As the low-carbon economy continues to evolve, the energy structure adjustment of using renewable energies to replace fossil fuel energies has become an inevitable trend. To increase the ratio of renewable energies in the electric power system and improve the economic efficiency of power generation systems based on renewables with hydrogen production, in this paper, an operation optimization model of a wind–solar hybrid hydrogen energy storage system is established based on electrochemical energy storage and hydrogen energy storage technology. The adaptive simulated annealing particle swarm algorithm is used to obtain the solution, and the results are compared with the standard particle swarm algorithm. The results show that the day-ahead operation scheme solved by the improved algorithm can save about 28% of the system operating cost throughout the day. The analytical results of the calculation example revealed that the established model had fully considered the actual operational features of devices in the system and could reduce the waste of wind and solar energy by adjusting the electricity purchased from the power grid and the charge and discharge powers of the storage batteries under the mechanism of time-of-use electricity price. The optimization of the day-ahead scheduling of the system achieved the minimization of daily system operation costs while ensuring that the hydrogen-producing power could meet the hydrogen demand.

In recent years, the medium-depth ground source heat pump (MD-GSHP) has become a promising and potential geothermal energy heating technology. This is due to the superior characteristics of buried pipes with a small diameter, the high energy efficiency of the heat pump, the insensitivity to the ground climate, and other conditions. Among currently available studies, both the variable operating conditions and the addition of thermal storage devices have rarely been considered. Despite this, the optimization methods applied to the medium-depth GSHP system are relatively simple. In this paper, an MD-GSHP system, including the thermal storage device with variable operating conditions, was fabricated. The operation strategies of the system were optimized by employing the improved particle swarm algorithm after applying the operating costs, the coefficient of performance of the system (COP) and the geothermal energy utilization coefficient to the objective functions, and the optimization results were compared and analyzed. The results show that the predictions of the optimized operating costs, the COP of the system, and the geothermal energy utilization coefficient were found to be CNY 279.27, 6.4420, and 0.8527, respectively. The effect of the COP on the optimization effect was opposite to that of operating costs, but analogous to that of the geothermal energy utilization coefficient.