Capacity configuration optimization of photovoltaic‐battery‐electrolysis hybrid system for hydrogen generation considering dynamic efficiency and cost learning

Abstract

Abstract Green hydrogen production via photovoltaic (PV)‐electrolysis is a promising method for addressing global climate change. The battery provides a stable power supply for the PV‐electrolysis system. Hence, this study proposes a robust model for configuring the capacity of a PV‐battery‐electrolysis hybrid system by considering the dynamic efficiency characteristics and cost learning curve effect of key equipments. As a segmented function, the dynamic efficiency of electrolysis is incorporated into the robust model, which describes the hydrogen production efficiency based on power fluctuations. A learning curve model is developed based on historical data from 2012 to 2020 to predict future capital expenditure. Major results are as follows: (1) The use of dynamic efficiency characteristics can reflect the real‐time status of the electrolysis more accurately, and make the capacity configuration more reasonable compared with fixed efficiency. (2) Considering the effect of the learning curve, by 2050, the capital expenditure of the PV panel and proton exchange membrane electrolysis can be dropped to 2981 and 1992 CNY/kW, respectively. (3) The optimal case considering uncertainty currently is a 1 MW PV panel equipped with 242 kW electrolysis and 2276 kW battery.

Keywords

• configuration optimization
• cost learning
• dynamic efficiency
• photovoltaic‐battery‐electrolysis hybrid system