Multi-agent approach to modeling and simulation of microgrid operation with vehicle-to-grid system

被引:21
作者
Egbue O. [1 ]
Uko C. [2 ]
机构
[1] Department of Informatics and Engineering Systems, University of South Carolina Upstate, 800 University Way, Spartanburg, 29303, SC
[2] Department of Electrical Engineering, University of South Carolina, 301 Main St., Columbia, 29208, SC
来源
Egbue, Ona (egbueo@uscupstate.edu) | 1600年 / Elsevier Inc.卷 / 33期
关键词
Agent-based modeling; Microgrid; Plug-in electric vehicles; Simulation; Vehicle-to-grid;
D O I
10.1016/j.tej.2020.106714
中图分类号
学科分类号
摘要
This study presents the modeling and simulation of a vehicle-to-grid (V2G) system within a microgrid considering the requirements of various components of the microgrid system such as distributed renewable energy resources (DERs), plug-in electric vehicles (PEVs) and non-PEV loads. The system modeling is carried out using an agent-based methodology where components of the microgrid are modeled as agents. The use of PEV car batteries collectively as a virtual power plant (VPP) enables PEVs to not only act as loads but as energy sources alongside DERs such as wind and solar power generation. The smart control and scheduling of the charging and discharging of the PEVs by the charging station can be used to achieve sustainable integration of a high number of PEVs in the microgrid power system. In addition to simulating a microgrid operation, results of this study show how agents’ behavior change based on factors such as penetration of renewable energy, penetration of PEVs, travel pattern of PEV drivers and price of energy generation. © 2020
引用
收藏
相关论文
共 28 条
  • [21] U.S. Environmental Protection Agency, U.S. Department of Energy, www.fueleconomy.gOv - the Official Government Source for Fuel Economy Information, (2019)
  • [22] Vestas, (Nd). V110-2.0 MW® at a Glance, (2020)
  • [23] Wang Y., Lin H., Liu Y., Sun Q., Wennersten R., Management of household electricity consumption under price-based demand response scheme, J. Clean. Prod., 204, pp. 926-938, (2018)
  • [24] Wang Y., Mao S., Nelms R.M., Online Algorithms for Optimal Energy Distribution in Microgrids, (2015)
  • [25] Weiss G., Multi-agent Systems, (2016)
  • [26] Wen Y., MacKenzie D., Keith D., Modeling charging choices of BEV owners using stated preference data, Paper Presented at the Proceedings of the EVS28 International Electric Vehicle Symposium and Exhibition, Goyang, Korea, (2015)
  • [27] Wu L., Ortmeyer T., Li J., The community microgrid distribution system of the future, Electr. J., 29, 10, pp. 16-21, (2016)
  • [28] Zhou Z., Sun C., Shi R., Chang Z., Zhou S., Li Y., Robust energy scheduling in vehicle-to-grid networks, IEEE Netw., 31, 2, pp. 30-37, (2017)