Data-Driven Approaches to Power Grids: Planning, Control and Integration of Renewable Energy Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 197
Editors
Interests: renewable energy systems; power system stability; grid integration of renewable energy; wind energy systems; smart grids; power system optimization
Special Issues, Collections and Topics in MDPI journals
Interests: power electronics; renewable energy; smart grid; embedded programming; renewable energy technology applications; microgrids smart grids; distributed generation; engineering education; posthumanism; black empowerment
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The global commitment to sustainability and mitigating climate change necessitates a fundamental transformation of traditional power grids toward systems capable of handling high penetration of variable renewable energy (VRE) sources like solar PV and wind energy conversion power, and geographically distributive. This transition introduces significant complexity, uncertainty, and non-linearity into existing electrical power networks.
This complexity provided an opportunity presented by the proliferation of data generated by IoT sensors, SCADA system, and smart meters across the electrical power system landscape. The growing volume of data enables the application of data-driven methodologies, particularly Artificial Intelligence (AI) and Machine Learning (ML). These techniques are becoming essential, offering a powerful approach to extracting reliable representations from the complex, non-linear system data, thereby identifying grid dynamics without prior knowledge of the underlying model structure.
Data-driven solutions are essential for improving system efficiency and generating additional value, especially where traditional deterministic models struggle to capture the wide range of outcomes induced by Variable Renewable Energy intermittency and uncertainty. Applications of these methods include enhancing forecasting accuracy, improving state estimation for real-time monitoring, enabling intelligent fault detection and diagnosis (FDD), and optimizing operations like Demand Response (DR) and energy storage management. By leveraging data-driven innovation, we aim to ensure the reliable, secure, and sustainable operation of next-generation smart grids (NGSGs). Contributions exploring these advanced analytics and predictive modelling techniques will offer valuable insights into navigating the complex challenges of the clean energy transition. To this end, the goal of this volume is to showcase cutting-edge data-driven approaches that address the critical functions of control, planning, and integration of renewable energy systems in these evolving power systems.
Topics of interest for publication include, but are not limited to, the following:
- Stochastic and Robust Optimization Techniques
- Advanced Forecasting for RES Planning
- Long-Term Planning and Investment Strategies
- Resilience and Contingency Planning for RES
- AI-Enabled System Identification and Dynamics Analysis
- Advanced Predictive and Adaptive Control
- AI and Machine Learning in Renewable Energy Systems
- Model-Free Identification Methodologies and Control Systems
- Fault Detection, Diagnosis (FDD), and Predictive Maintenance
- Virtual Power Plants (VPPs) and DER Aggregation
- Demand-Side Management (DSM) and Grid Flexibility
- Data Integrity and Cybersecurity
Dr. Atanda Raji
Dr. Marco Adonis
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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.
Keywords
- optimization
- AI
- machine learning
- predictive algorithms
- data-driven
- renewable energy resources
- data integrity
- cybersecurity
- smart grid
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