###### Abstract  
The volatility and uncertainty of wind power output pose significant challenges to the safe and stable operation of power systems. To enhance the economic efficiency and reliability of day-ahead scheduling in wind farms, this paper proposes a day-ahead planning and scheduling method for wind/storage systems based on multi-scenario generation and Conditional Value-at-Risk (CVaR). First, based on the statistical characteristics of historical wind power forecasting errors, a kernel density estimation method is used to fit the error distribution. A Copula-based correlation model is then constructed to generate multi-scenario wind power output sequences that account for spatial correlation, from which representative scenarios are selected via K-means clustering. An objective function is subsequently formulated, incorporating electricity sales revenue, energy storage operation and maintenance cost, initial state-of-charge (SOC) cost, peak-valley arbitrage income, and penalties for schedule deviations. The initial SOC of the storage system is introduced as a decision variable to enable flexible and efficient coordinated scheduling of the wind/storage system. The storage system is implemented using a 1500 kWh/700 kW lithium iron phosphate (LiFePO\({}_{4}\)) battery to enhance operational flexibility and reliability. To mitigate severe profit fluctuations under extreme scenarios, the model incorporates a CVaR-based risk constraint, thereby enhancing the reliability of the day-ahead plan. Finally, simulation experiments under various initial SOC levels and confidence levels are conducted to validate the effectiveness of the proposed method in improving economic performance and risk management capability. wind/storage system; day-ahead scheduling; multi-scenario generation; copula function; conditional value-at-risk +
Footnote †: journal: Applied sciences  
0000-0002-4000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-00000-00000-00000-00000-000000-000000-000000-000000  
simple structure, it fails to account for the risk of deviation caused by forecasting errors, often resulting in large-scale wind curtailment or substantial penalty costs. To improve the feasibility of scheduling plans, some studies have introduced probabilistic constraint optimization frameworks to control the probability of wind power schedule deviations under a specified confidence level [4]. Reference [5] proposed a hydro-wind-thermal joint dispatching method based on the confidence interval of wind power output. By incorporating the confidence interval to model wind power uncertainty and leveraging the regulation capabilities of hydropower and thermal power, the method facilitates the accommodation of wind power uncertainty while minimizing system costs. Meanwhile, robust optimization methods have been increasingly applied to wind power dispatch models to enhance the system's resilience against adverse scenarios [6]. Reference [7] proposed a robust modeling approach for multiple wind farms based on high-dimensional non-parametric Copula functions, which effectively captures the correlation structure of wind power outputs. Reference [8] integrated an improved Wasserstein metric with a combined heat and power (CHP) coordination mechanism to enhance both the conservativeness and economic efficiency of system scheduling. Building on this, Reference [9] proposed a piecewise stochastic robust optimization approach, which divides the uncertainty set of wind power and introduces piecewise linear decision rules to coordinate AGC (Automatic Generation Control) and P2G (Power-to-Gas) resources, thereby improving real-time dispatch efficiency under wind power uncertainty. Although robust optimization performs well in handling extreme risks, its inherently conservative nature may limit the economic performance of dispatch strategies to some extent. To strike a balance between risk control and profit maximization, multi-scenario optimization methods have been extensively studied and applied in wind power scheduling [10].