IDEAS home Printed from https://ideas.repec.org/a/eee/oprepe/v5y2018icp161-168.html
   My bibliography  Save this article

Cooperation model in the electricity energy market using bi-level optimization and Shapley value

Author

Listed:
  • Acuña, Luceny Guzmán
  • Ríos, Diana Ramírez
  • Arboleda, Carlos Paternina
  • Ponzón, Esneyder González

Abstract

In this paper, a cooperation model between a generating company and several marketers is presented. The model considers two cooperation schemes. The first finds the optimal decision for the generating company and the group of marketers in terms of maximization of their profits, based on bi-level optimization. Second scheme proposes the cooperation among the marketers, whose objective is to serve a common set of consumers and to increase their profits through cooperation, with respect to the profit gained individually. Profit of the marketers group are divided among them, based on the Shapley value. The model was solved using GAMS and Visual Studio Tools for Office and was validated through a case study in a region in Colombia. The results of the study showed that implementing these cooperation structures brings additional economic benefits to the cooperating agents.

Suggested Citation

  • Acuña, Luceny Guzmán & Ríos, Diana Ramírez & Arboleda, Carlos Paternina & Ponzón, Esneyder González, 2018. "Cooperation model in the electricity energy market using bi-level optimization and Shapley value," Operations Research Perspectives, Elsevier, vol. 5(C), pages 161-168.
    • Handle: RePEc:eee:oprepe:v:5:y:2018:i:c:p:161-168
    DOI: 10.1016/j.orp.2018.07.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S221471601830037X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.orp.2018.07.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kovacevic, Raimund M. & Pflug, Georg Ch., 2014. "Electricity swing option pricing by stochastic bilevel optimization: A survey and new approaches," European Journal of Operational Research, Elsevier, vol. 237(2), pages 389-403.
    2. Grimm, Veronika & Martin, Alexander & Schmidt, Martin & Weibelzahl, Martin & Zöttl, Gregor, 2016. "Transmission and generation investment in electricity markets: The effects of market splitting and network fee regimes," European Journal of Operational Research, Elsevier, vol. 254(2), pages 493-509.
    3. Oliveira, Fernando S. & Ruiz, Carlos & Conejo, Antonio J., 2013. "Contract design and supply chain coordination in the electricity industry," European Journal of Operational Research, Elsevier, vol. 227(3), pages 527-537.
    4. Anthony Shorrocks, 2013. "Decomposition procedures for distributional analysis: a unified framework based on the Shapley value," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 11(1), pages 99-126, March.
    5. Zhang, Di & Samsatli, Nouri J. & Hawkes, Adam D. & Brett, Dan J.L. & Shah, Nilay & Papageorgiou, Lazaros G., 2013. "Fair electricity transfer price and unit capacity selection for microgrids," Energy Economics, Elsevier, vol. 36(C), pages 581-593.
    6. Su, Wencong & Huang, Alex Q., 2014. "A game theoretic framework for a next-generation retail electricity market with high penetration of distributed residential electricity suppliers," Applied Energy, Elsevier, vol. 119(C), pages 341-350.
    7. Min, C.G. & Kim, M.K. & Park, J.K. & Yoon, Y.T., 2013. "Game-theory-based generation maintenance scheduling in electricity markets," Energy, Elsevier, vol. 55(C), pages 310-318.
    8. Zhang, Ni & Yan, Yu & Su, Wencong, 2015. "A game-theoretic economic operation of residential distribution system with high participation of distributed electricity prosumers," Applied Energy, Elsevier, vol. 154(C), pages 471-479.
    9. Alipour, Manijeh & Zare, Kazem & Seyedi, Heresh, 2018. "A multi-follower bilevel stochastic programming approach for energy management of combined heat and power micro-grids," Energy, Elsevier, vol. 149(C), pages 135-146.
    10. Steven Gabriel & Sauleh Siddiqui & Antonio Conejo & Carlos Ruiz, 2013. "Solving Discretely-Constrained Nash–Cournot Games with an Application to Power Markets," Networks and Spatial Economics, Springer, vol. 13(3), pages 307-326, September.
    11. Beibei Wang & Xin Fang & Xiayang Zhao & Houhe Chen, 2015. "Bi-Level Optimization for Available Transfer Capability Evaluation in Deregulated Electricity Market," Energies, MDPI, vol. 8(12), pages 1-17, November.
    12. Wei, F. & Jing, Z.X. & Wu, Peter Z. & Wu, Q.H., 2017. "A Stackelberg game approach for multiple energies trading in integrated energy systems," Applied Energy, Elsevier, vol. 200(C), pages 315-329.
    13. Lo Prete, Chiara & Hobbs, Benjamin F., 2016. "A cooperative game theoretic analysis of incentives for microgrids in regulated electricity markets," Applied Energy, Elsevier, vol. 169(C), pages 524-541.
    14. Banez-Chicharro, Fernando & Olmos, Luis & Ramos, Andres & Latorre, Jesus M., 2017. "Estimating the benefits of transmission expansion projects: An Aumann-Shapley approach," Energy, Elsevier, vol. 118(C), pages 1044-1054.
    15. Zugno, Marco & Morales, Juan Miguel & Pinson, Pierre & Madsen, Henrik, 2013. "A bilevel model for electricity retailers' participation in a demand response market environment," Energy Economics, Elsevier, vol. 36(C), pages 182-197.
    16. Wu, Qiong & Ren, Hongbo & Gao, Weijun & Ren, Jianxing & Lao, Changshi, 2017. "Profit allocation analysis among the distributed energy network participants based on Game-theory," Energy, Elsevier, vol. 118(C), pages 783-794.
    17. Pinto, T. & Morais, H. & Oliveira, P. & Vale, Z. & Praça, I. & Ramos, C., 2011. "A new approach for multi-agent coalition formation and management in the scope of electricity markets," Energy, Elsevier, vol. 36(8), pages 5004-5015.
    18. Mazidi, Peyman & Tohidi, Yaser & Ramos, Andres & Sanz-Bobi, Miguel A., 2018. "Profit-maximization generation maintenance scheduling through bi-level programming," European Journal of Operational Research, Elsevier, vol. 264(3), pages 1045-1057.
    19. Li, Guoqing & Zhang, Rufeng & Jiang, Tao & Chen, Houhe & Bai, Linquan & Li, Xiaojing, 2017. "Security-constrained bi-level economic dispatch model for integrated natural gas and electricity systems considering wind power and power-to-gas process," Applied Energy, Elsevier, vol. 194(C), pages 696-704.
    20. Lozano, S. & Moreno, P. & Adenso-Díaz, B. & Algaba, E., 2013. "Cooperative game theory approach to allocating benefits of horizontal cooperation," European Journal of Operational Research, Elsevier, vol. 229(2), pages 444-452.
    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. Churkin, Andrey & Bialek, Janusz & Pozo, David & Sauma, Enzo & Korgin, Nikolay, 2021. "Review of Cooperative Game Theory applications in power system expansion planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    2. Tseng, Chin-Yi & Lee, Chia-Yen & Wang, Qunwei & Wu, Changsong, 2022. "Data envelopment analysis and stochastic equilibrium analysis for market power investigation in a bi-level market," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    3. Fuentes González, Fabián & Sauma, Enzo & van der Weijde, Adriaan Hendrik, 2022. "Community energy projects in the context of generation and transmission expansion planning," Energy Economics, Elsevier, vol. 108(C).

    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. Wu, Qiong & Ren, Hongbo & Gao, Weijun & Ren, Jianxing, 2017. "Benefit allocation for distributed energy network participants applying game theory based solutions," Energy, Elsevier, vol. 119(C), pages 384-391.
    2. Wu, Qiong & Ren, Hongbo & Gao, Weijun & Ren, Jianxing & Lao, Changshi, 2017. "Profit allocation analysis among the distributed energy network participants based on Game-theory," Energy, Elsevier, vol. 118(C), pages 783-794.
    3. Andoni, Merlinda & Robu, Valentin & Früh, Wolf-Gerrit & Flynn, David, 2017. "Game-theoretic modeling of curtailment rules and network investments with distributed generation," Applied Energy, Elsevier, vol. 201(C), pages 174-187.
    4. Motalleb, Mahdi & Annaswamy, Anuradha & Ghorbani, Reza, 2018. "A real-time demand response market through a repeated incomplete-information game," Energy, Elsevier, vol. 143(C), pages 424-438.
    5. Yiqi Dong & Zuoji Dong, 2023. "Bibliometric Analysis of Game Theory on Energy and Natural Resource," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    6. Bhatti, Bilal Ahmad & Broadwater, Robert, 2019. "Energy trading in the distribution system using a non-model based game theoretic approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    7. Bhatti, Bilal Ahmad & Broadwater, Robert, 2020. "Distributed Nash Equilibrium Seeking for a Dynamic Micro-grid Energy Trading Game with Non-quadratic Payoffs," Energy, Elsevier, vol. 202(C).
    8. Motalleb, Mahdi & Ghorbani, Reza, 2017. "Non-cooperative game-theoretic model of demand response aggregator competition for selling stored energy in storage devices," Applied Energy, Elsevier, vol. 202(C), pages 581-596.
    9. Jing, Rui & Xie, Mei Na & Wang, Feng Xiang & Chen, Long Xiang, 2020. "Fair P2P energy trading between residential and commercial multi-energy systems enabling integrated demand-side management," Applied Energy, Elsevier, vol. 262(C).
    10. Martin Weibelzahl & Alexandra Märtz, 2020. "Optimal storage and transmission investments in a bilevel electricity market model," Annals of Operations Research, Springer, vol. 287(2), pages 911-940, April.
    11. Tang, Yanyan & Zhang, Qi & Li, Yaoming & Li, Hailong & Pan, Xunzhang & Mclellan, Benjamin, 2019. "The social-economic-environmental impacts of recycling retired EV batteries under reward-penalty mechanism," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    12. Wang, Shouxiang & Wang, Shaomin & Zhao, Qianyu & Dong, Shuai & Li, Hao, 2023. "Optimal dispatch of integrated energy station considering carbon capture and hydrogen demand," Energy, Elsevier, vol. 269(C).
    13. Wang, Can & Yan, Chao & Li, Gengfeng & Liu, Shiyu & Bie, Zhaohong, 2020. "Risk assessment of integrated electricity and heat system with independent energy operators based on Stackelberg game," Energy, Elsevier, vol. 198(C).
    14. Akhil Joseph & Patil Balachandra, 2020. "Energy Internet, the Future Electricity System: Overview, Concept, Model Structure, and Mechanism," Energies, MDPI, vol. 13(16), pages 1-26, August.
    15. Longxi Li, 2020. "Optimal Coordination Strategies for Load Service Entity and Community Energy Systems Based on Centralized and Decentralized Approaches," Energies, MDPI, vol. 13(12), pages 1-22, June.
    16. Yong Wang & Shouguo Peng & Kevin Assogba & Yong Liu & Haizhong Wang & Maozeng Xu & Yinhai Wang, 2018. "Implementation of Cooperation for Recycling Vehicle Routing Optimization in Two-Echelon Reverse Logistics Networks," Sustainability, MDPI, vol. 10(5), pages 1-27, April.
    17. Jin, Yuhui & Chang, Chuei-Tin & Li, Shaojun & Jiang, Da, 2018. "On the use of risk-based Shapley values for cost sharing in interplant heat integration programs," Applied Energy, Elsevier, vol. 211(C), pages 904-920.
    18. Wang, Yifei & Wang, Xiuli & Shao, Chengcheng & Gong, Naiwei, 2020. "Distributed energy trading for an integrated energy system and electric vehicle charging stations: A Nash bargaining game approach," Renewable Energy, Elsevier, vol. 155(C), pages 513-530.
    19. Morteza Nazari-Heris & Behnam Mohammadi-Ivatloo & Somayeh Asadi, 2020. "Optimal Operation of Multi-Carrier Energy Networks Considering Uncertain Parameters and Thermal Energy Storage," Sustainability, MDPI, vol. 12(12), pages 1-20, June.
    20. Banez-Chicharro, Fernando & Olmos, Luis & Ramos, Andres & Latorre, Jesus M., 2017. "Beneficiaries of transmission expansion projects of an expansion plan: An Aumann-Shapley approach," Applied Energy, Elsevier, vol. 195(C), pages 382-401.

    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:eee:oprepe:v:5:y:2018:i:c:p:161-168. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/operations-research-perspectives .

    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.