Day-Ahead Planning and Scheduling of Wind/Storage Systems Based on Multi-Scenario Generation and Conditional Value-at-Risk - Jianhong Zhu; Shaoxuan Chen; Caoyang Ji (Applied Sciences)
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 (LiFePO4) 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.