Data-driven nested robust optimization for generation maintenance scheduling considering temporal correlation

Traditional power grids are gradually transitioning to smart grids with high penetration of renewable energy, which can realize the efficient utilization of power resources and low carbon emissions. However, the uncertainties of renewable energy (e.g., wind power) and load demand pose considerable challenges to secure operation and cost-effective planning in smart grids, such as generation maintenance scheduling (GMS). In this context, conventional methods including stochastic optimization and robust optimization are adopted to cope with the uncertainties and formulate the GMS plan. Unfortunately, these methods fail to consider the temporal information in uncertain variables, which can introduce extra operational costs brought by the uncertainties. To address this issue, we consider the temporal correlation of the uncertain wind power and load demand, and develop a data-driven two-stage nested robust optimization (NRO) approach for GMS to minimize the total costs of power system operation under uncertain scenarios. In our proposed approach, a temporal correlation Dirichlet process mixture model (TCDPMM) is developed to investigate the temporal information in the wind power and load demand datasets. Then, variational Bayesian inference (VBI) is employed to construct the data-driven uncertainty set, in which the temporal information for the uncertain variables and the correlations between the uncertain variables are considered. Subsequently, combined with this uncertainty set, a two-stage GMS problem is converted to a “min–max-max–min” optimization problem which is solved by the parallel Benders’ decomposition algorithm. The effectiveness and superiority of the proposed approach are demonstrated with a six-bus power system and a practical power system in China.