Enhanced bi-level optimal scheduling strategy for distribution network with multi-microgrids considering source-load uncertainties

Abstract

With the increasing integrations of renewable energy resources into distribution networks (DNs) and microgrids (MGs), the imperative for an effective market scheduling mechanism becomes paramount to enhance the operational safety, reliability, and economic efficiency of distribution grids. Taking advantage of bi-level programming theory, this study meticulously formulates a comprehensive optimization scheduling model for the multi-MGs distribution network. The upper-level optimization objective is to minimize both the operational losses and total costs of the DN. Concurrently, the lower-level optimization pursues the maximization of daily operational revenue for MGs. Recognizing the pervasive impact of the inherent uncertainty associated with renewable energy sources on system safety and reliability, a cutting-edge scenario-based stochastic planning framework is introduced. The methodology integrates a heuristic matrix matching approach to effectively handle the intricate challenges posed by uncertainties from wind and photovoltaic generations. Moreover, in addressing the proposed nonlinear models, a sophisticated method is employed, utilizing the second-order cone relaxation and linearization methods. These methods meticulously transform the upper and lower-level models into second-order cone planning and mixed-integer linear programming issues, respectively. Finally, the proposed methodologies are rigorously scrutinized and validated with intricate case studies, providing a nuanced understanding of their efficacy. The empirical results underscore the theoretical feasibility and superiority of the proposed scheduling scheme. Notably, the operational performance of the DN as well as the economic viability of multiple MGs can also be significantly improved.