Recent Advances in Renewable Energy Automation and Energy Forecasting

The advancement of sustainable energy is becoming an important concern for many countries. The traditional electrical grid supports only one-way interaction of power being delivered to the consumers. The emergence of improved sensors, actuators, and automation technologies has consequently improved the control, monitoring and communication techniques within the energy sector, including the Smart Grid system. With the support of the aforementioned modern technologies, the information flows in two-ways between the consumer and supplier. This data communication helps the supplier in overcoming challenges like integration of renewable technologies, management of energy demand, load automation and control. Renewable energy (RE) is intermittent in nature and therefore difficult to predict. The accurate RE forecasting is very essential to improve the power system operations. The forecasting models are based on complex function combinations that include seasonality, fluctuation, and dynamic nonlinearity. The advanced intelligent computing algorithms for forecasting should consider the proper parameter determinations for achieving optimization. For this we need, new generation research areas like Machine learning (ML), and Artificial Intelligence (AI) to enable the efficient integration of distributed and renewable generation at large scale and at all voltage levels. The modern research in the above areas will improve the efficiency, reliability and sustainability in the
Smart grid.

The energy grid is one of the most complex infrastructures and requires quick decision- making in real-time, which big data and AI algorithms enable for utilities. Beyond grid analytics and management, AI’s applications in the renewable sector include power consumption forecasting and predictive maintenance of renewable energy sources. It further enables the internet of energy applications that predict grid capacity levels and carry out time-based autonomous trading and pricing. Future smart power systems need the intelligent field devices to help in implementation of effective control mechanisms and protection schemes. The researchers should focus on development of human and machine interaction (HMI) system based on advanced AI and ML techniques in plant control and monitoring systems. An increase in the application of advanced automation by RE based research may lead to an eventual total shift from conventional energy sources to RE.

This Research Topic aims to highlight new methods and models that can help improve the operations on RE, improves gird security and help ensure a future of energy sustainability. This Research Topic calls for novel and innovative research submissions that focus
on application-oriented studies improving RE automation and Energy forecasting models. Specific topics of interest include (but are not limited to):
- Intelligent systems, solving methods, optimization, and advanced heuristics for improving
operation of renewable integrated energy systems;
- Advanced control and operation methods to improve power system flexibility;
- Artificial intelligence and its application in RE automation;
- Modelling of Intelligent Field Devices;
- Smart energy, IoT and modern power systems modelling of RE operations;
- Energy-forecasting technologies;
- Deep learning and machine learning applications in smart grids;
- Modelling of data analytics for smart grid operations;
- Business models for different electricity market players;