Modeling of Greenhouse Gas Emissions in Energy Communities: A Comparative Study Between Emission and Cost Minimization Strategies

Abstract

This paper develops an optimization framework for energy communities that explicitly accounts for greenhouse gas (GHG) emissions through time-varying grid emission factors. A linear program is formulated with either a cost-minimizing or an emissions-minimizing objective, under identical operational constraints and with optional peer-to-peer (P2P) trading. Real 15-minute data from a Portuguese community are used to evaluate eight scenarios combining two tariff schemes, two objectives, and two trading modes. Results show that P2P consistently lowers both costs and emissions by matching surplus PV locally, while the choice of objective strongly shapes energy storage scheduling. Cost-oriented cases align battery use with price signals, whereas emission-oriented cases align with cleaner periods, reducing GHG emissions by up to 14% at the expense of higher bills (≈13% increase). Notably, emission-oriented schedules are insensitive to tariffs, confirming that dispatch follows the emission factor profile rather than prices. Fairness analysis further indicates that P2P narrows the spread of household bills and reduces outliers. The study highlights the importance of emission-aware formulations in future energy community design and provides quantitative evidence of the trade-offs between cost, emissions, and equity.