Modern Machine Learning Applications in Control and Optimization of Energy Power and Storage Systems

Dear Colleagues,

As the global energy landscape continues to evolve, with increasing demands and a growing emphasis on sustainable, renewable, and efficient energy solutions, the need for advanced control and optimization methods becomes increasingly critical in energy power and storage systems. This Special Issue is dedicated to exploring the latest advancements in the application of modern machine learning (ML) techniques for the control and optimization of energy power and storage systems. The advanced ML models, such as deep learning (DL), transfer learning (TL), generative adversarial learning (GAN), ensemble learning (EL), and reinforcement learning (RL), have provided a promising avenue for addressing the complex challenges inherent in energy power and storage systems. These models can possess exceptional capabilities in handling intricate, nonlinear relationships and large datasets, making them an attractive solution for enhancing the efficiency, reliability, and sustainability of power grids, renewable energy integration, energy storage management system, and more.

 This Special Issue, titled “Modern Machine Learning Applications in Control and Optimization of Energy Power and Storage Systems”, seeks high-quality research contributions that focus on the application of modern ML models to the control and optimization of energy power and storage systems. This curated collection of pioneering research aims to deliver valuable insights and innovative solutions, thereby shaping the future of energy power and storage systems through the lens of modern ML techniques.

Topics include, but are not limited to, the following:

• Modern ML models for grid management and control;
• Forecasting renewable energy generation (solar, wind, etc.) using ML;
• ML for optimization of renewable energy integration;
• Use of DL and RL in power system optimization;
• Energy consumption pattern analysis and prediction;
• ML-based detection and prevention of cyber-attacks;
• DL for power system state estimation;
• DL for distribution network reconfiguration;
• ML for power big data anomaly detection;
• ML for energy storage management system optimization and control.

Dr. Wentao Ma
Dr. Tengpeng Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• machine learning models
• energy power system
• energy storage management system
• grid management and control
• renewable energy integration
• energy consumption prediction
• power system state estimation
• cybersecurity in energy systems
• sustainable energy solutions