Smart Home Energy Management with Electric Vehicles Considering Battery Degradation

Abstract:

With the development of vehicles-to-grid (V2G) technology, electric vehicles (EVs) are receiving increasing attention in recent years. This paper describes a smart home energy dispatch model including EVs and flexible appliances. In this model, the home's electricity cost is minimized through optimal dispatch, while the comfort level of home is considered. A simplified method to measure battery degradation is proposed. The model is solved by the dissipative particle swarm optimization (DPSO) algorithm. Simulation results show that the battery lifetime can be extended when the battery degradation is considered in the charging and discharging of the EV. With the decrease in the battery cost, the user has more incentives to use the EV as a storage device to reduce the electricity cost.

• Periodical: Advanced Materials Research (Volumes 860-863)
• Pages: 1085-1091
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.860-863.1085
• Online since: December 2013
• Authors: Qi Zhang, Shao Hua Zhang
• Keywords: Battery Degradation, Electric Vehicle (EV), Optimal Dispatch, Smart Home

References

[1] R. Masiello. Demand Response the other side of the curve [Guest Editorial], IEEE Power and Energy Magazine, vol. 8, no. 3, pp.18-18, May-June (2010).

DOI: 10.1109/mpe.2010.936206

[2] V. Ricquebourg, D. Menga, D. Durand, B. Marhic, L. Delahoche, and C. Loge. The smart home concept: Our immediate future, in Proc. 1st IEEE Int. Conf. on E-Learn. Ind. Electron., Dec. 2006, p.23–28.

DOI: 10.1109/icelie.2006.347206

[3] M. Pedrasa, T. Spooner, and I. MacGill. Coordinated scheduling of residential distributed energy resources to optimize smart home energy services, Smart Grid, IEEE Transactions on, vol. 1, no. 2, p.134 –143, sept. (2010).

DOI: 10.1109/tsg.2010.2053053

[4] Zhe Yu, L. McLaughlin, Liyan Jia, M. C Murphy-Hoye, A. Pratt, Lang Tong. Modeling and stochastic control for Home Energy Management, Power and Energy Society General Meeting, 2012 IEEE, pp.1-9.

DOI: 10.1109/pesgm.2012.6345471

[5] Jidong Wang, Zhiqing Sun, Yue Zhou, Jiaqiang Dai. Optimal dispatching model of Smart Home Energy Management System, Innovative Smart Grid Technologies - Asia (ISGT Asia), 2012 IEEE, pp.1-5.

DOI: 10.1109/isgt-asia.2012.6303266

[6] A Hoke, A Brissette, D Maksimovic, A Pratt, K Smith. Electric vehicle charge optimization including effects of lithium-ion battery degradation, Vehicle Power and Propulsion Conference (VPPC), 2011 IEEE, pp.1-8.

DOI: 10.1109/vppc.2011.6043046

[7] K. Smith, T. Markel, G. -H. Kim, and A. Pesaran. Design of Electric Drive Vehicles for Long Life and Low Cost, IEEE 2010 Workshop on Accelerated Stress Testing & Reliability, Denver, CO, NREL/PR-540-48933, Oct. (2010).

[8] J. Kennedy, R. Eberhart. Particle swarm optimization, Proc. IEEE Int. Conf: on Neural Networks, pp.1942-1948, (1995).

[9] X. Xie, W. Zhang, and Z. Yang. A dissipative particle swarm optimization, Proc. IEEE Congr. Evol. Comput, May 2002, vol. 2, p.1456–1461.

[10] C. A. Coello. Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art, in Computer Methods in Applied Mechanics and Engineering, vol. 191, no. 11-12, 4 January 2002, p.1245.

DOI: 10.1016/s0045-7825(01)00323-1

[11] Laskari E C, Parsopoulos K E, Vrahatis M N. Particle swarm optimization for integer programming, Proc of the 2002 Congress on Evolutionary Computation. Honolulu: IEEE Conf Publications, 2002: 1582-1587.

DOI: 10.1109/cec.2002.1004478