The technical and economic model was established to evaluate the economic feasibility of different energy storage systems using both deterministic and stochastic methods. For deterministic methods, the evaluation of system LCOE uses point value calculations without considering the uncertainty of input variables. The use of Monte Carlo simulation in a stochastic model considers parameter uncertainty and obtains probability distributions for different results, providing more comprehensive information than single point value estimation. In addition, the impact of carbon pricing on the generation cost of different energy storage systems was considered. The main conclusions are as follows:
(1) From the deterministic analysis results, it can be seen that compared to traditional dual tank molten salt energy storage systems, energy storage systems using concrete sensible heat storage, single and cascaded phase change packed bed energy storage, and a combination of sensible and latent heat have economic feasibility. Among them, the LCOE of the C-X1 system is the lowest, at 0.1596 $/kWh and 0.1186 $/kWh in the Blue Map and Roadmap scenarios, respectively, which is 30.12% and 30.11% less than the dual tank molten salt energy storage system.
(2) For the random model, the distribution of LCOE for different energy storage systems was obtained based on the probability density function of the input variables. From the perspective of the degree of influence of input variables, capacity factor is the most important factor affecting the LCOE of energy storage systems, followed by nominal interest rates. In the Roadmap scenario, the impact of learning rate LR on the LCOE of energy storage systems becomes important, mainly because the scale of photothermal power plants in the Roadmap scenario is larger, and the scale effect makes the learning rate have a greater impact on the LCOE of the system. Except for high-temperature phase change energy storage systems, the material costs of energy storage and fluids have a relatively small impact on the system’s LCOE, while external economic factors have a significant impact on the system’s LCOE, requiring attention to external macroeconomic changes.
(3) As the installed capacity increases, the LCOE of different energy storage systems will decrease over time. In the BlueMap scenario, due to the small initial scale, as the scale increases, the decrease in system LCOE is greater than in the Roadmap scenario. As the scale increases to a certain extent, the system LCOE tends to stabilize, and the LCOE reduction effect caused by scale effect weakens. By 2050, the LCOE values of different energy storage systems have decreased by approximately 41% and 27% respectively compared to 2020 in two scenarios.
(4) Based on the risk aversion level of investment decision-makers, the Monte Carlo simulation method was used to quantitatively evaluate the uncertainty of different energy storage systems. Assuming the coefficient of risk aversion is 2, the uncertainty premium of the sensible latent heat combined energy storage system in the Blue Map and Roadmap scenarios is 0.0096 $/kWh and 0.0079 $/kWh, respectively. After considering the uncertain premium, the deterministic equivalents of the C-X1 energy storage system are 0.1852 $/kWh and 0.1321 $/kWh, respectively, which still have the lowest LCOE value among the studied energy storage systems, indicating optimal economic feasibility.
(5) After considering the environmental and social benefits of carbon reduction, the LCOE values of different energy storage systems will decrease in both deterministic and stochastic models, indicating that environmental and social benefits also have a significant impact on improving the economic efficiency of energy storage systems.