## 4 Discussion
### Case Simulation and Results Analysis  
Figure 5 presents a comparison between the original forecasted wind power and the optimized scheduled power. Figure 6 shows the _SOC_ curve of the energy storage system along with the corresponding time-of-use electricity price curve at each time interval. Since the _SOC_ curve includes the initial _SOC_, a total of 145 sampling points are displayed. Table 4 further summarizes the comparison of day-ahead economic benefits and risk indicators of the wind/storage system under the optimized scheduling results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Rated Capacity & 1500 kWh & Minimum _SOC_ & 0.1 \\ Rated Power & 700 kW & Maximum _SOC_ & 0.9 \\ Charging Efficiency & 0.95 & Terminal _SOC_ & 0.5 \\ Discharging Efficiency & 0.95 & Maintenance Cost & 0.02 CNY/kWh \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Energy storage system parameters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Period Type** & **Time Range** & **Purchase Price (CNY/kWh)** & **Selling Price (CNY/kWh)** \\ \hline Peak & 10:00–15:00 & 0.83 & 0.65 \\  & 18:00–21:00 & & \\  & 7:00–10:00 & & \\ Flat & 15:00–18:00 & 0.49 & 0.38 \\  & 21:00–23:00 & & \\ Valley & 23:00–7:00 & 0.17 & 0.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Time-of-use electricity pricing. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Period Type** & \begin{tabular}{c} **Purchase Price** \\ **(CNY/kWh)** \\ \end{tabular} & \
\begin{tabular}{c} **Dynamic Penalty** \\ **Coefficient** \\ \end{tabular} &
\begin{tabular}{c} **Unit Penalty Cost for Scheduling** \\ **Deviation (CNY/kWh)** \\ \end{tabular} \\ \hline Peak & 0.83 & 2.0 & 1.66 \\ Flat & 0.49 & 1.5 & 0.735 \\ Valley & 0.17 & 1.5 & 0.255 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Penalty cost for scheduling deviations in different time periods. According to the results in Figures 5 and 6 and Table 4, the optimized initial \(SOC\) is 0.1. This decision is mainly influenced by the time-of-use electricity price structure and the cost associated with energy storage. During valley periods, the electricity purchase price is only 0.17 CNY/kWh, which is significantly lower than the unit cost of the initial \(SOC\) (0.45 CNY/kWh). If the initial \(SOC\) is set too high, the storage system will have little room for charging during low-price hours, making it impossible to take advantage of cheaper electricity and missing the opportunity to reduce operational costs. At the same time, the feed-in tariff during valley periods is also low, making discharging less profitable and reducing the cost-effectiveness of selling stored energy. In contrast, during peak periods (10:00-15:00 and 18:00-21:00), the high electricity price encourages the wind/storage system to discharge the storage unit in coordination with wind power to increase the scheduled output and achieve peak-period arbitrage gains. The final optimization results show \begin{table}
\begin{tabular}{c c} \hline
**Indicator** & **Value** \\ \hline Initial \(SOC\) & 0.1 \\ Expected Revenue (CNY) & 16,233.78 \\ Minimum Scenario Revenue (CNY) & 15,532.48 \\ Maximum Scenario Revenue (CNY) & 16,263.00 \\ CVaR (CNY) & 15,903.29 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of day-ahead economic benefits and risk indicators of the wind/storage system. Figure 5: Comparison of forecasted and scheduled wind power. Figure 6: Variation of energy storage state of charge. that the expected revenue across multiple scenarios is CNY 16,233.78, with a CVaR of CNY 15,903.29. The minimum and maximum scenario revenues are CNY 15,532.48 and CNY 16,263.00, respectively. To validate the effectiveness of the proposed day-ahead planning and scheduling model for wind/storage systems based on multi-scenario generation and CVaR, three comparison schemes are designed using the same set of wind power output scenarios.