IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i10p2498-d171098.html
   My bibliography  Save this article

Demand Bidding Optimization for an Aggregator with a Genetic Algorithm

Author

Listed:
  • Leehter Yao

    (Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Wei Hong Lim

    (Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia)

  • Sew Sun Tiang

    (Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia)

  • Teng Hwang Tan

    (Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia)

  • Chin Hong Wong

    (Department of Engineering and Information Technology, UCSI College, Kuala Lumpur 56000, Malaysia)

  • Jia Yew Pang

    (School of Engineering and Physical Sciences, Heriot Watt University, Putrajaya 62200, Malaysia)

Abstract

Demand response (DR) is an effective solution used to maintain the reliability of power systems. Although numerous demand bidding models were designed to balance the demand and supply of electricity, these works focused on optimizing the DR supply curve of aggregator and the associated clearing prices. Limited researches were done to investigate the interaction between each aggregator and its customers to ensure the delivery of promised load curtailments. In this paper, a closed demand bidding model is envisioned to bridge the aforementioned gap by facilitating the internal DR trading between the aggregator and its large contract customers. The customers can submit their own bid as a pairs of bidding price and quantity of load curtailment in hourly basis when demand bidding is needed. A purchase optimization scheme is then designed to minimize the total bidding purchase cost. Given the presence of various load curtailment constraints, the demand bidding model considered is highly nonlinear. A modified genetic algorithm incorporated with efficient encoding scheme and adaptive bid declination strategy is therefore proposed to solve this problem effectively. Extensive simulation shows that the proposed purchase optimization scheme can minimize the total cost of demand bidding and it is computationally feasible for real applications.

Suggested Citation

  • Leehter Yao & Wei Hong Lim & Sew Sun Tiang & Teng Hwang Tan & Chin Hong Wong & Jia Yew Pang, 2018. "Demand Bidding Optimization for an Aggregator with a Genetic Algorithm," Energies, MDPI, vol. 11(10), pages 1-22, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2498-:d:171098
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/10/2498/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/10/2498/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Deng, S.J. & Oren, S.S., 2006. "Electricity derivatives and risk management," Energy, Elsevier, vol. 31(6), pages 940-953.
    2. Leehter Yao & Lei Yao & Wei Hong Lim, 2018. "A Soft Curtailment of Wide-Area Central Air Conditioning Load," Energies, MDPI, vol. 11(3), pages 1-15, February.
    3. Sezgen, Osman & Goldman, C.A. & Krishnarao, P., 2007. "Option value of electricity demand response," Energy, Elsevier, vol. 32(2), pages 108-119.
    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. Younes Zahraoui & Tarmo Korõtko & Argo Rosin & Hannes Agabus, 2023. "Market Mechanisms and Trading in Microgrid Local Electricity Markets: A Comprehensive Review," Energies, MDPI, vol. 16(5), pages 1-52, February.
    2. Francisco G. Montoya & Raúl Baños & Alfredo Alcayde & Francisco Manzano-Agugliaro, 2019. "Optimization Methods Applied to Power Systems," Energies, MDPI, vol. 12(12), pages 1-8, June.
    3. Zixu Liu & Xiaojun Zeng & Fanlin Meng, 2018. "An Integration Mechanism between Demand and Supply Side Management of Electricity Markets," Energies, MDPI, vol. 11(12), pages 1-23, November.

    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. Deng, Shi-Jie & Xu, Li, 2009. "Mean-risk efficient portfolio analysis of demand response and supply resources," Energy, Elsevier, vol. 34(10), pages 1523-1529.
    2. Moore, J. & Woo, C.K. & Horii, B. & Price, S. & Olson, A., 2010. "Estimating the option value of a non-firm electricity tariff," Energy, Elsevier, vol. 35(4), pages 1609-1614.
    3. Feng, Zongbao & Wu, Xianguo & Chen, Hongyu & Qin, Yawei & Zhang, Limao & Skibniewski, Miroslaw J., 2022. "An energy performance contracting parameter optimization method based on the response surface method: A case study of a metro in China," Energy, Elsevier, vol. 248(C).
    4. Jacobs, Kris & Li, Yu & Pirrong, Craig, 2022. "Supply, demand, and risk premiums in electricity markets," Journal of Banking & Finance, Elsevier, vol. 135(C).
    5. Woo, C.K. & Zarnikau, J. & Moore, J. & Horowitz, I., 2011. "Wind generation and zonal-market price divergence: Evidence from Texas," Energy Policy, Elsevier, vol. 39(7), pages 3928-3938, July.
    6. Kursad Derinkuyu & Fehmi Tanrisever & Nermin Kurt & Gokhan Ceyhan, 2020. "Optimizing Day-Ahead Electricity Market Prices: Increasing the Total Surplus for Energy Exchange Istanbul," Manufacturing & Service Operations Management, INFORMS, vol. 22(4), pages 700-716, July.
    7. Ricardo Raineri, 2025. "Power Shift: Decarbonization and the New Dynamics of Energy Markets," Energies, MDPI, vol. 18(3), pages 1-52, February.
    8. Moret, Fabio & Pinson, Pierre & Papakonstantinou, Athanasios, 2020. "Heterogeneous risk preferences in community-based electricity markets," European Journal of Operational Research, Elsevier, vol. 287(1), pages 36-48.
    9. Russo, Marianna & Bertsch, Valentin, 2020. "A looming revolution: Implications of self-generation for the risk exposure of retailers," Energy Economics, Elsevier, vol. 92(C).
    10. Markus Hang & Jerome Geyer-Klingeberg & Andreas W. Rathgeber & Clémence Alasseur & Lena Wichmann, 2021. "Interaction effects of corporate hedging activities for a multi-risk exposure: evidence from a quasi-natural experiment," Review of Quantitative Finance and Accounting, Springer, vol. 56(2), pages 789-818, February.
    11. Blanco, Jesús M. & Vazquez, L. & Peña, F., 2012. "Investigation on a new methodology for thermal power plant assessment through live diagnosis monitoring of selected process parameters; application to a case study," Energy, Elsevier, vol. 42(1), pages 170-180.
    12. Ethem Çanakoğlu & Esra Adıyeke, 2020. "Comparison of Electricity Spot Price Modelling and Risk Management Applications," Energies, MDPI, vol. 13(18), pages 1-22, September.
    13. Munoz, Francisco D. & van der Weijde, Adriaan Hendrik & Hobbs, Benjamin F. & Watson, Jean-Paul, 2017. "Does risk aversion affect transmission and generation planning? A Western North America case study," Energy Economics, Elsevier, vol. 64(C), pages 213-225.
    14. Petrella, Andrea & Sapio, Alessandro, 2012. "Assessing the impact of forward trading, retail liberalization, and white certificates on the Italian wholesale electricity prices," Energy Policy, Elsevier, vol. 40(C), pages 307-317.
    15. Kim, Kyoung-Kuk & Lee, Chi-Guhn, 2012. "Evaluation and optimization of feed-in tariffs," Energy Policy, Elsevier, vol. 49(C), pages 192-203.
    16. Brown, D.P. & Tsai, C.H. & Woo, C.K. & Zarnikau, J. & Zhu, S., 2020. "Residential electricity pricing in Texas's competitive retail market," Energy Economics, Elsevier, vol. 92(C).
    17. Zare, Kazem & Moghaddam, Mohsen Parsa & Sheikh El Eslami, Mohammad Kazem, 2010. "Electricity procurement for large consumers based on Information Gap Decision Theory," Energy Policy, Elsevier, vol. 38(1), pages 234-242, January.
    18. Castellini, Marta & Menoncin, Francesco & Moretto, Michele & Vergalli, Sergio, 2021. "Photovoltaic Smart Grids in the prosumers investment decisions: a real option model," Journal of Economic Dynamics and Control, Elsevier, vol. 126(C).
    19. Shabanzadeh, Morteza & Sheikh-El-Eslami, Mohammad-Kazem & Haghifam, Mahmoud-Reza, 2016. "A medium-term coalition-forming model of heterogeneous DERs for a commercial virtual power plant," Applied Energy, Elsevier, vol. 169(C), pages 663-681.
    20. Woo, C.K. & Chen, Y. & Olson, A. & Moore, J. & Schlag, N. & Ong, A. & Ho, T., 2017. "Electricity price behavior and carbon trading: New evidence from California," Applied Energy, Elsevier, vol. 204(C), pages 531-543.

    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:11:y:2018:i:10:p:2498-:d:171098. 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.