Hybrid Forecast and Control Chain for Operation of Flexibility Assets in Micro-Grids

Abstract

Studies on forecasting and optimal exploitation of renewable resources (especially within microgrids) were already introduced in the past. However, in several research papers, the constraints regarding integration within real applications were relaxed, i.e., this kind of research provides impractical solutions, although they are very complex. In this paper, the computational components (such as photovoltaic and load forecasting, and resource scheduling and optimization) are brought together into a practical implementation, introducing an automated system through a chain of independent services aiming to allow forecasting, optimization, and control. Encountered challenges may provide a valuable indication to make ground with this design, especially in cases for which the trade-off between sophistication and available resources should be rather considered. The research work was conducted to identify the requirements for controlling a set of flexibility assets—namely, electrochemical battery storage system and electric car charging station—for a semicommercial use-case by minimizing the operational energy costs for the microgrid considering static and dynamic parameters of the assets.

Design and Implementation of an Interoperable Architecture for Integrating Building Legacy Systems into Scalable Energy Management Systems

“…In cases of installed renewable energy systems (such as photovoltaics) the predicted accuracy of electricity generation is of great importance for increasing self-consumption and optimizing energy use. Thus, the application of methodologies that can enhance the forecasting of renewable energy production using ML techniques, such as the "Hybrid Approach" [43], is quite important.…”