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

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  • 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
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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
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    3. 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).
    4. 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).

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