Multi-objective optimization of thermochemical energy storage systems with configuration planning for different applications

Due to the lack of effective operation configuration planning strategy, the promotion and efficient operation of thermochemical energy storage systems, which are seriously affected by parameter changes, are hindered. Based on the mathematical model developed from experimental data, this study proposes a configuration planning strategy combining non-dominated sorting genetic algorithm and TOPSIS algorithm. The minimum heating temperature, available heating energy and available heating time are used as the optimization targets of the discharging process, while the energy storage temperature, energy storage efficiency and total stored energy are the optimization targets of the charging process. Higher supplied heating temperature is only suitable for short-term heating scenarios after storing excess energy. Lowering the heating temperature increases the available heating energy by 2.58 times, and the reactor discharging efficiency is increased to 85.68%. The inlet air moisture is the key factor in increasing the air temperature rise. There is an optimal reaction point inside the reactor that reaches the saturation state first. For the charging process, the scenario with lower energy storage temperature requires a larger air mass flow rate. Increasing the energy storage temperature increases the total stored energy by 16.05%, but it will reduce the energy storage efficiency.