Integrated Maximum Power Extraction for Multiple Renewable Energy Sources
To cope with the global demand, renewable energy is gaining more and more popularity owing to its cleaner production and ample supply. The use of renewable energy sources (RESs) for residential and commercial applications is possible both as a stand-alone microgrid system and as a system that is incorporated into the grid. However, due to the intermittent nature of RESs and the need to harvest clean power with the highest possible efficiency, integrating RESs with the grid presents a number of challenges. As a result, maximum power extraction (MPE) has attracted a lot of attention. This task gets more complicated in the case of numerous RESs, especially when the PVs are coupled in a multi-string topology and receive irregular illumination. In this project, we develop integrated MPE control techniques to reduce the implementation cost for the hybrid FC and multi-string PV systems while optimizing the extracted power from all the RESs.
Intelligent Next-Generation Sustainable Energy Systems
The design procedure of compact and efficient on-chip nano-photonics, which are integral parts of energy conversion systems, are accelerated and are aided computationally by the inclusion of machine/ deep-learning data-driven approaches. More recently, these methods have gained attention in this domain primarily due to their ability to make the design optimization process faster. These methods mimic the way humans gain certain types of knowledge, have risen to the forefront in many fields of research where there is a significant amount of data to be processed, and make the overall process quicker, more accurate, and bias-free. The greater accuracy associated with these methods stems from making a holistic analysis by including all possible variables in the design process. Additionally, while humans tend to lean towards certain outcomes for a particular design problem, a deep-learning routine is virtually free of any such tendencies and thus yields bias-free results. Similarly, machine-learning-based regression models have lent their hands to swift design procedures.
Sustainable buildings are key aspects of the smart cities of the future; they are realized through the use of windows to generate solar energy. A transparent/organic photovoltaic cell (basic architecture shown in front) can be made from materials that partially allow the passage of visible light and absorb ultraviolet and infrared light. The most exciting feature of transparent solar cells is their integration with windows, skylights, or even smartphone and tablet screens. However, the working efficiency of transparent solar cells varies depending on the materials and manufacturing methods used. Recently, these cells have achieved an efficiency of only around 5%, significantly lower than traditional solar cells (around 20%). ITL is developing novel and efficient transparent photovoltaics using new materials and manufacturing methods.
Intelligent Optimization Strategies for Smart Energy Systems