Version 1
: Received: 1 August 2024 / Approved: 1 August 2024 / Online: 1 August 2024 (17:03:13 CEST)
How to cite:
Khattab, M. M.; Abdelrahim, A.; Youssef, E.; Hamad, M. S.; Elmanfaloty, R. Carbon Footprint Optimization with Data Resolution Conversion via Kalman Filter for Smart Energy Hub. Preprints2024, 2024080103. https://doi.org/10.20944/preprints202408.0103.v1
Khattab, M. M.; Abdelrahim, A.; Youssef, E.; Hamad, M. S.; Elmanfaloty, R. Carbon Footprint Optimization with Data Resolution Conversion via Kalman Filter for Smart Energy Hub. Preprints 2024, 2024080103. https://doi.org/10.20944/preprints202408.0103.v1
Khattab, M. M.; Abdelrahim, A.; Youssef, E.; Hamad, M. S.; Elmanfaloty, R. Carbon Footprint Optimization with Data Resolution Conversion via Kalman Filter for Smart Energy Hub. Preprints2024, 2024080103. https://doi.org/10.20944/preprints202408.0103.v1
APA Style
Khattab, M. M., Abdelrahim, A., Youssef, E., Hamad, M. S., & Elmanfaloty, R. (2024). Carbon Footprint Optimization with Data Resolution Conversion via Kalman Filter for Smart Energy Hub. Preprints. https://doi.org/10.20944/preprints202408.0103.v1
Chicago/Turabian Style
Khattab, M. M., Mostafa Saad Hamad and Rania Elmanfaloty. 2024 "Carbon Footprint Optimization with Data Resolution Conversion via Kalman Filter for Smart Energy Hub" Preprints. https://doi.org/10.20944/preprints202408.0103.v1
Abstract
The severe climate changes due to global warming pushed the world to strive to reduce carbon emissions. This study aims to minimize the carbon emissions for a proposed smart energy hub. This paper prioritizes the minimization of carbon emissions, taking into consideration the minimization of operation running costs, and the maximization of profit. In this paper two optimization scenarios were studied, to compare the results. In the first scenario, the minimization of carbon emissions was achieved, in the second scenario, the minimization of running costs and maximization of profit from the hub assets were studied. The proposed model was designed in MATLAB. Then the results were verified by CPLEX and validated by RTDS. The multi-objective model was presented to obtain the optimal operation. The mitigation of data uncertainty was achieved by applying the Kalman filter. In this work, a novel method was proposed for the estimation of the quarter-hour resolution data from the hourly one via the Kalman filter rather than applying the classic polynomial interpolation methods.
Keywords
Smart energy hub; carbon footprin; , dynamic pricing; Kalman filter
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
