An improved wind power uncertainty model for day-ahead robust scheduling considering spatio-temporal correlations of multiple wind farms. (February 2023) - Tu, Qingyu; Miao, Shihong; Yao, Fuxing; Yang, Weichen; Lin, Yujun; Zheng, Zhong
Highlights: Based on the KDE model and the non-parametric pair Copula model, the forecast error boundary constraints are established to capture the heteroscedasticity characteristics of the forecast error. The constraints can provide less redundant forecast error boundaries of each wind farm without loss of robustness, which helps the grid operators to determine the reserve power reasonably. By combining the autoregressive integrated moving average (ARIMA) model, the Mallat algorithm, and the t distribution model, the temporal dimension constraints are established to describe the time-series characteristics of wind power. The constraints can well evaluate and limit the maximum ramp rate of wind power and the amplitude and frequency of the random fluctuations, which helps the grid operators to reasonably determine the power ramp capability of the controllable resources. Based on the high-dimensional non-parametric R-vine Copula model, the spatial dimension constraints are established to fit the spatial correlation of multiple wind farms. By fitting the relationship between each two wind farms, the constraints can provide more accurate boundaries of total forecast error of all wind farms, which helps the grid operators to determine reserve power reasonably. Through a 2-stage day-ahead robust scheduling model with a 3-layer min-max-min structure, the effectiveness and superiority of the proposed wind power uncertainty models are verified. The case study shows that the proposed models help to improve the economics of the scheduling plan while ensuring robustness. Abstract: Robust optimization (RO) is an important method to deal with the uncertainty of wind power. The main challenge is to reduce the redundancy of the uncertainty model, thereby improving the economy of the scheduling plan while ensuring its robustness. In this paper, aiming at the day-ahead robust scheduling problem, the uncertainty model of wind power is improved by fitting 3 typical characteristics. First, the kernel density estimation (KDE) model and the non-parametric Copula model are combined to fit the nonlinear correlation between forecast power and forecast error, and the forecast error boundary constraints are established. Second, by combining the Mallet algorithm, the autoregressive integrated moving average (ARIMA) model, and the t distribution model, the temporal dimension constraints are established to describe the time-series characteristics of wind power. Third, the spatial dimension constraints are established based on the high-dimensional non-parametric regular vine (R-vine) Copula model, and the spatial correlation of multiple wind farms is reflected. Based on the above wind power uncertainty model, a 2-stage day-ahead robust scheduling model is established. The case study shows that the proposed wind power uncertainty model helps to achieve the balance between economics and robustness of the scheduling plan.