IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i3p300-d92057.html
   My bibliography  Save this article

A Dynamic Economic Dispatch Model for Uncertain Power Demands in an Interconnected Microgrid

Author

Listed:
  • Young-Sik Jang

    (Technology Strategy Team, Korea Electric Power Corporation, 55, Jeollyeok-ro, Naju-si 58217, Jeollanam-do, Korea)

  • Mun-Kyeom Kim

    (Department of Energy System Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 156-756, Korea)

Abstract

In this paper, we propose a dynamic economic dispatch (DED) model with sharing of responsibility for supply–demand balance under uncertain demands in a microgrid (MG). For developing the proposed model, an energy band operation scheme, including a tie-line flow (TLF) contraction between the main grid and the microgrid (MG), is constructed for preventing considerable changes in the TLFs caused by DED optimization. The proposed scheme generalizes the relationship between TLF contractions and MG operational costs. Moreover, a chance-constrained approach is applied to prevent short- and over-supply risks caused by unpredictable demands in the MG. Based on this approach, it is possible to determine the reasonable ramping capability versus operational cost under uncertain power demands in the MG.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:3:p:300-:d:92057
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/3/300/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/3/300/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Woo-Kyu Chae & Hak-Ju Lee & Jong-Nam Won & Jung-Sung Park & Jae-Eon Kim, 2015. "Design and Field Tests of an Inverted Based Remote MicroGrid on a Korean Island," Energies, MDPI, vol. 8(8), pages 1-18, August.
    2. Sanseverino, Eleonora Riva & Di Silvestre, Maria Luisa & Ippolito, Mariano Giuseppe & De Paola, Alessandra & Lo Re, Giuseppe, 2011. "An execution, monitoring and replanning approach for optimal energy management in microgrids," Energy, Elsevier, vol. 36(5), pages 3429-3436.
    3. Alvaro Cartea & Marcelo Figueroa, 2005. "Pricing in Electricity Markets: A Mean Reverting Jump Diffusion Model with Seasonality," Applied Mathematical Finance, Taylor & Francis Journals, vol. 12(4), pages 313-335.
    4. Ayres, Robert U. & Turton, Hal & Casten, Tom, 2007. "Energy efficiency, sustainability and economic growth," Energy, Elsevier, vol. 32(5), pages 634-648.
    5. Neves, Diana & Brito, Miguel C. & Silva, Carlos A., 2016. "Impact of solar and wind forecast uncertainties on demand response of isolated microgrids," Renewable Energy, Elsevier, vol. 87(P2), pages 1003-1015.
    6. Haydt, Gustavo & Leal, Vítor & Pina, André & Silva, Carlos A., 2011. "The relevance of the energy resource dynamics in the mid/long-term energy planning models," Renewable Energy, Elsevier, vol. 36(11), pages 3068-3074.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Trinadh Pamulapati & Muhammed Cavus & Ishioma Odigwe & Adib Allahham & Sara Walker & Damian Giaouris, 2022. "A Review of Microgrid Energy Management Strategies from the Energy Trilemma Perspective," Energies, MDPI, vol. 16(1), pages 1-34, December.
    2. Xiaogang Guo & Zhejing Bao & Zhijie Li & Wenjun Yan, 2018. "Adaptively Constrained Stochastic Model Predictive Control for the Optimal Dispatch of Microgrid," Energies, MDPI, vol. 11(1), pages 1-17, January.
    3. Byeong-Cheol Jeong & Dong-Hwan Shin & Jae-Beom Im & Jae-Young Park & Young-Jin Kim, 2019. "Implementation of Optimal Two-Stage Scheduling of Energy Storage System Based on Big-Data-Driven Forecasting—An Actual Case Study in a Campus Microgrid," Energies, MDPI, vol. 12(6), pages 1-20, March.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Howlader, Abdul Motin & Izumi, Yuya & Uehara, Akie & Urasaki, Naomitsu & Senjyu, Tomonobu & Yona, Atsushi & Saber, Ahmed Yousuf, 2012. "A minimal order observer based frequency control strategy for an integrated wind-battery-diesel power system," Energy, Elsevier, vol. 46(1), pages 168-178.
    2. Moghaddam, Amjad Anvari & Seifi, Alireza & Niknam, Taher & Alizadeh Pahlavani, Mohammad Reza, 2011. "Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source," Energy, Elsevier, vol. 36(11), pages 6490-6507.
    3. Afanasyev, Dmitriy O. & Fedorova, Elena A. & Popov, Viktor U., 2015. "Fine structure of the price–demand relationship in the electricity market: Multi-scale correlation analysis," Energy Economics, Elsevier, vol. 51(C), pages 215-226.
    4. Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafał, 2013. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," Energy Economics, Elsevier, vol. 39(C), pages 13-27.
    5. Jun Maekawa & Koji Shimada, 2019. "A Speculative Trading Model for the Electricity Market: Based on Japan Electric Power Exchange," Energies, MDPI, vol. 12(15), pages 1-15, July.
    6. Moreno, Manuel & Novales, Alfonso & Platania, Federico, 2019. "Long-term swings and seasonality in energy markets," European Journal of Operational Research, Elsevier, vol. 279(3), pages 1011-1023.
    7. Marcelo G. Figueroa, 2006. "Pricing Multiple Interruptible-Swing Contracts," Birkbeck Working Papers in Economics and Finance 0606, Birkbeck, Department of Economics, Mathematics & Statistics.
    8. Michel Culot & Valérie Goffin & Steve Lawford & Sébastien de Meten & Yves Smeers, 2013. "Practical stochastic modelling of electricity prices," Post-Print hal-01021603, HAL.
    9. Asad, R. & Kazemi, A., 2014. "A novel distributed optimal power sharing method for radial dc microgrids with different distributed energy sources," Energy, Elsevier, vol. 72(C), pages 291-299.
    10. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
    11. Jacobs, Kris & Li, Yu & Pirrong, Craig, 2022. "Supply, demand, and risk premiums in electricity markets," Journal of Banking & Finance, Elsevier, vol. 135(C).
    12. Zachmann, Georg, 2013. "A stochastic fuel switching model for electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 5-13.
    13. Somani, Abhishek, 2012. "Financial risk management and market performance in restructured electric power markets: Theoretical and agent-based test bed studies," ISU General Staff Papers 201201010800003479, Iowa State University, Department of Economics.
    14. Galarneau-Vincent, Rémi & Gauthier, Geneviève & Godin, Frédéric, 2023. "Foreseeing the worst: Forecasting electricity DART spikes," Energy Economics, Elsevier, vol. 119(C).
    15. Steinberger, Julia K. & van Niel, Johan & Bourg, Dominique, 2009. "Profiting from negawatts: Reducing absolute consumption and emissions through a performance-based energy economy," Energy Policy, Elsevier, vol. 37(1), pages 361-370, January.
    16. Volk-Makarewicz, Warren & Borovkova, Svetlana & Heidergott, Bernd, 2022. "Assessing the impact of jumps in an option pricing model: A gradient estimation approach," European Journal of Operational Research, Elsevier, vol. 298(2), pages 740-751.
    17. Shiwen Liu & Hongyuan Li, 2020. "Does Financial Development Increase Urban Electricity Consumption? Evidence from Spatial and Heterogeneity Analysis," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    18. Moritz Wegener & Antonio Isalgué & Anders Malmquist & Andrew Martin, 2019. "3E-Analysis of a Bio-Solar CCHP System for the Andaman Islands, India—A Case Study," Energies, MDPI, vol. 12(6), pages 1-19, March.
    19. Ignatieva Katja, 2014. "A nonparametric model for spot price dynamics and pricing of futures contracts in electricity markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(5), pages 1-23, December.
    20. Caldana, Ruggero & Fusai, Gianluca & Roncoroni, Andrea, 2017. "Electricity forward curves with thin granularity: Theory and empirical evidence in the hourly EPEXspot market," European Journal of Operational Research, Elsevier, vol. 261(2), pages 715-734.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:10:y:2017:i:3:p:300-:d:92057. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.