Multi-Objective Scheduling of V2G-Enabled PEVs in Local Multi-Energy Systems: Balancing Profitability and Carbon Emissions Using Time-Varying Operational Profiles
DOI:
https://doi.org/10.13052/dgaej2156-3306.4144Keywords:
Combined Heat and Power, Local Multi-Energy System, Multi-Objective Optimization (MOO), PEV, Vehicle-to-GridAbstract
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.
Downloads
References
W. Kempton and J. Tomiæ, “Vehicle-to-grid power fundamentals: Calculating capacity and net revenue,” J Power Sources, vol. 144, no. 1, pp. 268–279, Jun. 2005, doi:10.1016/J.JPOWSOUR.2004.12.025.
E. Sortomme and M. A. El-Sharkawi, “Optimal charging strategies for unidirectional vehicle-to-grid,” IEEE Trans Smart Grid, vol. 2, no. 1, pp. 131–138, 2011, doi:10.1109/TSG.2010.2090910.
C. Liu, K. T. Chau, D. Wu, and S. Gao, “Opportunities and challenges of vehicle-to-home, vehicle-to-vehicle, and vehicle-to-grid technologies,” Proceedings of the IEEE, vol. 101, no. 11, pp. 2409–2427, 2013, doi:10.1109/JPROC.2013.2271951.
K. Clement-Nyns, E. Haesen, and J. Driesen, “The impact of Charging plug-in hybrid electric vehicles on a residential distribution grid,” IEEE Transactions on Power Systems, vol. 25, no. 1, pp. 371–380, Feb. 2010, doi:10.1109/TPWRS.2009.2036481.
E. Sortomme and M. A. El-Sharkawi, “Optimal scheduling of vehicle-to-grid energy and ancillary services,” IEEE Trans Smart Grid, vol. 3, no. 1, pp. 351–359, Mar. 2012, doi:10.1109/TSG.2011.2164099.
A. Y. Saber and G. K. Venayagamoorthy, “Plug-in vehicles and renewable energy sources for cost and emission reductions,” IEEE Transactions on Industrial Electronics, vol. 58, no. 4, pp. 1229–1238, Apr. 2011, doi:10.1109/TIE.2010.2047828.
J. Hu, H. Morais, T. Sousa, and M. Lind, “Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects,” Renewable and Sustainable Energy Reviews, vol. 56, pp. 1207–1226, Apr. 2016, doi:10.1016/J.RSER.2015.12.014.
J. A. P. Lopes, F. J. Soares, and P. M. R. Almeida, “Integration of electric vehicles in the electric power system,” Proceedings of the IEEE, vol. 99, no. 1, pp. 168–183, Jan. 2011, doi:10.1109/JPROC.2010.2066250.
Z. Yang, K. Li, and A. Foley, “Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review,” Renewable and Sustainable Energy Reviews, vol. 51, pp. 396–416, Nov. 2015, doi:10.1016/J.RSER.2015.06.007.
S. Zhang, C. Wang, B. Wang, and J. Zhu, “Multi-objective Optimization Charging Strategy for Plug-in Electric Vehicles Based on Dynamic Time-of-use Price”.
W. Li, Z. Lin, K. Cai, H. Zhou, and G. Yan, “Multi-Objective Optimal Charging Control of Plug-In Hybrid Electric Vehicles in Power Distribution Systems,” Energies (Basel), vol. 12, no. 13, pp. 1–19, 2019, Accessed: Jul. 22, 2025. [Online]. Available: https://ideas.repec.org/a/gam/jeners/v12y2019i13p2563-d245392.html.
Z. Xie, K. Chen, Q. Wang, K. Tang, and J. Tian, “Research on Application System of Multi-Objective Optimization in Charging and Discharging Strategy of Electric Vehicle,” Proceedings of 2024 IEEE 6th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2024, pp. 909–913, 2024, doi:10.1109/ICCASIT62299.2024.10828127.
A. A. Karandinou, F. D. Kanellos, A. A. Karandinou, and F. D. Kanellos, “A Method for the Assessment of Multi-objective Optimal Charging of Plug-in Electric Vehicles at Power System Level,” WSEAS Transactions on Systems and Control, vol. 17, pp. 314–323, 2022, doi:10.37394/23203.2022.17.36.
S. Zeynali, N. Rostami, and M. R. Feyzi, “Multi-objective optimal short-term planning of renewable distributed generations and capacitor banks in power system considering different uncertainties including plug-in electric vehicles,” International Journal of Electrical Power & Energy Systems, vol. 119, p. 105885, Jul. 2020, doi:10.1016/J.IJEPES.2020.105885.
L. Zhang, Z. Ying, Z. Yang, and Y. Guo, “Dynamic Multi-Energy Optimization for Unit Commitment Integrating PEVs and Renewable Energy: A DO3LSO Algorithm,” 2024, doi:10.3390/math12244037.
W. Kong, T. Cai, Y. Fan, F. Jiang, and S. Wan, “Multi-objective optimal charging scheduling strategy for electric vehicles considering traffic flow, power grid, and charging stations,” vol. 12165, pp. 74–80, Mar. 2022, doi:10.1117/12.2627941.
W. Cui, N. Cui, T. Li, Y. Du, and C. Zhang, “Multi-objective hierarchical energy management for connected plug-in hybrid electric vehicle with cyber–physical interaction,” Appl Energy, vol. 360, p. 122816, Apr. 2024, doi:10.1016/J.APENERGY.2024.122816.
M. Cuchı, J. Vokøínek, and M. Jakob, “Multi-Objective Electric Vehicle Route and Charging Planning with Contraction Hierarchies,” Proceedings of the International Conference on Automated Planning and Scheduling, vol. 34, pp. 114–122, May 2024, doi:10.1609/ICAPS.V34I1.31467.
V. N. Coelho, “Multi-objective microgrid storage planning problem using plug-in electric vehicles,” Apr. 2016, Accessed: Jul. 22, 2025. [Online]. Available: https://repositorio.ufmg.br/handle/1843/RAOA-BC6J8A.
Y. Wang and X. Jiao, “Multi-objective energy management for PHEV using Pontryagin’s minimum principle and particle swarm optimization online,” Science China Information Sciences, vol. 64, no. 1, pp. 1–3, Jan. 2021, doi:10.1007/S11432-018-9595-3/METRICS.
Y. Zhongan, Y. Kang, and S. Haohui, “Multi-objective Optimization of Electric Vehicle Orderly Charging Based on Variable Preprocessing and MOSO,” Lecture Notes in Electrical Engineering, vol. 1161 LNEE, pp. 374–381, 2024, doi:10.1007/978-981-97-0869-7_41.
D. Ribeiro dos Santos, “Exceptional model mining meets multi-objective optimization: Application to plant growth recipes in controlled environments,” p. 255, Oct. 2021, doi:10.34894/VQ1DJA.
S. Zheng, X. Zhu, L. Xu, Z. Xiang, L. Quan, and B. Yu, “Multi-Objective Optimization Design of a Multi-Permanent-Magnet Motor Considering Magnet Characteristic Variation Effects,” IEEE Transactions on Industrial Electronics, vol. 69, no. 4, pp. 3428–3438, Apr. 2022, doi:10.1109/TIE.2021.3073311.
X. Wang, Y. Fan, C. Yang, Z. Wu, and C. H. T. Lee, “Multi-Objective Optimization Framework of a Radial-Axial Hybrid Excitation Machine for Electric Vehicles,” IEEE Trans Veh Technol, vol. 72, no. 2, pp. 1638–1648, Feb. 2023, doi:10.1109/TVT.2022.3207231.
S. Ehtesham and A. Hanif, “Multi-objective Optimization of Plug-in Hybrid Electric Vehicle (PHEV) Powertrain Families Considering Variable Drive Cycles and User Types over the Vehicle Lifecycle,” Technology (Singap World Sci), 2010.
F. Jiao, Y. Zou, X. Zhang, and R. Zou, “Multi-objective optimal energy management of microgrids including plug-in electric vehicles with the vehicle to grid capability for energy resources scheduling,” Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, vol. 235, no. 3, pp. 563–580, May 2021, doi:10.1177/0957650920942998.
H. Li, C. Zheng, X. Zhang, X. Chen, and X. Chang, “Research on multi-objective optimization coordination of plug-in hybrid electric vehicle and distributed generation,” Proceedings of the 5th IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies, DRPT 2015, pp. 2608–2613, Mar. 2016, doi:10.1109/DRPT.2015.7432688.
W. Wang, M. Wang, J. Guo, Y. Liu, P. Zheng, and Z. Fu, “Multi-Objective Optimization of Less-Rare-Earth Interior Permanent Magnet Synchronous Machines Used for Electric Vehicles,” 2019 22nd International Conference on Electrical Machines and Systems, ICEMS 2019, Aug. 2019, doi:10.1109/ICEMS.2019.8921444.
R. Roberto, R. De Iulio, M. Di Somma, G. Graditi, G. Guidi, and M. Noussan, “A multi-objective optimization analysis to assess the potential economic and environmental benefits of distributed storage in district heating networks: a case study,” International Journal of Sustainable Energy Planning and Management, vol. 20, pp. 5–20, Apr. 2019, doi:10.5278/IJSEPM.2019.20.2.
E. D. Mehleri, H. Sarimveis, N. C. Markatos, and L. G. Papageorgiou, “A mathematical programming approach for optimal design of distributed energy systems at the neighbourhood level,” Energy, vol. 44, no. 1, pp. 96–104, Aug. 2012, doi:10.1016/J.ENERGY.2012.02.009.
C. Weber and N. Shah, “Optimisation based design of a district energy system for an eco-town in the United Kingdom,” Energy, vol. 36, no. 2, pp. 1292–1308, Feb. 2011, doi:10.1016/J.ENERGY.2010.11.014.
G. Liu, Y. Xu, and K. Tomsovic, “Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization,” IEEE Trans Smart Grid, vol. 7, no. 1, pp. 227–237, Jan. 2016, doi:10.1109/TSG.2015.2476669.
T. Terlouw, T. AlSkaif, C. Bauer, and W. van Sark, “Optimal energy management in all-electric residential energy systems with heat and electricity storage,” Appl Energy, vol. 254, p. 113580, Nov. 2019, doi:10.1016/J.APENERGY.2019.113580.
H. Ren, W. Zhou, K. Nakagami, W. Gao, and Q. Wu, “Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects,” Appl Energy, vol. 87, no. 12, pp. 3642–3651, Dec. 2010, doi:10.1016/J.APENERGY.2010.06.013.
U. Ciarniello, G. Orsini, and F. Santi, “1.2. 2-Indagine sui consumi e sulla diffusione delle apparecchiature nel settore terziario in Italia,” Technical Report RSE A5–053452 (In Italian)(2005), 2005.
R. and A.-C. E. (ASHRAE) American Society of Heating, “International Weather for Energy Calculations (IWEC Weather Files) Users Manual and CD-ROM,” 2001, ASHRAE Atlanta, GA. Accessed: Jul. 22, 2025. [Online]. Available: https://www.mercatoelettrico.org/en/.
“Data available from.” Accessed: Jul. 22, 2025. [Online]. Available: https://www.mercatoelettrico.org/en/.
I. P. on C. C. (IPCC) and I. E. A. (IEA), “Guidelines for National Greenhouse Gas Inventories E CO2 Emissions from Fuel Combustion, Highlights,” International Energy Agency, Paris, 2018. [Online]. Available: https://www.iea.org/data-and-statistics/data-product/greenhouse-gas-emissions-from-energy-highlights.
Educogen, “Educogen, The European Educational Tool on Cogeneration, second ed.,” Educogen, 2001.
