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Unit commitment problem of power system with plug-in electric vehicles

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PL
Problem zobowiązań energetycznych jednostek wytwórczych w systemie energetycznym, zawierającym podłączane pojazdy elektryczne
Języki publikacji
EN
Abstrakty
EN
The electric vehicle (EV) has become a popular topic because of the increasing scarcity of energy sources and growing environmental pollution. Unit commitment (UC) with plug-in hybrid electric vehicle (PHEV) for cost optimization is presented in this paper. The profile of charging load and vehicle-to-grid (V2G) power of PHEV is forecasted, and various scenarios with different PHEV control strategy are simulated. Quantuminspired binary particle swarm optimization algorithm with heuristic strategy has been employed to solve the UC problem. Results show that PHEVs will significantly affect the UC problem. PHEVs bring on new load demand to power system, which will increase the generation cost. However, the coordinated charging strategy and reasonable usage of V2G power can reduce the generating cost.
PL
W artykule przedstawiono problem optymalizacji kosztów ładowania pojazdów elektrycznych (ang. Plug-in Hybrid Electric Vehicle) pod względem doboru jednostek wytwórczych. Badaniom poddano różne metody regulacji przepływu energii do ładowania odbiorników w postaci pojazdów elektrycznych. Wykazany został wpływ nowego rodzaju obciążenia na zwiększenie kosztów wytwarzania energii.
Rocznik
Strony
297--302
Opis fizyczny
Bibliogr. 25 poz., tab., rys.
Twórcy
autor
autor
autor
autor
  • State Key Laboratory of Advanced Electromagnetic Engineering and Technology (Huazhong University of Science and Technology), gaowanglee@gmail.com
Bibliografia
  • [1] E. Ungar, K. Fell. Plug In, Turn On, and Load Up, IEEE Power and Energy Magazine, 8 (2010), No. 3, 30 -35.
  • [2] EPRI. Transportation Electrification-A Technology Overview, 1021334, Final Report, July 2011.
  • [3] Kejun Qian, Chengke Zhou, M. Allan, Yue Yuan. Modeling of Load Demand Due to EV Battery Charging in Distribution Systems, IEEE Trans. Power Systems, 26 (2011), No. 2, 802-810.
  • [4] Ahmed Yousuf Saber, Ganesh Kumar Venayagamoorthy. Intelligent unit commitment with vehicle-to-grid-A cost-emission optimization, J Power Sources, 195 (2010), No. 3, 898-911.
  • [5] Robert C., Green II, Lingfeng Wang, Mansoor Alam. The impact of plug-in hybrid electric vehicles on distribution networks: A review and outlook, Renewable and Sustainable Energy Reviews, 15 (2011), No. 1, 544-553.
  • [6] Lu Lingrong, Wen Fushuan, Xue Yusheng, Xin Jianbo. Unit Commitment in Power System with Plug-in Electric Vehicles, Automation of Electric Power Systems, 35 (2011), No. 21, 16-20. (in chinese)
  • [7] F. N. Lee. Short-term thermal unit commitment-a new method, IEEE Trans. Power Systems, 3 (1988), No. 2, 421-428.
  • [8] T. Senjyu, K. Shimabukuro, K. Uezato, et al. A fast technique for unit commitment problem by extended priority list, IEEE Trans. Power Systems, 18 (2003), No. 2, 882-888.
  • [9] P. G. Lowery. Generating Unit Commitment by Dynamic Programming, IEEE Trans. Power Apparatus and Systems, 85 (1966), No. 5, 422-426.
  • [10] C. K. Pang, G. B. Sheble, F. Albuyeh. Evaluation of Dynamic Programming Based Methods and Multiple area Representation for Thermal Unit Commitments, IEEE Trans. Power Apparatus and Systems, 100 (1981), No. 3, 1212-1218.
  • [11] Walter L. Snyder, H. David Powell, John C. Rayburn. Dynamic Programming Approach to Unit Commitment, IEEE Trans. Power Systems, 2 (1987), No. 2, 339-348.
  • [12] J. A. Muckstadt, R. C. Wilson. An Application of Mixed-Integer Programming Duality to Scheduling Thermal Generating Systems, IEEE Trans. on Power Apparatus and Systems, 87 (1968), No. 12, 1968-1978.
  • [13] S. J. Wang, S. M. Shahidehpour, D. S. Kirschen, et al. Shortterm generation scheduling with transmission and environmental constraints using an augmented Lagrangian relaxation, IEEE Trans. Power Systems, 10 (1995), No. 3, 1294-1301.
  • [14] F. Zhuang, F. D. Galiana. Towards a more rigorous and practical unit commitment by Lagrangian relaxation, IEEE Trans. Power Systems, 3 (1988), No. 2, 763-773.
  • [15] W. Ongsakul, N. Petcharaks. Unit commitment by enhanced adaptive Lagrangian relaxation, IEEE Trans. Power Systems, 19 (2004), No. 1, 620-628.
  • [16] S. A. Kazarlis, A. G. Bakirtzis, V. Petridis. A genetic algorithm solution to the unit commitment problem, IEEE Trans. Power Systems, 11 (1996), No. 1, 83-92.
  • [17] K. S. Swarup, S. Yamashiro. A genetic algorithm approach to generator unit commitment, Int J Elec Power, 25 (2003), No. 9, 679-687.
  • [18] F. Zhuang, F. D. Galiana. Unit commitment by simulated annealing, IEEE Trans. Power Systems, 5 (1990), No. 1, 311-318.
  • [19] Grzegorz Dudek. Adaptive simulated annealing schedule to the unit commitment problem, Electr Pow Syst Res, 80 (2010), No. 4, 465-472.
  • [20] A. H. Mantawy, Y. L. Abdel-Magid, S. Z. Selim. Unit commitment by tabu search, IEE Proceedings-Generation, Transmission and Distribution, 145 (1998), No. 1, 56-64.
  • [21] Zwe-Lee Gaing. Discrete particle swarm optimization algorithm for unit commitment, IEEE Power Engineering Society General Meeting, 1 (2003), 418-424.
  • [22] US. Department Of Transportation. National Household Travel Survey, [Online]. Available: http://nhts.ornl.gov/download.shtml.
  • [23] Yun-Won Jeong, Jong-Bae Park, Se-Hwan Jang, et al. A New Quantum-Inspired Binary PSO: Application to Unit Commitment Problems for Power Systems, IEEE Trans. on Power Systems, 25 (2010), No. 3, 1486-1495.
  • [24] Balci H H, Valenzuela J F. Scheduling electric power generators using particle swarm optimization combined with the Lagrangian relaxation method, Int. J. Appl. Math. Comput. Sci., 14 (2004), No. 3, 411-421.
  • [25] Yuan Xiaohui, Su Anjun, Nie Ha, et al. Unit Commitment Problem Baed on PSO with Heuristic-Adjusted Strategies, Trans. of China Electrotechnical Socirty, 24 (2009), No. 12, 137-141. (in chinese)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPS4-0004-0106
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