AI-driven approaches for optimizing the energy efficiency of integrated energy system

To decarbonize the global energy system and replace the unidirectional architecture of existing grid networks, integrated and electrified energy systems are becoming more demanding. Energy integration is critical for renewable energy sources like wind, solar, and hydropower. However, there are still specific challenges to overcome, such as their high reliance on the weather and the complexity of their integrated operation. As a result, this research goes through the study of a new approach to energy service that has arisen in the shape of data-driven AI technologies, which hold tremendous promise for system improvement while maximizing energy efficiency and reducing carbon emissions. This research aims to evaluate the use of data-driven AI techniques in electrical integrated energy systems, focusing on energy integration, operation, and planning of multiple energy supplies and demand. Based on the formation point, the main research question is: "To what extent do AI algorithms contribute to attaining greater efficiency of integrated grid systems?". It also included a discussion on four key research areas of AI application: Energy and load prediction, fault prediction, AI-based technologies IoT used for smart monitoring grid system optimization such as energy storage, demand response, grid flexibility, and Business value creation. The study adopted a two-way approach that includes empirical research on energy industry expert interviews and a Likert scale survey among energy sector representatives from Finland, Norway, and Nepal. On the other hand, the theoretical part was from current energy industry optimization models and a review of publications linked to a given research issue. The research's key findings were AI's significant potential in electrically integrated energy systems, which concluded AI's implication as a better understanding of energy consumption patterns, highly effective and precise energy load and fault prediction, automated energy management, enhanced energy storage system, more excellent business value, a smart control center, smooth monitoring, tracking, and communication of energy networks. In addition, further research directions are prospects towards its technical characteristics on energy conversion

PARAS-2 precision radial velocimeter: optical and mechanical design of a fiber-fed high resolution spectrograph under vacuum and temperature control

We present here the optical and mechanical design of a fiber-fed High-resolution spectrograph at resolution (R) = 100,000 which will be under vacuum (0.001 to 0.005 mbar) and temperature controlled environment at 25C ± 0.001C. The spectrograph will be attached to our upcoming new PRL 2.5m aperture telescope at Gurushikar, Mount Abu, Rajasthan, India. The spectrograph is named PARAS-2 after the successful operation of PARAS (PARAS-1) with our existing 1.2m aperture telescope at Gurushikar, Mount Abu since 2012 summer. The spectrograph (PARAS-2) will be operating in the range of 380nm to 690nm wavelength in a single shot using Grism as a Cross Disperser, R4 Echelle at blaze angle of 76degrees, and pupil diameter of 200 mm. We will use a combination of octagonal and circular fibers along with double scrambler and simultaneous calibration for getting down to the RV precision of 50cm/s or better (< 50cm/s). Minimum 30% time will be reserved for exoplanet work with the spectrograph on the 2.5m telescope when it becomes operational in early 2020. The overall efficiency of the whole spectrograph (Echelle, M1, M2, FM, Grism, Camera lens system, Dewar window) excluding fiber is expected to be 22.5% - 28% and 4% - 8% including optical fiber, telescope and fibertelescope interface losses