Optimal control of a solar-driven seasonal sorption storage system through deep reinforcement learning

Deep reinforcement learning (DRL) has demonstrated its effectiveness in the control of energy systems, although it has not yet been applied to sorption thermal energy storage (TES) systems. The operation of sorption TES systems is notably more complicated compared to other TES variants. The discharge of a sorption TES occurs at a particular desorption and evaporation temperature. Achieving a continuous and efficient discharge of a sorption TES is a challenging control task if heat required at the evaporator is obtained from the sun or the environment. Its operation is especially complicated during winter, because of the limited availability of solar irradiation and low ambient temperatures. Thus, this study analyzes for first time in the literature the competitiveness of deep reinforcement learning to control a solar-driven seasonal sorption TES system and compares it against traditional optimized rule-based control strategy. The system, located in Central Europe, supplied domestic hot water and space heating to a single-family house. Two DRL models were developed and trained to operate the system under two different sets of data: 120 winter consecutive days and 60 winter non-consecutive days. The results showed that the DRL control strategy reduced the system operational costs by 28% in a 60 winter days scenario. For a 120 winter days scenario, the operational cost savings decreased to 13% because the smart control performed worst once the sorption TES was fully discharged. These results were derived from a four-year validation data set, bolstering their robustness. The study demonstrates the successful application of DRL in controlling a solar-driven seasonal sorption TES system, yielding considerable economic savings compared to an RBC strategy. Subsequent work will consist of implementing the smart control strategy at prototype level to assess its performance.

Automated summary

This study applies deep reinforcement learning (DRL) to control a solar-driven seasonal sorption thermal energy storage (TES) system for the first time and compares it with a traditional optimized rule-based control strategy. The results show that the DRL strategy significantly reduces the operational costs of the system, but performs worse once the sorption TES is fully discharged.