Multi-Objective Scheduling of V2G-Enabled PEVs in Local Multi-Energy Systems: Balancing Profitability and Carbon Emissions Using Time-Varying Operational Profiles

Abstract

Plug-in electric vehicles (EVs) introduce both benefits and challenges to effective energy regulation when considered in contemporary power systems. To incorporate PEVs with V2G and G2V technology into LMESs, the authors of this study propose an in-depth MOO framework, which maximizes economic profitability and minimizes CO2 emissions through simulations of a system with gas carriers, electricity, heating, cooling, and other renewable energy sources (RES), including photovoltaics, CHP units, and thermal storage. The model relies on time-varying operational inputs, including varying electricity prices and variable RES generation profiles. Findings indicate that PEV integration progressively improves both economic and environmental performance across the examined scenarios. Compared with the No-PEV case, G2V operation increases operator profit from about 12000$ to about 18000$ while reducing CO2 emissions from about 6000 kg to about 4800 kg. When V2G is enabled, operator profit rises further to about 22000$ and total CO2 emissions decline to about 3500 kg. The Pareto frontier further confirms a clear trade-off between environmental and economic objectives, spanning approximately 1800-3500 kg CO2 and 12000$–22000$ across the sampled solutions. The study of PEV behavior in clusters further shows differentiated flexibility patterns for residential, commercial, and industrial users, enabling more effective scheduling of G2V and V2G interactions and better utilization of the grid. By promoting the use of V2G and scalable optimization strategies in multi-carrier energy systems, this work provides a solid foundation for sustainable energy management.