Online Energy Dispatch Strategy for Residential Microgrid Considering Uncertainty of Electric Vehicle

被引：0
|
作者：
Sun H. [1 ]
Chen Z. [1 ]
Wu J. [2 ]
机构：
[1] Department of Automation, University of Science and Technology of China, Hefei, 230027, Anhui
[2] Department of Automotive Engineering, Hefei University of Technology, Hefei, 230009, Anhui
来源：
Dianwang Jishu/Power System Technology | 2019年 / 43卷 / 07期
基金：
中国国家自然科学基金;
关键词：
MCMC; Model predictive control; Online optimization; V2H system;
D O I：
10.13335/j.1000-3673.pst.2018.2847
中图分类号：
学科分类号：
摘要：
Research on V2H systems attracts increasing attention with development of EV and renewable energy. As a controllable load and mobile energy storage, EV plays a role of peak shaving and valley filling, and can be used as backup power, thus improving economy and reliability of microgrid. However, due to uncertainty of EV, reliability and economic operation of microgrid face new challenges. To address this issue, an EV travel model based on MCMC (Markov chain Monte Carlo) is established and an energy management solution based on model predictive control is designed in this paper. The proposed solution can reduce system operational cost by fully considering the energy storage property of the EV. Experiments under different conditions are conducted to verify the proposed energy dispatch strategy. © 2019, Power System Technology Press. All right reserved.
引用
收藏
页码：2544 / 2551
页数：7
相关论文
共 17 条
  • [1] Habib S., Kamran M., Rashid U., Impact analysis of vehicle-to-grid technology and charging strategies of electric vehicles on distribution networks-a review, Journal of Power Sources, 277, pp. 205-214, (2015)
  • [2] Duan X., Hu Z., Cui Y., Et al., Optimal charging and discharging strategy for electric vehicles under large timescales, Power System Technology, 42, 12, pp. 4037-4044, (2018)
  • [3] Liang Z., Chen H., Wang Y., Et al., Robust economic dispatch of microgrids containing electric vehicles, Power System Technology, 41, 8, pp. 2647-2658, (2017)
  • [4] Muratori M., Impact of uncoordinated plug-in electric vehicle charging on residential power demand, Nature Energy, 3, 3, (2018)
  • [5] Lund P.D., Lindgren J., Mikkola J., Et al., Review of energy system flexibility measures to enable high levels of variable renewable electricity, Renewable and Sustainable Energy Reviews, 45, pp. 785-807, (2015)
  • [6] Li B., Tang K., Planning model and algorithm of microgrid considering energy storage characteristics of electric vehicles, Electric Power Construction, 38, 2, pp. 60-65, (2017)
  • [7] Nguyen D.T., Le L.B., Joint optimization of electric vehicle and home energy scheduling considering user comfort preference, IEEE Transactions on Smart Grid, 5, 1, pp. 188-199, (2014)
  • [8] Corchero C., Cruz-Zambrano M., Heredia F.J., Optimal energy management for a residential microgrid including a vehicle-to-grid system, IEEE Transactions on Smart Grid, 5, 4, pp. 2163-2172, (2014)
  • [9] Lin H., Liu Y., Sun Q., Et al., The impact of electric vehicle penetration and charging patterns on the management of energy hub-A multi-agent system simulation, Applied Energy, 230, pp. 189-206, (2018)
  • [10] Wang Y., Wang B., Chu C.C., Et al., Energy management for a commercial building microgrid with stationary and mobile battery storage, Energy and Buildings, 116, pp. 141-150, (2016)
12