Assessment of energy self-sufficiency of a smart farm through integrated modeling of air-source heat pumps and solar power generation

This study explored methods to operate a sustainable system by improving energy independence and integrating renewable energy into a microgrid. However, there are challenges associated with the intermittency of renewable energy and difficulties in predicting the energy demand of energy systems. Therefore, this study emphasizes the need to analyze solar power generation and predict the cooling and heating demands of energy systems. The target system was a smart farm in which hot and cold water was supplied to the smart farm using a heat pump during seasonal changes. A prediction model for the smart farm was developed to calculate power consumption according to the ambient temperature in summer and winter. The results suggested that additional consideration is needed to optimize system operation and renewable energy use to improve the balance between energy consumption and supply. A comparison of the total power consumed to solar energy generation highlighted the challenge of attaining 100% self-sufficiency rates, reaching 44% in summer and 40% in winter. Analysis of solar power generation and air-source heat pump usage trends provided insights into strategies for achieving energy independence in smart farms. In conclusion, this study provides a model for managing energy demands on smart farms and insights into the optimization of system operation with renewable energy. It also provides a foundational framework for evaluating microgrid systems based on renewable energy and contributes to identifying methods to increase the energy self-sufficiency rates to enable sustainability of smart farms.