Optimal Sizing of Photovoltaic/Energy Storage Hybrid Power Systems: Considering Output Characteristics and Uncertainty Factors

Abstract

The integration of PV and energy storage systems (ESS) into buildings is a recent trend. By optimizing the component sizes and operation modes of PV-ESS systems, the system can better mitigate the intermittent nature of PV output. Although various methods have been proposed to optimize component size and achieve online energy management in PV-ESS systems, the optimal interconnection between them has received less attention. In order to maximize the effectiveness of systems with limited component sizes and address the impact of uncertainty on the system, an optimization framework is proposed for determining the optimal size of the PV-ESS system. The proposed framework consists of five parts: determination of optimal size, analysis of component output characteristics, system state prediction, parameter calibration of energy management strategies, and update of system components output features, and it considers uncertain factors, including climate, different components, and battery degradation caused by irregular charging and discharging, to establish the model for energy saving. To validate the results, four different climates in a year were considered. The obtained results indicate that the proposed framework can effectively achieve the optimal working state of the system, realizing a matching degree of 94.55% between the offline size optimization and online management strategy. The proposed framework’s universality and effectiveness were demonstrated through simulation analysis across four cities with different climates in China.

Optimization of Electrical and Thermal Storage in a High School Building in Central Greece

“…Their approach included optimal sizing, a component output analysis, state prediction and parameter tuning. Energy management strategies were examined by updating the components' output to maximize energy savings [28]. Wu et al proposed a reinforcement learning model approach for the performance optimization of existing zero-energy houses.…”