A two-phase robust comprehensive optimal scheduling strategy for regional distribution network based on multiple scenarios

With the increasing integration of renewable energy sources, the optimization of distribution networks has become a critical challenge to ensure sustainable and reliable energy supply. In this paper, a robust comprehensive optimization (RCO) strategy based on multi-scenarios is proposed to manage the uncertainty of distributed power supply and load in regional distribution networks, for making up for the shortcomings of existing methods in multi-scenario integrated energy optimization of distribution networks. Firstly, the development of a holistic model that concurrently considers constraints related to wind power, photovoltaics (PVs), gas turbines (GTs), energy storage systems, reactive power compensation, and carbon dioxide (CO2) emissions, ensuring a comprehensive approach to network management. Then, the application of Latin Hypercube Sampling (LHS) for scenario generation, combined with an adaptive K-means clustering approach using the elbow method (EM), which results in the creation of highly representative prototypical scenarios. In addition, the imposition of 1-norm and ∞-norm constraints on the probability confidence intervals for scenario distribution, provides a rigorous framework for addressing uncertainty in energy scenarios. Furthermore, a novel two-phase decomposition model based on the box decomposition algorithm will be introduced to handle the temporal dependencies between energy storage and unit commitment, optimizing both operational costs and system flexibility. Using the column and constraint generation (C&CG) algorithm, the proposed complex optimization problem has been solved comprehensively. Finally, the validation of the model using the IEEE 33-note system based on the Matlab/Simulink platform from four regional distribution networks, demonstrates that the proposed method can effectively improve the practicability, reduce the clustering error, enhance the robustness, and have better scene representation. Copyright © 2024 Ma, Zhang and Wang.