Modeling seasonal thermal storage dynamics in the year-round scheduling of renewable energy systems

Seasonal thermal storage (STS) enables the long-term storage of renewable energy in heat, which could effectively address the seasonal mismatch between renewable energy supply and heat demand. This paper models the temperature distribution of the water-based STS, considering the insulating effects of the soil surrounding the tank. Considering that the commonly used state-of-charge (SOC) model could not describe the time-variant heat loss in detail in a renewable energy system scheduling problem, a temperature field correction method is proposed to correct the scheduling results. Three case studies are performed on Garver’s 6-node system and HRP-38 system to validate the proposed method’s enhanced accuracy in managing STS compared to the SOC model. The proposed method could also reduce renewable curtailment in the scheduling problem due to a more detailed description of the heat loss process and a more effective renewable energy system scheduling scheme.