Modeling and optimization of a multi-carrier renewable energy system for zero-energy consumption buildings

For the carbon-neutral, a multi-carrier renewable energy system (MRES), driven by the wind, solar and geothermal, was considered as an effective solution to mitigate CO2 emissions and reduce energy usage in the building sector. A proper sizing method was essential for achieving the desired 100% renewable energy system of resources. This paper presented a bi-objective optimization formulation for sizing the MRES using a constrained genetic algorithm (GA) coupled with the loss of power supply probability (LPSP) method to achieve the minimal cost of the system and the reliability of the system to the load real time requirement. An optimization App has been developed in MATLAB environment to offer a user-friendly interface and output the optimized design parameters when given the load demand. A case study of a swimming pool building was used to demonstrate the process of the proposed design method. Compared to the conventional distributed energy system, the MRES is feasible with a lower annual total cost (ATC). Additionally, the ATC decreases as the power supply reliability of the renewable system decreases. There is a decrease of 24% of the annual total cost when the power supply probability is equal to 8% compared to the baseline case with 0% power supply probability.