###### Abstract
By constructing multiple representative wind power output scenarios, this approach incorporates the randomness of forecasting errors into the scheduling model, thereby enhancing the stability and flexibility of the dispatch strategy. Reference [11] addressed the uncertainty of load response by proposing a two-stage multi-scenario demand response strategy based on forecast deviation compensation. The prediction-adjustment dual-stage framework effectively mitigates the impact of forecasting errors on plan execution. Reference [12] considered the coordinated regulation capabilities of industrial loads and energy storage systems and developed a multi-timescale optimization model under high wind power penetration scenarios. This approach improves both system flexibility and the economic efficiency of wind power integration. Reference [13] further incorporated environmental wind conditions and proposed an efficient scenario generation method that integrates scenario screening with incremental risk control. This method enhances the robustness and computational efficiency of wind power scheduling models under chance-constrained frameworks. Reference [14] proposed a rolling intraday scheduling strategy for wind/storage systems based on multi-scenario synergy. By incorporating real-time adjustments of energy storage dispatch in response to wind power fluctuations, their method enhances operational flexibility and short-term economic benefits. Accurate modeling of wind power forecast errors and their dependencies is essential for generating realistic multi-scenario datasets. Traditional parametric models--such as Beta, Levy-stable, or Gamma-like distributions--often fail to capture the skewness, heavy tails, and multimodal characteristics observed in actual data [15]. To address these limitations, this study adopts kernel density estimation (KDE) for flexible marginal distribution modeling without predefined assumptions. In addition, Copula-based methods are employed to characterize the spatio-temporal dependencies among forecast errors more accurately than traditional linear correlation models, providing a more realistic foundation for scenario generation [16]. While improving the executability of wind power scheduling, energy storage systems have also been integrated into the dispatch framework as flexible regulation resources. Reference [17] proposed a frequency control strategy based on state-of-charge (SOC) regulation to enable coordinated operation between wind and storage systems. Reference [18] developed a fluctuation characteristic analysis and typical day clustering model based on actual wind and solar output data, and proposed an energy storage capacity optimization strategy aimed at smoothing power fluctuations. However, most existing studies regard energy storage primarily as a regulation resource, without fully exploring its arbitrage potential under time-of-use pricing mechanisms. Moreover, current scheduling models often fail to incorporate the initial _SOC_ of storage systems into the joint optimization framework. On the other hand, although multi-scenario approaches can improve the average profit of dispatch plans, they still face the risk of revenue decline under extreme scenarios. Therefore, Conditional Value-at-Risk (CVaR), as a risk measurement tool, has been introduced into power dispatch optimization models to quantify tail risk [19]. Reference [20] incorporated CVaR into a wind power investment equilibrium model under multiple market trading mechanisms to characterize the risk preferences on the power production side. Reference [21] introduced CVaR into the scheduling model of an integrated energy system with uncertain wind power, enabling the quantification and management of risks caused by wind power volatility. However, existing studies that incorporate CVaR into dispatch models mainly focus on the formulation and constraint of the risk metric itself, with insufficient attention to the participation mechanism of energy storage systems in risk mitigation.