A novel stochastic multi-area unit commitment (MAUC) framework is proposed to coordinate scheduling of generators and tie lines. In consideration of the randomness and volatility characteristics of wind energy, a worst-case based scenario selection method (SSM) based on the peak and valley shaving of the system, the ramping-up/down rates of net load, and the dispersion of uncertainty factors is presented to reduce the number of scenarios and improve the robustness of unit commitment (UC) schemes. Regarding the balance of efficiency and flexibility of tie-line scheduling, tie-line operation modes are established on the basis of the tie-line load rate (TLLR). Besides, the number of reversals of tie-line power flow directions during the entire scheduling period is also modeled. By linearizing the tie-line operation modes’ equations, the MAUC can be converted into a mixed-integer linear programming (MILP) formulation. Case studies show that more wind energy can be consumed by connecting power systems through tie lines and coordinating scheduling of generators and tie lines. Expected energy not supplied (EENS) of scenario reduction techniques based on the clustering method, i.e. k-means, Gaussian mixture model (GMM) and Fuzzy C-means (FCM), are 147%, 38%, and 130% higher than that of the SSM.

提出了一种新颖的随机多区域单位承诺（MAUC）框架来协调发电机和联络线的调度。考虑到风能的随机性和波动性特征，基于系统的峰和谷剃光，净负载的上升/下降速率以及不确定性的分散性，基于最坏情况的方案选择方法（SSM）提出了一些因素来减少方案的数量并提高单位承诺（UC）计划的稳定性。关于联络线调度的效率和灵活性之间的平衡，基于联络线负载率（TLLR）建立联络线操作模式。此外，还对整个调度周期内联络线潮流方向的反转次数进行了建模。通过线性化联络线操作模式的方程，MAUC可以转换为混合整数线性规划（MILP）公式。案例研究表明，通过联络线连接电力系统以及协调发电机和联络线的调度可以消耗更多的风能。基于聚类方法（即k均值，高斯混合模型（GMM）和模糊C均值（FCM））的场景缩减技术的预期未提供能源（EENS）分别比其高出147％，38％和130％ SSM。