# Method for Wind-Solar-Load Extreme Scenario Generation Based on an Improved InfoGAN
## 4 Case Study
### Analysis of Example Results  
To evaluate the performance of the power system under extreme scenarios, simulation tests are conducted with actual data from a power grid in Xinjiang, China, with a simulation duration of one year. Table 7 shows the indicators of the power system's loss of load probability, loss of load frequency, power shortage expectation, and renewable energy discard rate under extreme scenarios.  
Loss of load probability:  
\[\text{LOLP}=\sum_{L_{i}\in L}Prob(L_{i})=\frac{T_{loss}}{T_{all}} \tag{27}\]  
Loss of load frequency:  
\[\text{LOLF}=\sum_{L_{i}\in L}Fre(L_{i})=\frac{F_{loss}}{F_{all}} \tag{28}\]  
Expectation of power shortage:  
\[\text{EENS}=\sum_{F_{i}\in F}Prob(L_{i})\times(L_{i}-R) \tag{29}\]  
Renewable energy discard rate:  
\[\text{REDR}=\sum_{P_{i}\in G_{re}}Fre(P_{i})=\frac{P_{dis,i}}{P_{re,j}} \tag{30}\]  
where LOLP represents the loss of load probability, where \(T_{loss}\) denotes the loss of load duration and \(T_{all}\) is the total simulation time; LOLF refers to the Loss of Load Frequency, with \(F_{loss}\) representing the number of loss of load occurrences and \(F_{all}\) the total number of simulation instances. EENS stands for the Expected Energy Not Supplied, where \(L_{i}\) represents the load and \(R\) is the reserve capacity. REDR indicates the Renewable Energy Dis  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Extreme Weather Events** & **LOLP** & **LOLF** & **EENS (MW)** & **REDS** \\ \hline long term high temperature & 0.032 & 11.534 & 454.790 & 0.471 \\ Extreme cold weather & 0.057 & 20.951 & 542.627 & 0.335 \\ Typhoon weather & 0.012 & 4.566 & 180.922 & 0.561 \\ No wind for a long time & 0.026 & 9.307 & 366.150 & 0.134 \\ Prolonged absence of solar & 0.018 & 6.636 & 358.136 & 0.182 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Practical simulation analysis of scenario sets.  
Figure 9: Solar power–load historical data (**left**) and generated data (**right**) Pearson analysis.  
patch Ratio, where \(P_{dis,i}\) is the dispatched renewable energy and \(P_{re,i}\) is the total renewable energy capacity.  
Table 7 shows that the probability of power system load loss is the highest and that the average annual load loss time is the longest under conditions of prolonged hot weather and extreme cold weather. This is because, under these two extreme weather conditions, the power system load will be at a high level for a long period of time. However, the probability of load loss under long-term hot weather conditions is lower than that under cold weather conditions because long-term high-temperature solar power generation increases to compensate for the energy gap in the power system to a certain extent. However, because the solar system has no output at night, it cannot completely replace conventional power sources. The loss of load probability also increases when there is no wind for a long period and when there is no solar energy for a long period, resulting in a low level of renewable energy for a long period and a lack of energy input to the power system. When typhoon weather occurs, wind power generation increases rapidly, but it is difficult for the power system to absorb this rapid increase in wind power, so the renewable energy discard rate is highest at this time.