Advanced Forecasting Methods for Sustainable Power Grid: 2nd Edition

Dear Colleagues,

Many smart power grid systems currently deployed include various renewable energy sources, such as photovoltaic and wind energy. These renewable energy sources could have considerable impacts on power grid systems from both technical and environmental perspectives. The generated renewable energy can cause the discontinuity of energy production due to the non-programmable and unpredictable nature of renewable sources. The output of plants powered by non-programmable renewable energy sources (NPRESs) significantly changes the hourly pattern of zonal loads that need to be fulfilled by conventional generation plants. Thus, NPRESs introduce a stochastic component into the electricity demand related to the inherent variability of weather conditions, making the residual electricity load increasingly intermittent and harder to predict. As such, the rising implementation of NPRES plants results in both increasing imbalances between demand and generation and greater difficulty in building up the reserve margins needed to manage the randomness of the load while providing security and stability to the grid. For this reason, more effort has been made by the research community to establish accurate forecasting systems. This Special Issue aims to present advanced forecasting methods with applications that cover various practical challenges in sustainable power grids.

Topics to be covered in this Special Issue include, but are not limited to, the following:

• Forecasting PV and wind power generation;
• Energy demand forecasting;
• Forecast models for wind speed and solar radiations;
• Forecast models for grid connected MPPT;
• Electric vehicle load forecasting;
• Electricity price forecasting;
• Forecasting techniques for smart grids;
• Artificial intelligence and data-driven approaches;
• The application of forecasting techniques in power systems;
• Anomalies and faults prediction.

Dr. Cristina Ventura
Dr. Santi Agatino Rizzo
Guest Editors

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