Low-carbon Optimal Scheduling of Electric Vehicles With High Spatiotemporal Resolution
Main Article Content
Keywords
carbon emission flow, dynamic carbon emission factor, electric vehicles, energy storage system, low-carbon, scheduling
Abstract
As electric vehicles (EVs) become increasingly connected to power systems at scales, their emission reduction benefits no longer depend merely on whether the vehicles themselves are zero-emission, but are also closely related to charging time, charging loc ation, and the carbon intensity of electricity sources. In order to reduce indirect carbon emissions of charging for EVs, a user-side low-carbon optimal scheduling framework for EVs is established, in which dynamic electricity pricing, energy storage system operation, charging characteristics of various EV types and dynamic carbon emission factor are considered. Firstly, based on the carbon emission approach, user-side electricity consumption behavior is associated with nodal carbon intensity, which provides a theoretical basis for evaluating the carbon footprint of EVs. Secondly, through dynamic pricing mechanisms and real-time electricity trading prices, electric vehicles are encouraged to charge during low-load or low-carbon periods, while energy storage systems are coordinated for charging and discharging operations to improve the peak-to-valley load profile. Lastly, experiments are conducted to analyze the variation patterns of optimized electricity price curves, categorized EV charging power behaviors, storage system operating conditions, and the variations in dynamic marginal carbon emission factors and overall carbon emission factors. The experimental results indicate that electricity price signals alone are insufficient for user-side low-carbon scheduling, and that dynamic carbon emission factors should also be taken into account.
References
- [1] Algarni, A. S., Suryanarayanan, S., Siegel, H. J., & Maciejewski, A. A. (2021). Combined impact of demand response aggregators and carbon taxation on emissions reduction in electric power systems. IEEE Transactions on Smart Grid, 12(2), 1825-1827.
- [2] Fabbri, G., Odoardi, S., Tarquini, G., Pasquali, L., Teodori, S., & Santini, E. (2014). Impact of V2G/G2V technologies on distributed generation systems. Proceedings of the IEEE 23rd International Symposium on Industrial Electronics, 1677-1682.
- [3] Kang, C., Zhou, T., Chen, Q., Xu, Q., Xia, Q., & Ji, Z. (2015). Carbon emission flow from generation to demand: A network-based model. IEEE Transactions on Smart Grid, 6(5), 2386-2394.
- [4] Kang, C., Zhou, T., Chen, Q., Xu, Q., Xia, Q., & Ji, Z. (2012). Carbon emission flow in networks. Scientific Reports, 2, Article 479.
- [5] Li, B., Song, Y., & Hu, Z. (2013). Carbon flow tracing method for assessment of demand-side carbon emissions obligation. IEEE Transactions on Sustainable Energy, 4(4), 1100-1107.
- [6] Li, D., Chiu, W.-Y., Sun, H., & Poor, H. V. (2018). Multiobjective optimization for demand side management program in smart grid. IEEE Transactions on Industrial Informatics, 14(4), 1482-1490.
- [7] Lu, Z., Bai, L., Wang, J., Wei, J., Xiao, Y., & Chen, Y. (2023). Peer-to-peer joint electricity and carbon trading based on carbon-aware distribution locational marginal pricing. IEEE Transactions on Power Systems, 38(1), 835-852.
- [8] Mahmoud, M. S., Rahman, M. S. U., & Al-Sunni, F. M. (2015). Review of microgrid architectures: A system of systems perspective. IET Renewable Power Generation, 9(8), 1064-1078.
- [9] Mu, C., Ding, T., Zhu, S., Han, O., Du, P., Li, F., & Siano, P. (2023). A decentralized market model for a microgrid with carbon emission rights. IEEE Transactions on Smart Grid, 14(2), 1388-1402.
- [10] Nguyen, H. N., Zhang, C., & Mahmud, M. A. (2015). Optimal coordination of G2V and V2G to support power grids with high penetration of renewable energy. IEEE Transactions on Transportation Electrification, 1(2), 188-195.
- [11] Qiu, J., Dong, Z. Y., Zhao, J., Xu, Y., Luo, F., & Yang, J. (2016). A risk-based approach to multi-stage probabilistic transmission network planning. IEEE Transactions on Power Systems, 31(6), 4867-4876.
- [12] Rodrigues, Y. R., de Souza, A. C. Z., & Ribeiro, P. F. (2018). An inclusive methodology for plug-in electrical vehicle operation with G2V and V2G in smart microgrid environments. International Journal of Electrical Power & Energy Systems, 102, 312-323.
- [13] Tao, Y., Qiu, J., Lai, S., Wang, Y., & Sun, X. (2023). Reserve evaluation and energy management of microgrids in joint electricity markets based on non-intrusive load monitoring. IEEE Transactions on Industry Applications, 59(1), 207-219.
- [14] Wang, X., Gong, Y., & Jiang, C. (2015). Regional carbon emission management based on probabilistic power flow with correlated stochastic variables. IEEE Transactions on Power Systems, 30(2), 1094-1103.
- [15] Wang, Y., Qiu, J., Tao, Y., Zhang, X., & Wang, G. (2020). Low-carbon oriented optimal energy dispatch in coupled natural gas and electricity systems. Applied Energy, 280, Article 115948.
- [16] Yan, M., Shahidehpour, M., Alabdulwahab, A., Abusorrah, A., Gurung, N., Zheng, H., Ogunmubi, O., Vukojevic, A., & Paaso, E. A. (2021). Blockchain for transacting energy and carbon allowance in networked microgrids. IEEE Transactions on Smart Grid, 12(6), 4702-4714.
- [17] Zhuang, M., Shahidehpour , M., & Li, Z. (2018). An overview of non-intrusive load monitoring: Approaches, business applications, and challenges. Proceedings of the IEEE International Conference on Power System Technology, 4291-4299.
Article Sidebar
This work is licensed under a Creative Commons Attribution 4.0 International License.