Footprint Optimization of Smart Energy Hub with Dataset Resolution Conversion Using Kalman Filter

The world strives to reduce carbon emissions in order to confront global warming and severe climate changes. This paper aims to achieve an optimization technique for energy assets, to minimize carbon emission. This study prioritizes the minimization of carbon emission, while considering the minimum effect on the running cost. In this work two scenarios have been studied for the proposed smart energy hub, in order to compare the results. For the first scenario, a minimization of hub component carbon emission objective function, while in the second scenario, a minimization of hub component running cost and maximization profit were studied. The multi objective model is presented to analyze the economic and environmental impacts, and to obtain the optimal design. This study proposed a novel method for calculating the quarter hour resolution dataset from hourly one by applying Kalman filter instead of applying classic interpolation methods.