A multi-objective decision model for residential building energy optimization based on hybrid renewable energy systems

Renewable energy sources (RES) are an inevitable environmental option in near future. These sources compete with conventional power generation, where good wind and solar resources are available. Hybrid renewable energy systems improve the economic and environmental aspects of renewable resources to meet energy demand. This paper aims to propose a multi-objective model to size a hybrid renewable system optimally. The system consists of wind turbines, photovoltaic panels, batteries, and a diesel generator as support for the system. This multi-objective optimization problem is solved using non-dominated sorting genetic algorithm (NSGA-II) method, resulting in the number of system components that will maximize the renewable energy efficiency while minimizing net present cost and CO2 emission. Results are compared to another multi-objective optimization algorithm, Epsilon-constraint. The comparison shows the feasibility of our suggested method for the problem. A residential building complex is then chosen in Khansar, Iran, to apply the proposed model and optimally size the hybrid renewable energy system. Results show that under the chosen climate and the building parameters, the renewable energy efficiency of nearly 80% is achievable which is satisfactory. Furthermore, the results show the undeniable impact of using renewable resources on reducing the pollutants' emission and their related external costs.

Automated summary

Renewable energy sources are an inevitable environmental choice in the future, and hybrid renewable energy systems can effectively improve the economic and environmental benefits of renewable resources. This study proposes a multi-objective model to optimize the design of hybrid renewable energy systems, showing the feasibility of the method. Results from applying the model to a residential building complex in Iran demonstrate significant efficiency in renewable energy utilization and reduction in pollutants' emission.