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Smart Grid Cost-Emission Unit Commitment via Co-Evolutionary Agents

Author

Listed:
  • Xiaohua Zhang

    (School of Urban Rail Transportation, Changzhou University, Changzhou 213164, China)

  • Jun Xie

    (College of Automation, Nanjing University of Posts and Telecommunication, Nanjing 210023, China)

  • Zhengwei Zhu

    (College of information science and engineering, Changzhou University, Changzhou 213164, China)

  • Jianfeng Zheng

    (School of Urban Rail Transportation, Changzhou University, Changzhou 213164, China)

  • Hao Qiang

    (School of Urban Rail Transportation, Changzhou University, Changzhou 213164, China)

  • Hailong Rong

    (School of Urban Rail Transportation, Changzhou University, Changzhou 213164, China)

Abstract

In this paper, the uncertainty of wind, solar and load; smart charging and discharging of plug-in hybrid electric vehicles (PHEVs) to and from various energy sources; and the coordination of wind, solar power, PHEVs and cost-emission are considered in the smart grid unit commitment (UC). First, a multi-scenario simulation is used in which a set of valid scenarios is considered for the uncertainties of wind and solar energy sources and load. Then the UC problem for the set of scenarios is decomposed into the optimization of interactive agents by multi-agent technology. Agents’ action is represented by a genetic algorithm with adaptive crossover and mutation operators. The adaptive co-evolution of agents is reached by adaptive cooperative multipliers. Finally, simulation is implemented on an example of a power system containing thermal units, a wind farm, solar power plants and PHEVs. The results show the effectiveness of the proposed method. Thermal units, wind, solar power and PHEVs are mutually complementarily by the adaptive cooperative mechanism. The adaptive multipliers’ updating strategy can save more computational time and further improve the efficiency.

Suggested Citation

  • Xiaohua Zhang & Jun Xie & Zhengwei Zhu & Jianfeng Zheng & Hao Qiang & Hailong Rong, 2016. "Smart Grid Cost-Emission Unit Commitment via Co-Evolutionary Agents," Energies, MDPI, vol. 9(10), pages 1-13, October.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:10:p:834-:d:80720
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    References listed on IDEAS

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    1. Dang, Chuangyin & Li, Minqiang, 2007. "A floating-point genetic algorithm for solving the unit commitment problem," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1370-1395, September.
    2. Tuohy, Aidan & Meibom, Peter & Denny, Eleanor & O'Malley, Mark, 2009. "Unit commitment for systems with significant wind penetration," MPRA Paper 34849, University Library of Munich, Germany.
    3. Sioshansi, Ramteen & Fagiani, Riccardo & Marano, Vincenzo, 2010. "Cost and emissions impacts of plug-in hybrid vehicles on the Ohio power system," Energy Policy, Elsevier, vol. 38(11), pages 6703-6712, November.
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    5. Wang, Jianhui & Liu, Cong & Ton, Dan & Zhou, Yan & Kim, Jinho & Vyas, Anantray, 2011. "Impact of plug-in hybrid electric vehicles on power systems with demand response and wind power," Energy Policy, Elsevier, vol. 39(7), pages 4016-4021, July.
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