## 5 Simulation Analysis
### IEEE-33 System Simulation Analysis  
1. Basic data  
The distribution network load, photovoltaic, and wind power data are shown in Table 3. The installation locations of wind power and photovoltaic are shown in Figure 6. The distribution network contains twelve distributed photovoltaics with a capacity of 0.5 MW, which are connected to nodes 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, and 24. At the same time, the distribution network also contained four distributed wind power generation with a capacity of 0.5 MW, which were connected to nodes 26, 28, 30, and 32. In order to ensure the maximum consumption of renewable energy, each distributed power supply is operated with actual output. 2. Result analysis  
(1) Optimal scheduling results of ETSHE considering load characteristics  
In principle, the nodes that can be connected to ETSHE are 1-32, but harmonics, voltage deviations, and voltage fluctuations caused by high-permeability distributed wind power and photovoltaic will cause some nodes to cancel each other out. Considering the objective function, the node sensitivity analysis method was applied, and the set of nodes for energy storage access included five nodes: nodes 4, 8, 13, 25, and 31. In order to highlight the impact of load characteristics on optimal planning settings, we considered that nodes 4 and 13 were residential loads, node 8 was a commercial load, node 25 was an industrial load, and node 31 was an educational load. According to the optimization model constructed in this paper, the IMOPSO algorithm was used to solve the calculation example. The population size of the IMOPSO algorithm was 80, and \(\mu_{min}\) = 0.5,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Period**} & \multirow{2}{*}{**Total Load**} & **Wind** & **Photovoltaic** & \multirow{2}{*}{**Period**} & \multirow{2}{*}{**Total Load**} & **Wind** & **Photovoltaic** \\  & & **Power** & & & & **Power** & **Power** \\ \hline
1 & 4.8 & 0.42 & 0.00 & 13 & 5.3 & 0.38 & 0.50 \\
2 & 4.8 & 0.40 & 0.00 & 14 & 5.4 & 0.30 & 0.44 \\
3 & 4.8 & 0.48 & 0.00 & 15 & 5  
\(\mu_{max}=0.8\), \(\sigma=0.2\)[30; 31]. The installation position and capacity of the electric regenerative heating equipment are shown in Table 4.  
(2) Node sensitivity analysis before and after comparison  
As can be seen from Table 5, the range of optimized nodes is effectively reduced and the effectiveness of the solution is greatly improved by the use of the node sensitivity analysis method. (3) Power quality improvement analysis  
Through the optimization of two flexible resources, the power quality of distributed wind power with high penetration and photovoltaic power distribution networks has been significantly improved. Figures 7-9 are the comparison curves of the total current harmonic distortion rate, voltage deviation, voltage fluctuation of the distribution network with high penetration wind power, and photovoltaic access in two scenarios. Line charts show the comparison of different scenarios of node power quality indicators, and the bar charts show the percentage improvement of the two scenarios relative to the national standard value. According to the GB/T14549 standard, the total current distortion rate of the electric heat storage heating equipment after being connected to the distribution network should be less than or equal to 2.2% [21; 32]. Figure 7a,b are the comparison curves of the total distortion rate of the node current harmonics in the two scenarios, and Figure 7c is the percentage increase of the harmonic distortion rate of the node current relative to the national standard value. As can be seen from Figure 7a,b, in Scenario 1, nodes 4 and 31 have a high light intensity between 8:00 am and 7:00 pm. Due to the introduction of a large number of power electronic equipment by photovoltaics, the current harmonic distortion rate exceeded the standard. To ensure the stability of the system, wind abandonment, light abandonment, and load shedding must occur.