Comparative Study of Rule-Based Scheduling Strategies for Community Microgrid Energy Management with Battery Energy Storage

This paper presents a comparative study of rulebased scheduling strategies for community microgrid energy management with battery energy storage systems. The proposed framework evaluates six heuristic scheduling strategies using real-world datasets. We implement a multi-objective optimization approach that balances operating costs and carbon emissions through weighted price-carbon signal integration. Experimental results demonstrate that the optimal weighted strategy achieves 0.73% cost reduction and 0.61% carbon emission reduction compared to the no-battery baseline. The ablation study reveals that increasing battery capacity from 500 kWh to 4000 kWh improves cost reduction from$\mathbf{0. 1 8 \%}$to$\mathbf{1. 4 5 \%}$. Weight sensitivity analysis shows that the optimal price weight of 0.6 provides the best trade-off between economic and environmental objectives. This research contributes to Sustainable Development Goals (SDG) 7, 11, and 13.