Simultaneous sizing and scheduling optimization for farmhouse PV-battery systems with a multi-structured power system model

Increasing the proportion of photovoltaic (PV) power in energy supplies is effective in decarbonizing energy use in buildings. Optimization model analysis is essential for the design and operation of PV-battery systems. The optimization models developed in previous studies are mainly suitable for one specific scenario, and there is a lack of research on the suitability of different types of PV-battery systems applying in various scenarios. This study proposes a multi-structured power system optimization model for various rural PV-battery systems, compares the optimal sizing and performance of three commonly used PV-battery systems, and quantifies the impacts of system capacity on system performance. The optimization model was constructed using the improved simulated annealing algorithm with the self-consumption rate and economy as the objective functions, while the system node power balance was the constraint. The sensitivity analysis shows that increasing the PV capacity will reduce the PV self-consumption rate and payback period of the system while increasing the battery capacity will increase the PV self-consumption rate and payback period of the system. For every 1 kW & sdot;h increase in battery capacity, the payback period of the system increases by 0.5 years. The spatial optimization model simulates the operation strategies of typical farm houses in different climate zones in China, and obtains payback periods for rural PV-battery systems in different regions of China. This study provides a theoretical basis for capacity sizing for rural PV-battery systems. The payback period of farmhouse PV systems in Gansu, Ningxia, Qinghai, Tibet, and Yunnan regions is <7 years, while the payback period of farmhouse PV systems in Guangdong, Guangxi, Jiangsu, Zhejiang, and Chongqing regions is higher than 10 years.