A Hierarchical Two-Layer Optimization Framework for Renewable Energy Hosting Capacity Enhancement in Distribution-Microgrid Systems with Uncertainty Conversion

The integration of distributed renewable energy sources into power systems has raised challenges in optimizing the operation of distribution network (DN) and microgrids, particularly in terms of renewable energy accommodation and cost reduction. This paper proposes a two-level coordination optimization model, which includes converting source-load uncertainty into deterministic constraints. In this model, the upper layer maximizes renewable energy consumption in the DN and minimizes both DN and microgrid adjustment costs, with decision variables including power exchange between the DN and microgrids. The lower layer optimizes energy storage within the microgrid to improve renewable energy consumption and minimize costs. Both layers iteratively adjust their schedules to reach a global optimal solution. The model is solved using hybrid genetic algorithms in the upper layer and model predictive control in the lower layer. A typical scenario is set up in a distribution network-microgrid system with a high proportion of photovoltaic (PV) generation to demonstrate the validity and efficiency of the proposed model. The proposed method enhances renewable energy utilization and reduces operational costs.