Robustly Multi-Microgrid Scheduling: Stakeholder-Parallelizing Distributed Optimization

Multi-stakeholders in multi-microgrids (MMGs) always face ubiquitous uncertainties which bring great challenges to the distributed scheduling of the system. To cope with this problem, a stakeholder-parallelizing distributed adaptive robust optimization (SPD-ARO) model is proposed in this paper for the scheduling of hybrid ac/dc MMGs. Stakeholders at the utility-, supply-, and network-levels are treated as lower layer bodies who synchronously conduct scheduling while considering multiple uncertainties. A nested column-and-constraint generation algorithm is applied to address the robustness problems of the lower layer, thus facilitating the rapid solution of the ARO model. A virtual level in the upper layer acts as a coordinating center to realize the global scheduling of tie-lines, and finally determine a robust plan for the MMGs. Focusing on the characteristics of ARO models, an improved analytical target cascading (ATC) method is proposed to develop the SPD framework for MMGs, which improves the optimization effect of the SPD-ARO model. Case studies are used to compare the different frameworks, distributed methods and model parameters, and the optimal results verify the superiority and effectiveness of the SPD-ARO model, the improved ATC method, and the solution method.