Predict-and-Optimize Model for Day-Ahead Inertia Prediction Using Distributionally Robust Unit Commitment With Renewable Energy Sources

As the persistent proliferation of renewable energy sources (RESs) will have a significant impact on the prediction of power system inertia, a statistically accurate prediction model for the system inertia, like the traditional prediction-then-optimize (PTO) model, might not necessarily lead to higher economics and security in the day-ahead scheduling. This paper proposes a predict-and-optimize (PAO) model for the day-ahead inertia prediction using distributionally robust unit commitment (DRUC). First, the probabilistic prediction of day-ahead inertia is obtained by the kernel density estimation (KDE), and the confidence set is used for the uncertainty quantization of DRUC. Then, the inertia prediction method is embedded into DRUC, combining with the relevant feature and the closed-loop feedback of DRUC cost increment. The cost-based day-ahead inertia prediction method is trained, in which the prediction quality is evaluated by inducing the DRUC cost increment rather than the statistical prediction error. Furthermore, the proposed PAO is trained by rolling updates to efficiently capture the time series characteristics of inertia, reducing the computational burden and enhancing the solution quality of DRUC. The case studies are carried out on the Finnish power system with a public dataset. The results show that the proposed PAO model offers a better prediction accuracy and incurs smaller re-dispatch cost increments as compared with those of the traditional PTO model, providing a higher security and economically efficient tool for transmission system operators (TSOs) in the day-ahead UC scheduling.