Artificial intelligence techniques for enabling Big Data services in distribution networks: A review

• Published in: Renewable & sustainable energy reviews 2021-10, Vol.150, p.111459, Article 111459
• Main Authors: Barja-Martinez, Sara, Aragüés-Peñalba, Mònica, Munné-Collado, Íngrid, Lloret-Gallego, Pau, Bullich-Massagué, Eduard, Villafafila-Robles, Roberto
• Format: Article
• Language: English
• Subjects: Deep learning; Distribution grid; Machine learning; Smart energy service; Smart grid
• ISSN: 1364-0321; 1879-0690
• DOI: 10.1016/j.rser.2021.111459

Artificial intelligence techniques lead to data-driven energy services in distribution power systems by extracting value from the data generated by the deployed metering and sensing devices. This paper performs a holistic analysis of artificial intelligence applications to distribution networks, ranging from operation, monitoring and maintenance to planning. The potential artificial intelligence techniques for power system applications and needed data sources are identified and classified. The following data-driven services for distribution networks are analyzed: topology estimation, observability, fraud detection, predictive maintenance, non-technical losses detection, forecasting, energy management systems, aggregated flexibility services and trading. A review of the artificial intelligence methods implemented in each of these services is conducted. Their interdependencies are mapped, proving that multiple services can be offered as a single clustered service to different stakeholders. Furthermore, the dependencies between the AI techniques with each energy service are identified. In recent years there has been a significant rise of deep learning applications for time series prediction tasks. Another finding is that unsupervised learning methods are mainly being applied to customer segmentation, buildings efficiency clustering and consumption profile grouping for non-technical losses detection. Reinforcement learning is being widely applied to energy management systems design, although more testing in real environments is needed. Distribution network sensorization should be enhanced and increased in order to obtain larger amounts of valuable data, enabling better service outcomes. Finally, the future opportunities and challenges for applying artificial intelligence in distribution grids are discussed. •Identification of main data-driven techniques and data sources in power systems.•Proposal and review of data-driven energy services and how they interrelate.•The dependencies among AI techniques and data-driven energy services are mapped.•Opportunities and challenges regarding the distribution grid digitalization.