Smart and Predictive Control for Power Distribution Grids with Prolific Distributed Generation: A Step toward the Smart Grid Paradigm
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".
Deadline for manuscript submissions: closed (10 February 2022) | Viewed by 8501
Special Issue Editors
Interests: control engineering; machine learning; energy-related systems and technologies
Interests: solar resource assessment and forecasting; distributed generation management; smart buildings; smart grids; thermal/electrical microgrids; machine/deep learning; reinforcement learning; model-based predictive control; non-linear optimization
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, large-scale deployment across the world of renewable-energy-based power generation sources, referred to as distributed generation, has been well under way. This is thanks to their potential as a promising alternative to fossil fuels for sustainable and eco-friendly power grids. Because power grids were originally designed for centralized generation with unidirectional power flow, large-scale deployment of distributed generation brings with it numerous operational issues. There is no shortage of works in the scientific literature tackling the different aspects of the power grid with prolific distributed generation transition into a “smart grid”.
Thus, the topic of this Special Issue covers all aspects of converting the power distribution grid into a smart grid using smart and predictive control tools. This includes the development of predictive controllers but also smart controllers which are able to improve the capabilities of the power distribution grid, using flexible assets such as electrical or non-electrical storage systems, to balance production and consumption, to suppress voltage or current constraints, and to improve robustness. The works presented in this issue should take into consideration the complexity of the control tools developed to ensure their ability to be implemented in real time. Works based on real data sets and experiments will be greatly appreciated. Because the prediction of stochastic quantities such as grid load and power generation, for example, using machine learning tools, is required for the implementation of predictive strategies, works on this topic are also welcome.
Submit your paper and select the Journal “Energies” and the Special Issue “Smart and Predictive Control for Power Distribution Grids with Prolific Distributed Generation: A Step toward the Smart Grid Paradigm” via: MDPI submission system. Please contact the special issue editor ([email protected]) for any queries. Our papers will be published on a rolling basis and we will be pleased to receive your submission once you have finished it.
Prof. Dr. Julien Eynard
Prof. Dr. Stéphane Grieu
Guest Editors
Manuscript Submission Information
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Keywords
- Smart-grid paradigm
- Microgrids
- Model-based predictive control
- Smart control
- Power distribution grids
- Distributed generation
- Time-series forecasting
- Machine learning
- Complexity analysis for real-time implementation
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