A decentralized access control algorithm for PHEV charging in smart grid

Springer Science and Business Media LLC - Tập 5 - Trang 607-626 - 2013
Kan Zhou1, Lin Cai1
1Department of Electrical and Computer Engineering, University of Victoria, Victoria, Canada

Tóm tắt

Plug-in hybrid electric vehicle (PHEV), in addition to its environment-friendliness, brings both challenges (due to its high demand) and opportunities (thanks to the elasticity of its demand) to future smart grid. How to control users’ elastic demand to reduce demand peaks and effectively use renewable energy are key objectives for smart grid, which also spark numerous research efforts. Existing solutions are either centralized, or decentralized based on real time pricing (RTP). In this paper, we introduce a new distributed random access approach for controlling PHEV charging, which does not need centralized control and can be executed in real time. Different from the existing work, we use the history information rather than RTP to coordinate all the distributed smart agents which schedule the PHEV charging. Simulation results show that the proposed decentralized access algorithm is efficient and effective in reducing peaks caused by uncoordinated PHEV charging, and can provide automatic demand response to make the demand follow the change of renewable energy supply. The proposed algorithm is simple and scalable to implement.

Tài liệu tham khảo

Chambers, J.T.: Fleet Electrification Roadmap. Revolutionizing Transportation and Achieving Energy Security (2010)

Tanaka, N.: Technology Roadmap: Electric and Plug-in Hybrid Electric Vehicles [Online]. Available http://www.iea.org/papers/2009/EV_PHEV_Roadmap.pdf (2011)

Sikes, K. et al.: Plug-in Hybrid Electric Vehicle Market Introduction Study. Final report, U.S. Dept. Energy, ORNL/TM-2009/019 (2010)

Wu, D., Aliprantis, D., Ying, L.: Load scheduling and dispatch for aggregators of plug-in electric vehicles. IEEE Trans. Smart Grid 3(1), 368–376 (2012)

Fairley, P.: Speed bumps ahead for electric-vehicle charging. IEEE Spectr. 47(1) (2010)

Ramchurn, S. et al.: Agent-based control for decentralised demand side management in the smart grid. In: Proceedings of AAMAS’11, vol. 1, pp. 5–12 (2011)

Shao, S., Pipattanasomporn, M., Rahman, S.: Grid integration of electric vehicles and demand response with customer choice. IEEE Trans. Smart Grid 3(1), 543–550 (2012)

USA: “Summary of Travel Trends: 2009 National Household Travel Survey”, Federal Highway Administration [Online]. Available http://nhts.ornl.gov/2009/pub/stt.pdf (2012)

FERC: Annual Electric Balancing Authority and Planning Area Report. Available http://www.ferc.gov/docs-filing/forms.asp#714

Xu, J., Wong, V.: An approximate dynamic programming approach for coordinated charging control at vehicle-to-grid aggregator. In: 2011 IEEE International Conference on Smart Grid. Oct, Communications (SmartGridComm) (2011)

Ma, Z., Callaway, D., Hiskens, I.: Decentralized charging control for large populations of plug-in electric vehicles. In: 49th CDC (2010)

Sundstrom, O., Binding, C.: Flexible charging optimization for electric vehicles considering distribution grid constraints. IEEE Trans. Smart Grid 3(1), 26–37 (2012)

Cao, Y., et al.: An optimized EV charging model considering TOU price and SOC curve. IEEE Trans. Smart Grid 3(1), 388–393 (2012)

Chengjian, W. et al.: Learning agents for storage devices management in the smart grid. In: 2010 International Conference on Computational Intelligence and Software Engineering (CiSE) (2010)

O’Neill, D., Levorato, M., Goldsmith, A., Mitra, U.: Residential demand response using reinforcement learning. In: 2010 First IEEE International Conference on Smart Grid. Oct , Communications (SmartGridComm) (2010)

Vytelingum, P., Voice, T., Ramchurn, S., Rogers, A., Jennings, N.: Agent-based micro-storage management for the smart grid. In: AAMAS 2010 (2010)

Chen, C., Kishore, S., Snyder, L.: An innovative RTP-based residential power scheduling scheme for smart grids. In: ICASSP 2011 (2011)

Mohsenian-Rad, A., Leon-Garcia, A.: Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Trans. Smart Grid 1(2):120–133 (2010)

Zhao, J., Dong, Z., Xu, Z., Wong, K.: A statistical approach for interval forecasting of the electricity price. IEEE Trans. Power Syst. 23(2), 267–276 (2008)

Ranjbar, M., Soleymani, S., Sadati, N., Ranjbar, A.: Electricity price forecasting using artificial neural network. PEDES 06, 12–15 (2006)

Ramchurn, S. et al.: Putting the “Smarts” into the Smart Grid: a grand challenge for artificial intelligence. Commun. ACM (2012)

Koutsopoulos, I., Tassiulas, L.: Optimal control policies for power demand scheduling in the Smart Grid. IEEE J. Sel. Areas Commun 30(6) (2012)

Shinwari, M., Youssef, A., Hamouda, W.: A water-filling based scheduling algorithm for the Smart Grid. IEEE Trans. Smart Grid (2012)

Briel, M., Scott, P., Thiebaux, S.: Randomized load control: a simple distributed approach for scheduling smart appliances [Online]. Available http://users.cecs.anu.edu.au/thiebaux/papers/ijcai13-rlc.pdf

Zhou, K., Cai, L.: Randomized PHEV charging under distribution grid constraints. IEEE Trans. Smart Grid (submitted)

Nguyen, H., Song, J., Han, Z.: Demand side management to reduce peak-to-average ratio using game theory in smart grid. In: Proceedings of IEEE Infocom Workshops, Orlando, FL (2012)

U.S. Department of Transportation, Federal Highway Administration, Highway Statistics. Table VM-1 [Online]. Available http://www.fhwa.dot.gov/policy/ohim/hs06/htm/vm1.htm (2006)

U.S. Department of Transportation, Bureau of Transportation Statistics, National Transportation Statistics. Table 4–6. Available http://www.bts.gov/publications/national_transportation_statistics/html/table_04_06.html

U.S. Department of Transportation, Bureau of Transportation Statistics, Transportation Statistics Annual Report 2008, Table 1–1-1 [Online]. Available http://www.bts.gov/publications/transportation_statistics_annual_report/2008/html/chapter_01/table_01_01_01.html (2008)

Peng, H., et al. Resident Plug-in Electric Vehicle Charging Modeling and Scheduling Mechanism in the Smart Grid. (SmartGridComm) (2012) (submitted)

Alto, Palo: Comparing the Benefits and Impacts of Hybrid Electric Vehicle Options. Electric Power Research Institute (2001)

Wind Integration Study [Online]. Availbale http://www.uwig.org/XcelMNDOCStudyReport.pdf

Thông tin
Thông tin xuất bản

Springer Science and Business Media LLC

Tập 5

607-626

Thông tin tác giả