Forecasting and Optimization Methods for High Renewable Penetration Power Systems
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".
Deadline for manuscript submissions: 22 July 2026 | Viewed by 5
Special Issue Editors
Interests: electricity market; machine learning; distribution network operation; demand response
Interests: distribution network economic operation; mobile energy storage
Special Issue Information
Dear Colleagues,
With the increasing integration of renewable energy sources such as wind and solar, along with distributed generation, energy storage, and flexible loads, traditional power systems are facing new challenges. The high penetration of renewable energy leads to increased uncertainties, fluctuations, and complex operational constraints, making advanced forecasting and optimization technologies essential.
This Special Issue, entitled “Forecasting and Optimization Methods for High Renewable Penetration Power Systems”, will focus on new theories, models, algorithms, and practical applications for the forecasting, optimization, planning, operation, and control of systems with high levels of renewable energy integration. Our goal is to explore how advanced forecasting and optimization methods can support efficient, reliable, and low-carbon operation in these evolving power systems.
We invite contributions that present innovative solutions and real-world case studies and discuss challenges such as scalability, data quality, and operational reliability. This Special Issue will provide a platform for both academic and industry researchers to share insights into improving the operation of high-penetration renewable energy systems.
Dr. Qiangang Jia
Dr. Zhuoxin Lu
Dr. Peng Zhuang
Guest Editors
Manuscript Submission Information
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Keywords
- forecasting
- optimization
- renewable energy systems
- energy storage
- demand response
- mathematical programming
- stochastic optimization
- low-carbon operation
- real-time control
- grid stability
- energy management
- system resilience
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