Hierarchical Optimization Scheduling Model of Multi-Microgrids Based on Analytical Target Cascading

Amidst growing global environmental awareness and energy efficiency demands, optimizing Multi-Microgrid (MMG) operational strategies is crucial. This study introduces an innovative energy scheduling model that integrates economic and environmental considerations by incorporating carbon trading costs and setting stringent carbon emission constraints. Additionally, to mitigate the impact of electricity price fluctuations, a peak-valley pricing mechanism optimizes costs associated with external electricity purchases and transactions between MMGs and sub-microgrids. A key component of this research, the Analytical Target Cascading (ATC) method, plays a pivotal role by decoupling and optimizing the interaction between the MMG energy scheduling center and sub-microgrids. ATC's hierarchical structure allows each level to focus on its specific constraints and objectives, enhancing efficiency and ensuring the attainment of global optimal solutions. Case studies confirm the model's efficacy in significantly reducing carbon emissions and managing price volatility, demonstrating substantial economic and environmental benefits. This study not only bolsters the MMG system's sustainability and cost-effectiveness but also offers practical approaches for the broader adoption of renewable energy, contributing vitally to the sustainable development of energy systems.