Emission Based Energy Management Framework for Networked Microgrids using Edge-Fog Computing

The growing global energy demand and the need to reduce carbon emissions has accelerated the shift towards decentralized and sustainable power systems. Networked Microgrids (NMGs) are emerging as a solution to increase distributed energy trading, promote reliability and integration of renewable energy sources. However, managing energy in NMGs while minimizing greenhouse gas (GHG) emissions is a big challenge. This paper proposes an Emission-Based Energy Management Framework for NMGs using Edge-Fog Computing to facilitate informed energy management. The framework uses edge-fog computing to process energy data in real-time to enable fast decision making and reduce system latency. A bi-level optimization approach is used where a local Mixed-Integer Nonlinear Programming (MINLP) optimizer at the edge level optimizes energy dispatch within individual MGs and a global MINLP optimizer at the fog level manages energy exchange between MGs and the utility grid prioritizing emission reduction along with economic efficiency. The simulation results using Australian grid data shows that the proposed framework is cost effective and achieves up to 3.85% cost savings, while managing energy efficiently and reducing emissions.