IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v245y2022ics0360544222001086.html
   My bibliography  Save this article

Coordinated optimal bidding strategies methods of aggregated microgrids: A game theory-based demand side management under an electricity market environment

Author

Listed:
  • Saeian, Hosein
  • Niknam, Taher
  • Zare, Mohsen
  • Aghaei, Jamshid

Abstract

Due to substantial demand of expansion and application of distributed generations and energy storage systems (ESSs), the operation of microgrids (MGs) has become more flexible but more complicated with the role of active network loads. To deploying these potentials of MGs, in this paper, a novel demand side management (DSM) method based on the game theory with an effective payoff function is developed in a scrutinized manner. In order to be more compatible with reality, the interactions between some MGs are optimized in an electricity market environment using an evolutionary method. To implement this model, in the first step, each microgrid operator (MGO) tries to maximize its benefit considering the payoff function in an iterative process deploying the proposed game theory method. In this step, a new strategy is reconciled to DSM and its payoff function to reach the Nash equilibrium, effectively. The aggregated load demand of MGs will be participated in market mechanism by MGO through a Gray Wolf Algorithm (GWA) to maximize the MGO's profit. The results confirm the compatibility between DSM and game theory to find the Nash equilibrium point. Furthermore, the price-maker strategy makes more profit compared to price-taker one form the MGO point of view.

Suggested Citation

  • Saeian, Hosein & Niknam, Taher & Zare, Mohsen & Aghaei, Jamshid, 2022. "Coordinated optimal bidding strategies methods of aggregated microgrids: A game theory-based demand side management under an electricity market environment," Energy, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:energy:v:245:y:2022:i:c:s0360544222001086
    DOI: 10.1016/j.energy.2022.123205
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222001086
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.123205?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. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation," Applied Energy, Elsevier, vol. 99(C), pages 455-470.
    2. Hasankhani, Arezoo & Hakimi, Seyed Mehdi, 2021. "Stochastic energy management of smart microgrid with intermittent renewable energy resources in electricity market," Energy, Elsevier, vol. 219(C).
    3. Hakimi, Seyed Mehdi & Hasankhani, Arezoo & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Stochastic planning of a multi-microgrid considering integration of renewable energy resources and real-time electricity market," Applied Energy, Elsevier, vol. 298(C).
    4. Tabar, Vahid Sohrabi & Ghassemzadeh, Saeid & Tohidi, Sajjad, 2019. "Energy management in hybrid microgrid with considering multiple power market and real time demand response," Energy, Elsevier, vol. 174(C), pages 10-23.
    5. MansourLakouraj, Mohammad & Shahabi, Majid & Shafie-khah, Miadreza & Ghoreishi, Niloofar & Catalão, João P.S., 2020. "Optimal power management of dependent microgrid considering distribution market and unused power capacity," Energy, Elsevier, vol. 200(C).
    6. Aghamohammadloo, Hossein & Talaeizadeh, Valiollah & Shahanaghi, Kamran & Aghaei, Jamshid & Shayanfar, Heidarali & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Integrated Demand Response programs and energy hubs retail energy market modelling," Energy, Elsevier, vol. 234(C).
    7. Niknam, Taher & Golestaneh, Faranak & Malekpour, Ahmadreza, 2012. "Probabilistic energy and operation management of a microgrid containing wind/photovoltaic/fuel cell generation and energy storage devices based on point estimate method and self-adaptive gravitational," Energy, Elsevier, vol. 43(1), pages 427-437.
    8. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Demand response in smart electricity grids equipped with renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 64-72.
    9. Chen, J.J. & Qi, B.X. & Rong, Z.K. & Peng, K. & Zhao, Y.L. & Zhang, X.H., 2021. "Multi-energy coordinated microgrid scheduling with integrated demand response for flexibility improvement," Energy, Elsevier, vol. 217(C).
    10. Qin, Zhijun & Mo, Yuhong & Liu, Hui & Zhang, Yihui, 2021. "Operational flexibility enhancements using mobile energy storage in day-ahead electricity market by game-theoretic approach," Energy, Elsevier, vol. 232(C).
    11. Khojasteh, Meysam & Faria, Pedro & Vale, Zita, 2022. "A robust model for aggregated bidding of energy storages and wind resources in the joint energy and reserve markets," Energy, Elsevier, vol. 238(PB).
    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. Wu, Shengyang & Ding, Zhaohao & Wang, Jingyu & Shi, Dongyuan, 2023. "Unveiling bidding uncertainties in electricity markets: A Bayesian deep learning framework based on accurate variational inference," Energy, Elsevier, vol. 276(C).
    2. R, Revathi & N, Senthilnathan & V, Kumar Chinnaiyan, 2024. "Hybrid optimization approach for power scheduling with PV-battery system in smart grids," Energy, Elsevier, vol. 290(C).
    3. Wu, Jiahui & Wang, Jidong & Kong, Xiangyu, 2022. "Strategic bidding in a competitive electricity market: An intelligent method using Multi-Agent Transfer Learning based on reinforcement learning," Energy, Elsevier, vol. 256(C).
    4. Zhang, Yang & Yang, Qingyu & Li, Donghe & An, Dou, 2022. "A reinforcement and imitation learning method for pricing strategy of electricity retailer with customers’ flexibility," Applied Energy, Elsevier, vol. 323(C).
    5. Feng, Yuanhao & Feng, Donghan & Zhou, Yun & Xu, Shaolun, 2024. "Generation side strategy and user side cost based on equilibrium analysis of the power market under the reliability option," Energy, Elsevier, vol. 287(C).
    6. Yanfang Hou & Hui Tian, 2023. "Research on the Dynamic Characteristics of Photovoltaic Power Production and Sales Based on Game Theory," Sustainability, MDPI, vol. 15(19), pages 1-19, October.

    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. Fazlalipour, Pary & Ehsan, Mehdi & Mohammadi-Ivatloo, Behnam, 2019. "Risk-aware stochastic bidding strategy of renewable micro-grids in day-ahead and real-time markets," Energy, Elsevier, vol. 171(C), pages 689-700.
    2. Jani, Ali & Karimi, Hamid & Jadid, Shahram, 2022. "Two-layer stochastic day-ahead and real-time energy management of networked microgrids considering integration of renewable energy resources," Applied Energy, Elsevier, vol. 323(C).
    3. Ahmad Khan, Aftab & Naeem, Muhammad & Iqbal, Muhammad & Qaisar, Saad & Anpalagan, Alagan, 2016. "A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1664-1683.
    4. Elkholy, M.H. & Metwally, Hamid & Farahat, M.A. & Senjyu, Tomonobu & Elsayed Lotfy, Mohammed, 2022. "Smart centralized energy management system for autonomous microgrid using FPGA," Applied Energy, Elsevier, vol. 317(C).
    5. Sharma, Sharmistha & Bhattacharjee, Subhadeep & Bhattacharya, Aniruddha, 2018. "Probabilistic operation cost minimization of Micro-Grid," Energy, Elsevier, vol. 148(C), pages 1116-1139.
    6. Khojasteh, Meysam & Faria, Pedro & Lezama, Fernando & Vale, Zita, 2023. "A hierarchy model to use local resources by DSO and TSO in the balancing market," Energy, Elsevier, vol. 267(C).
    7. Arriet, Andrea & Matis, Timothy I. & Feijoo, Felipe, 2024. "Electricity sector impacts of water taxation for natural gas supply under high renewable generation," Energy, Elsevier, vol. 294(C).
    8. Zhang, Chao & Yin, Wanjun & Wen, Tao, 2024. "An advanced multi-objective collaborative scheduling strategy for large scale EV charging and discharging connected to the predictable wind power grid," Energy, Elsevier, vol. 287(C).
    9. Bahmani-Firouzi, Bahman & Farjah, Ebrahim & Azizipanah-Abarghooee, Rasoul, 2013. "An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimization approach for dynamic economic emission dispatch considering load and wind power uncertainties," Energy, Elsevier, vol. 50(C), pages 232-244.
    10. Sai, Wei & Pan, Zehua & Liu, Siyu & Jiao, Zhenjun & Zhong, Zheng & Miao, Bin & Chan, Siew Hwa, 2023. "Event-driven forecasting of wholesale electricity price and frequency regulation price using machine learning algorithms," Applied Energy, Elsevier, vol. 352(C).
    11. Elsied, Moataz & Oukaour, Amrane & Gualous, Hamid & Hassan, Radwan, 2015. "Energy management and optimization in microgrid system based on green energy," Energy, Elsevier, vol. 84(C), pages 139-151.
    12. Izadbakhsh, Maziar & Gandomkar, Majid & Rezvani, Alireza & Ahmadi, Abdollah, 2015. "Short-term resource scheduling of a renewable energy based micro grid," Renewable Energy, Elsevier, vol. 75(C), pages 598-606.
    13. Rabiee, Abdorreza & Sadeghi, Mohammad & Aghaeic, Jamshid & Heidari, Alireza, 2016. "Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV units uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 721-739.
    14. Chen, Xianqing & Dong, Wei & Yang, Qiang, 2022. "Robust optimal capacity planning of grid-connected microgrid considering energy management under multi-dimensional uncertainties," Applied Energy, Elsevier, vol. 323(C).
    15. Meysam Khojasteh & Pedro Faria & Fernando Lezama & Zita Vale, 2023. "A Robust Model for Portfolio Management of Microgrid Operator in the Balancing Market," Energies, MDPI, vol. 16(4), pages 1-12, February.
    16. Talari, Saber & Shafie-khah, Miadreza & Osório, Gerardo J. & Aghaei, Jamshid & Catalão, João P.S., 2018. "Stochastic modelling of renewable energy sources from operators' point-of-view: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1953-1965.
    17. Tostado-Véliz, Marcos & Kamel, Salah & Aymen, Flah & Rezaee Jordehi, Ahmad & Jurado, Francisco, 2022. "A Stochastic-IGDT model for energy management in isolated microgrids considering failures and demand response," Applied Energy, Elsevier, vol. 317(C).
    18. Shafie-khah, M. & Heydarian-Forushani, E. & Golshan, M.E.H. & Siano, P. & Moghaddam, M.P. & Sheikh-El-Eslami, M.K. & Catalão, J.P.S., 2016. "Optimal trading of plug-in electric vehicle aggregation agents in a market environment for sustainability," Applied Energy, Elsevier, vol. 162(C), pages 601-612.
    19. Aslani, Mehrdad & Faraji, Jamal & Hashemi-Dezaki, Hamed & Ketabi, Abbas, 2022. "A novel clustering-based method for reliability assessment of cyber-physical microgrids considering cyber interdependencies and information transmission errors," Applied Energy, Elsevier, vol. 315(C).
    20. Guodong Liu & Zhi Li & Yaosuo Xue & Kevin Tomsovic, 2022. "Microgrid Assisted Design for Remote Areas," Energies, MDPI, vol. 15(10), pages 1-23, May.

    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:energy:v:245:y:2022:i:c:s0360544222001086. 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/energy .

    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.