## 1 Introduction
The results show that, under parallel mode, the operation cost of the micro-grid decreases by 2.9% by applying IPSO the algorithm compared to the other algorithm of PSO, while the operation cost is only 2.7% from the original algorithm under islanding mode. [PERSON] et al. [21] proposed an optimization of pulse CVT based on an improved particle swarm algorithm. The results show that the accuracy and robustness of the improved particle swarm algorithm is improved by an order of magnitude, the solution time is reduced by 70%, and the optimal transmission speed is decreased by about 41%. [PERSON] et al. [22] used the algorithm based on improved particle swarm optimization to detect the optimum drainage route in the current environment and proposed an intelligent scheduling method for the drainage routing problem in drainage pipe network. [PERSON] [23] proposed a grid harmonic estimation method based on the particle swarm optimization algorithm. The improved particle swarm optimization algorithm was used to simulate the dynamic voltage signal, and the parameter estimation effects in different noise environments were compared. [PERSON] et al. [24] introduced the idea of neighborhood particles into the standard particle swarm optimization algorithm and proposed a furnace flame recognition method based on the improved particle swarm optimization algorithm. [PERSON] et al. [25] improved the particle swarm algorithm to solve the problem of optimal keying angles for inverters. The simulation results represent the high efficiency of the improved type of particle swarm algorithm in determining the appropriate angle of fire for reducing the high harmonics and generating a waveform with a very low harmonic distortion and near sinusoidal. [PERSON] et al. [26] proposed an improved particle swarm optimization algorithm to solve the AGV path planning problem of a one-line production line in the workshop. The experimental results show that the algorithm can improve the efficiency of AGV in material transportation and verify the effectiveness of the related improvement mechanisms. [PERSON] [27] proposed a dormitory allocation method oriented around the heuristic particle swarm genetic algorithm, which uses a traditional greedy algorithm, heuristic improved particle swarm algorithm, and improved genetic algorithm to optimize the randomly allocated dormitory sequence. [PERSON] et al. [28] improved and applied the adaptive particle swarm optimization algorithm to the study of statistical model fitting of atmospheric turbulence profiles. The simulation results show that the proposed method has a similar convergence accuracy to other adaptive particle swarm optimization algorithms, but the speed is faster. In 2018, [PERSON], [PERSON] et al. [29] established a mathematical model to peak-shave and valley-fill the electricity consumption of non-residential buildings by managing the charging/discharging process in an electric vehicle parking lot to stabilize the load. [PERSON] et al. [30] discovered that the COP (coefficient of performance) of the heat pump was affected by the partial load rate and outdoor temperature. They proposed a method to reduce the operation cost of the heat pump by adjusting the temperature and mass flow rate of supply and return water, and the results showed that the operation cost could be saved by 20-30%. [PERSON] et al. [31] analyzed a community's appliances, such as their HAVC (heating, ventilation, and airconditioning) systems, electric boilers, electric cars, and water pumps. They found that the time-varying power price could encourage residents to concentrate power consumption from peak hours to valley hours on the premise of not incurring a significant inconvenience. [PERSON] et al. [32] performed a calculation of three configurations of a combined solar evacuation collector and ground source heat pump. They found that the indirect expansion of the collector in parallel had the highest COP (3.96), which was superior to those of a direct expansion and indirect expansion in series.