Marginal Emission Factors (MEFs)-based electricity load optimization for various building types

This study optimizes electricity consumption using Marginal Emission Factors (MEFs) to enhance CO2 reduction strategies, addressing the limitations of the conventional Average Emission Factor (AEF). While AEF assumes uniform emission rates, MEFs more accurately reflect real-time grid operations. Using hourly electricity generation data from the Korea Power Exchange, we estimated MEFs and applied nonlinear programming (NLP) for optimized load shifting.

Results indicate that shifting power use from 2 and 5 a.m. to 8 and 11 p.m. achieves a CO2 reduction of 75.7 kgCO2 per MWh without reducing total electricity use. This study demonstrates the effectiveness of MEF-based optimization and emphasizes the need for real-time grid-aware carbon reduction strategies.