Novel Information and Communication Technologies (ICT), Automation and Control for Smart Grids
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".
Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 5878
Special Issue Editors
Interests: model-based predictive control; dynamic optimization; industrial ICT; hybrid automata; energy management; systems engineering; machine learning; fuel cells; batteries; smart grids
Special Issue Information
Dear Colleagues,
The practical realization of a smart grid relies on a dependable Information and Communication Technologies (ICT) layer where robust automation and control algorithms can be efficiently implemented. With the advent of the Internet of Things (IoT), the multitude of connected devices, sensors, and equipment are going to provide large quantities of detailed data from both the consumer and the distribution side. Historical data of such size offer the opportunity to implement several machine learning techniques in order to create highly optimized and specifically tailored energy management strategies (EMS). Ideally, these EMS could be flexibly applied to islanded and/or connected networks of varying scale, from small isolated microgrids to wide area grids with renewable energy sources and energy storage systems (batteries and/or hydrogen). Furthermore, the high frequency of the collected data will make it possible to better adapt to the ever-changing conditions of the grid and to promptly detect anomalies and other rare events. In such a complex environment with different heterogeneous and asynchronous sources of data, it also becomes necessary to implement automatic methods to extract meaningful information.
The scope of this Special Issue is to present novel methodologies for grid operations which exploit the synergy of advanced ICT and data-driven analytics empowered by control theory. The articles include contributions on technology assessment, innovative technology developments, analytical models, reviews, and case studies related to:
- Automated real time decision making for grid operation;
- Diagnosis and prognosis of grid operation;
- Holistic approaches that combine both learning and decision making.
Topics of interest for publication include, but are not limited to, the following:
- Demand side management and behavior forecasting;
- Demand response;
- Anomaly detection;
- Model predictive control for energy management;
- Scheduling and dynamic optimization for prediction of operations;
- Energy management strategies;
- Digital twins for smart grid supervision;
- Visual analytics and Big data analytics;
- Networked systems.
Dr. Chrysovalantou Ziogou
Dr. Donatello Materassi
Guest Editors
Manuscript Submission Information
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Keywords
- Demand side management
- Demand response
- Anomaly detection
- Model predictive control
- Dynamic optimization
- Energy management strategies
- Machine learning
- Digital twins for smart grids
- Internet of Things
- Visual analytics
- Big data
- Networked systems
- Battery energy storage systems for smart grids
- Hydrogen technologies (electrolyzers and fuel cells) for smart grids
- Islanded smart grids and microgrids
- Renewable energy sources
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