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Using the Direct Search Method for Optimal Dispatch of Distributed Generation in a Medium-Voltage Microgrid

Author

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  • Wei-Tzer Huang

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

  • Kai-Chao Yao

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

  • Chun-Ching Wu

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

Abstract

This paper proposes a simple and efficient approach for the optimal dispatch in a medium-voltage microgrid (MG) with various types of distributed generation (DG). The fuel costs generated by these DGs are determined using quadratic and linear functions dependent on the types of DGs. Instead of using the traditional Lagrange multiplier method for power system economic dispatch, the proposed direct search method (DSM) approach is able to handle several inequality constraints without introducing any multipliers and furthermore it can solve the non-derivative problems or the fuel cost functions being much more complicated. Accordingly, the DSM is proposed for determining the optimal dispatch of MGs with various types of DG to minimize generation costs under grid-tied and autonomous operations. Results demonstrate that the proposed DSM is a highly suitable and simple approach to determining the optimal dispatch in medium-voltage MGs with various types of DG.

Suggested Citation

  • Wei-Tzer Huang & Kai-Chao Yao & Chun-Ching Wu, 2014. "Using the Direct Search Method for Optimal Dispatch of Distributed Generation in a Medium-Voltage Microgrid," Energies, MDPI, vol. 7(12), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:12:p:8355-8373:d:43491
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    References listed on IDEAS

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    Cited by:

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    2. Qingwu Gong & Jiazhi Lei & Jun Ye, 2016. "Optimal Siting and Sizing of Distributed Generators in Distribution Systems Considering Cost of Operation Risk," Energies, MDPI, vol. 9(1), pages 1-18, January.
    3. Wei-Tzer Huang & Tsai-Hsiang Chen & Hong-Ting Chen & Jhih-Siang Yang & Kuo-Lung Lian & Yung-Ruei Chang & Yih-Der Lee & Yuan-Hsiang Ho, 2015. "A Two-stage Optimal Network Reconfiguration Approach for Minimizing Energy Loss of Distribution Networks Using Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 8(12), pages 1-17, December.
    4. 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.
    5. Cristian Cepeda & Cesar Orozco-Henao & Winston Percybrooks & Juan Diego Pulgarín-Rivera & Oscar Danilo Montoya & Walter Gil-González & Juan Carlos Vélez, 2020. "Intelligent Fault Detection System for Microgrids," Energies, MDPI, vol. 13(5), pages 1-21, March.
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    7. Keshta, H.E. & Ali, A.A. & Saied, E.M. & Bendary, F.M., 2019. "Real-time operation of multi-micro-grids using a multi-agent system," Energy, Elsevier, vol. 174(C), pages 576-590.
    8. Robertas Lukočius & Žilvinas Nakutis & Vytautas Daunoras & Ramūnas Deltuva & Pranas Kuzas & Roma Račkienė, 2018. "An Analysis of the Systematic Error of a Remote Method for a Wattmeter Adjustment Gain Estimation in Smart Grids," Energies, MDPI, vol. 12(1), pages 1-26, December.
    9. Aqsa Naeem & Naveed Ul Hassan & Chau Yuen & S. M. Muyeen, 2019. "Maximizing the Economic Benefits of a Grid-Tied Microgrid Using Solar-Wind Complementarity," Energies, MDPI, vol. 12(3), pages 1-22, January.
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