Application of Computational Intelligence and Machine Learning Approaches in Photovoltaic-Rich Distribution Networks

Dear Colleagues

In recent years, increasing numbers of photovoltaics have been installed on distribution grids worldwide, and are leading to changes in the way traditional distribution grids are operated. Many new challenges are arising, such as reverse power flows, incresaes in voltage, power quality issues (increase harmonics),  as well as protection coordination. The quantity of energy produced by photovoltaics depends on stochastic solar irradiation, where sudden and unpredictable changes often occur; these cause difficulties in grid planning and operation. Scientists are currently endevouring to address and solve the above-mentioned challenges. Many traditionally employed tools and methods have been utilized, and some novel techniques have been developed.

Computational intelligence (CI) and machine learning (ML) approaches have attracted significant attention from researchers in many scientific fields, and modern distribution networks with photovoltaics are no exception. There are many potential applications of computational intelligence and machine learning in photovoltaic-rich distribution networks, and this Special Issue aims to address some of them and gather relevant scientific papers (research, as well as review) that attend to the application of CI and ML in modern distribution networks.

Topics of interest for publication include, but are not limited to, the following:

• Application of CI and ML in photovoltaic production forecasting;
• Load forecasting in modern distribution networks using CI and ML approaches;
• Application of CI and ML to power quality assessment in PV-rich distribution networks;
• Optimization of modern distribution network with photovoltaics using AI and ML approaches;
• Network reconfiguration, loss minimization and voltage optimization in modern distribution networks;
• Protection coordination of distribution networks with photovoltaics;
• Islanding detection methods based on CI and ML;
• Inverter control and optimization using CI and ML.

Dr. Krešimir Fekete
Prof. Dr. Danijel Topić
Dr. Zvonimir Klaić
Guest Editors

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• computational intelligence
• machine learning
• photovoltaics
• distribution network
• forecasting
• optimization
• islanding detection