphase flow; fuel cell; deep neural networks; energy storage
Interests: advanced combustion of low-carbon fuel; advanced power system of green energy; digital twin and machine learning in traffic industry
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue entitled "Model Predictive Control in Industrial Power Systems" focuses on the development and application of model predictive control (MPC) techniques in optimizing the operation and performance of industrial systems. This Special Issue explores the MPC algorithms to address challenges such as balancing supply and demand, managing grid stability, and integrating renewable energy sources or energy storage technologies into industrial power networks. By incorporating predictive models and optimization algorithms, MPC offers a flexible and efficient approach to control the complex dynamics of industrial power systems. The articles in this Special Issue highlight the latest research and innovations in MPC for industrial power systems, showcasing its potential to enhance system efficiency, reliability, and sustainability. Researchers in the related research field will gain valuable insights and practical solutions for improving the operation and management of industrial power networks or other industrial systems by applying MPC techniques.
Dr. Shaohua Wu
Dr. Hongpeng Xu
Prof. Dr. Feiyang Zhao
Guest Editors
Manuscript Submission Information
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Keywords
- model predictive control
- energy storage
- power system
- machine learning
- optimization
- smart grid
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