Applications of Machine Learning for Renewable Energy based Modern Power Systems

Dear Colleagues,

Nowadays, along with the increased importance of distributed non-programmable renewable electricity generation and the increasing spread of distributed storage systems, intelligent apparatus are needed for the technical and economical management of power systems.

Smart grid is an approach in which user safety should be ensured while monitoring, updating, and continuously and reliably distributing electricity grid by adding smart meters and monitoring systems to the power grid, in order to ensure electronic communication between suppliers and consumers.

The smart grid structure will offer opportunities to progress in the operation of the distribution network, which is not limited to energy supply and ancillary services (e.g., reserves and demand balance) but has also to ensure quality criteria of energy and energy measurement.

The field of Machine Learning has developed from the quest for artificial intelligence. It gives computers the ability to learn statistical patterns from data without being explicitly programmed. These patterns can then be applied to new data in order to make predictions. Machine Learning also allows to automatically adapt to changes in the data without amending the underlying model. Every day, we deal dozens of times with Machine Learning applications, such as when doing a Google search, using spam filters or face detection tools, speaking to voice recognition software, or sitting in a self-driving car.

In recent years, machine learning methods have evolved in the smart grid community. This change towards analyzing data rather than modeling specific problems has led to adaptable, more generic methods that require less expert knowledge and are easier to deploy in a number of cases.

The growing number of Distributed Energy Resources (DERs) connected to the DSO grid (or, more in general, within a smart grid) require a scalable approach to control, optimize, and monitor each end point. A centralized approach that introduces a single point of failure cannot be used in this scenario where a smart grid must be reliable without affecting the overall system stability. Furthermore, the computational power related to a centralized control and optimization algorithm grows exponentially with the number of DERs that must be integrated and optimally managed within a smart grid.

To tackle the scalability and the reliability requirements, machine learning techniques are usually coupled with a multi-agent system approach and a decentralized/distributed control architecture.

Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a  monolithic system to solve and can be applied to artificial intelligence: they simplify problem-solving by dividing the necessary knowledge into subunits—to which an independent intelligent agent is associated—and by coordinating the agents' activity (distributed artificial intelligence).

This Special Issue will bring together researchers from academia and industry to share and publish novel ideas, explore inherent challenges in developing future power systems, investigate novel designs, explore enabling technologies, and share relevant experiences in machine learning methods in smart grids and their applications.

Topics for this Special Issue include, but are not limited to:

• Enabling technologies for mini- e microgrids
• Distributed generating resources in smart grids
• Concentrated and distributed storage systems in smart grids
• Smart metering, demand–response, and dynamic pricing
• Intelligent monitoring systems.
• Control and operation for smart grids
• Smart grid impact on isolation and service restoration
• Smart grid enhancement of energy management systems
• Vehicle-to-grid (V2G).
• Data Management and Grid Analytics
• Energy management systems for microgrids
• DERs coordination and aggregation
• DERs distributed optimization
• DERs modelling through machine learning
• DERs and multi-agent system
• DERs and decentralized/distributed systems
• Microgrid modelling through machine learning
• Grid services and DERs optimization
• Multi-agent systems
• Distributed artificial intelligence

Prof. Giuseppe Marco Tina
Dr. Massimiliano De Benedetti
Guest Editors

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