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Derivation and Application of a New Transmission Loss Formula for Power System Economic Dispatch

Author

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  • Wei-Tzer Huang

    (Department of Industrial Education and Technology, National Changhua University of Education, No. 2, Shida Rd., Changhua 500, Taiwan)

  • Kai-Chao Yao

    (Department of Industrial Education and Technology, National Changhua University of Education, No. 2, Shida Rd., Changhua 500, Taiwan)

  • Ming-Ku Chen

    (Department of Industrial Education and Technology, National Changhua University of Education, No. 2, Shida Rd., Changhua 500, Taiwan)

  • Feng-Ying Wang

    (Department of Industrial Education and Technology, National Changhua University of Education, No. 2, Shida Rd., Changhua 500, Taiwan)

  • Cang-Hui Zhu

    (Department of Industrial Education and Technology, National Changhua University of Education, No. 2, Shida Rd., Changhua 500, Taiwan)

  • Yung-Ruei Chang

    (The Institute of Nuclear Energy Research, 1000 Wenhua Rd., Jiaan Village, Longtan Dist., Taoyuan City 325, Taiwan)

  • Yih-Der Lee

    (The Institute of Nuclear Energy Research, 1000 Wenhua Rd., Jiaan Village, Longtan Dist., Taoyuan City 325, Taiwan)

  • Yuan-Hsiang Ho

    (The Institute of Nuclear Energy Research, 1000 Wenhua Rd., Jiaan Village, Longtan Dist., Taoyuan City 325, Taiwan)

Abstract

The expression and calculation of transmission loss (TL) play key roles for solving the power system economic dispatch (ED) problem. ED including TL must compute the total TL and incremental transmission loss (ITL) by executing power flow equations. However, solving the power flow equations is time-consuming and may result in divergence by the iteration procedure. This approach is unsuitable for real-time ED in practical power systems. To avoid solving nonlinear power flow equations, most power companies continue to adopt the TL formula in ED. Traditional loss formulas are composed of network parameters and in terms of the generator’s real power outputs. These formulas are derived by several assumptions, but these basic assumptions sacrifice accuracy. In this study, a new expression for the loss formula is proposed to improve the shortcomings of traditional loss formulas. The coefficients in the new loss formula can be obtained by recording the power losses according to varying real and reactive power outputs without any assumptions. The simultaneous equations of the second-order expansion of the Taylor series are then established. Finally, the corresponding coefficients can be calculated by solving the simultaneous equations. These new coefficients can be used in optimal real and reactive power dispatch problems. The proposed approach is tested by IEEE 14-bus and 30-bus systems, and the results are compared with those obtained from the traditional B coefficient method and the load flow method. The numerical results show that the proposed new loss formula for ED can hold high accuracy for different loading conditions and is very suitable for real-time applications.

Suggested Citation

  • Wei-Tzer Huang & Kai-Chao Yao & Ming-Ku Chen & Feng-Ying Wang & Cang-Hui Zhu & Yung-Ruei Chang & Yih-Der Lee & Yuan-Hsiang Ho, 2018. "Derivation and Application of a New Transmission Loss Formula for Power System Economic Dispatch," Energies, MDPI, vol. 11(2), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:2:p:417-:d:131386
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    References listed on IDEAS

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    1. Qun Niu & Zhuo Zhou & Hong-Yun Zhang & Jing Deng, 2012. "An Improved Quantum-Behaved Particle Swarm Optimization Method for Economic Dispatch Problems with Multiple Fuel Options and Valve-Points Effects," Energies, MDPI, vol. 5(9), pages 1-19, September.
    2. Yanzhe Hu & Yang Li & Mengjie Xu & Li Zhou & Mingjian Cui, 2017. "A Chance-Constrained Economic Dispatch Model in Wind-Thermal-Energy Storage System," Energies, MDPI, vol. 10(3), pages 1-21, March.
    3. Wei-Tzer Huang & Kai-Chao Yao & Chun-Ching Wu & Yung-Ruei Chang & Yih-Der Lee & Yuan-Hsiang Ho, 2016. "A Three-Stage Optimal Approach for Power System Economic Dispatch Considering Microgrids," Energies, MDPI, vol. 9(11), pages 1-18, November.
    4. Young-Sik Jang & Mun-Kyeom Kim, 2017. "A Dynamic Economic Dispatch Model for Uncertain Power Demands in an Interconnected Microgrid," Energies, MDPI, vol. 10(3), pages 1-16, March.
    5. Kai Ma & Congshan Wang & Jie Yang & Qiuxia Yang & Yazhou Yuan, 2017. "Economic Dispatch with Demand Response in Smart Grid: Bargaining Model and Solutions," Energies, MDPI, vol. 10(8), pages 1-17, August.
    6. Boyang Qu & Baihao Qiao & Yongsheng Zhu & Jingjing Liang & Ling Wang, 2017. "Dynamic Power Dispatch Considering Electric Vehicles and Wind Power Using Decomposition Based Multi-Objective Evolutionary Algorithm," Energies, MDPI, vol. 10(12), pages 1-28, December.
    7. Jun Xie & Chi Cao, 2017. "Non-Convex Economic Dispatch of a Virtual Power Plant via a Distributed Randomized Gradient-Free Algorithm," Energies, MDPI, vol. 10(7), pages 1-12, July.
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