Energy systems digitalisation represents the energy sector's future, and Digital Twins represent the most advanced and complete way to monitor and optimally manage a complex system such as the upcoming solutions. Those latter will comprehend several energy generators, traditional and/or from renewable energy sources (RESs), different energy storage systems using several energy vectors and that interconnect different energy-consuming sectors (power, thermal, transport sectors) and that fully exploit the potential synergies offered by such interconnected system. Nevertheless, since the first conceptualisation of digital twins in the first years of the 21st century, its use has not started yet for different reasons that are affecting the adoption of this game-changer approach. Hence, what are the main barriers that are holding back the adoption of digital twins in smart energy systems? The present review paper answers this research question while discussing the case studies that can be found in literature and analysing the different approaches and the system architectures that have been tested or simply idealised. This paper provides a basis for future research that aims at applying the digital twin concept in the energy sector and particularly for power grid management. It deals with the challenges of big data management, the ones related to real-time measurements and continuous communication between the real-world system and its digital twin, the investment for measuring systems, the issues connected with the use of large data centres and the correlated energy-related challenges and doubts. The review analyses the challenges that have been encountered so far, the proposed solutions and the opportunities that such a 'work in progress' topic offers.
Hourly energy consumption profiles are of primary interest for measures to apply to the dynamics of the energy system. Indeed, during the planning phase, the required data availability and their quality is essential for a successful scenarios’ projection. As a matter of fact, the resolution of available data is not the requested one, especially in the field of their hourly distribution when the objective function is the production-demand matching for effective renewables integration. To fill this gap, there are several data analysis techniques but most of them require strong statistical skills and proper size of the original database. Referring to the built environment data, the monthly energy bills are the most common and easy to find source of data. This is why the authors in this paper propose, test and validate an expeditious mathematical method to extract the building energy demand on an hourly basis. A benchmark hourly profile is considered for a specific type of building, in this case an office one. The benchmark profile is used to normalize the consumption extracted from the 3 tariffs the bill is divided into, accounting for weekdays, Saturdays and Sundays. The calibration is carried out together with a sensitivity analysis of on-site solar electricity production. The method gives a predicted result with an average 25% MAPE and a 32% cvRMSE during one year of hourly profile reconstruction when compared with the measured data given by the Distributor System Operator (DSO).
Due to the growing use of Green Energy Sources (GESs), the activities of mapping, monitoring, measurement, and detection of various GESs have become crucial. Assessing and measuring GESs are very complex since different environmental conditions occur. This importance is even greater when researchers face a shortage of measuring instruments and tools in many parts of the world. GES assessment is a challenging task that requires accurate and continuous measurement methods. Currently, traditional methods are very time-consuming and require spending money and human sources. So, the use of accurate and fast measurement methods and tools assessing measuring GESs potential are seriously recommended, which can greatly help the growth of the use of GESs, especially to cover and focus large areas. Satellite remote sensing is used to observe the environment in many fields and new and fast applications. Satellites remote sensing technologies and techniques for GESs assessing are fast, accurate, and can help to reduce costs and decision-making risks of GESs converters installations projects and provide suitable products to the public end-users. These could also be used to identify regions of interest for energy converter installations and to accurately identify new areas with interesting potentials. In this case, researchers can dramatically reduce the possibility of significant error in assessment methods. There is a lack of in-situ measuring tools mainly due to their high economic costs in the interested areas; an accurate analysis was carried out to assess the GESs energy potential. Since there are only limited options for further expanding the measurement over large areas, the use of satellites makes it easier to overcome in-situ limitations. Actually, to use and develop it as much as possible, a correct interdisciplinary understanding is needed. Satellite remote sensing technology for identifying suitable areas for GESs power plants could be a powerful tool that is constantly increasing in its new and fast applications but requires good planning to apply it in various GESs converters installations projects. In this article, a comprehensive review on wind, wave, biomass, geothermal sources assessment using Sentinel-1 (S-1) Synthetic Aperture Radar (SAR) satellite estimation has been summarized along with the different techniques available to measure GESs using satellites. In the paper, several of the successful estimation techniques were introduced in each section and compared for the understanding of limitations and strengths of different methods of GESs availability evaluation.
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