Research and Application of Power Grid Maintenance Scheduling Strategy under the Interactive Mode of New Energy and Electrolytic Aluminum Load

Formulating a reasonable and feasible unit maintenance scheme is a promising way to eliminate potential risks and improve the reliability of power systems. However, the uncertainty and volatility of new energy outputs, such as wind power, increase the difficulty of scheme formulation. To overcome the complexity of uncertainty, a robust unit maintenance scheme considering the uncertainty of new energy output and electrolytic aluminum load is established in this paper. Considering the significant time-series characteristics of new energy, this paper first introduces the definition and mathematical model of information granulation (IG), through which the initial new energy output data can be transformed into fuzzy particles used for prediction and analysis. Moreover, a support-vector machine (SVM) regression prediction model is adopted, and a corresponding progressive search algorithm is designed to determine SVM parameters efficiently. Then, a robust unit maintenance model is established considering the upper and lower predicted error. In addition, electrolytic aluminum loads are allowed to participate in power system dispatch. Finally, the modified reliability test system-Grid Modernization Lab Consortium (RTS-GMLC test system) and an actual power grid in Southwest China are used to verify the accuracy and feasibility of the proposed method.