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Multi-Stage Bargaining of Smart Grid Energy Trading Based on Cooperative Game Theory

Author

Listed:
  • Nongmaithem Nandini Devi

    (Department of Computer Science and Engineering, National Institute of Technology Meghalaya, Shillong 793003, India
    These authors contributed equally to this work.)

  • Surmila Thokchom

    (Department of Computer Science and Engineering, National Institute of Technology Meghalaya, Shillong 793003, India
    These authors contributed equally to this work.)

  • Thoudam Doren Singh

    (Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar 788010, India
    These authors contributed equally to this work.)

  • Gayadhar Panda

    (Department of Electrical Engineering, National Institute of Technology Meghalaya, Shillong 793003, India
    These authors contributed equally to this work.)

  • Ramasamy Thaiyal Naayagi

    (School of Electrical and Electronic Engineering, Newcastle University in Singapore, Singapore 567739, Singapore)

Abstract

Due to global warming and climate change, it is essential to produce power using renewable sources, such as solar, wind, fuel cells, etc. The traditional grid shifts towards the smart grid by infusing digital communication techniques and information technology. As the current power system is shifting towards a smart grid, the utility and prosumers participate in the energy trading process. Due to the distributed nature of the smart grid, providing a fair price among them is becoming a difficult task. The article introduces a model for energy trading in a smart grid by allowing participants to negotiate in multiple stages using a game-theory-based multi-stage Nash Bargaining Solution (NBS). The model’s application of game theory enables the participants to decide on a mutually acceptable price, thereby encouraging the utility, private parties and prosumers (those who are able to generate and consume energy) to participate in the trading process. Since all parties participate in the trading procedure, greenhouse gas emissions are reduced. The proposed model also balances the benefits of consumers and producers in the final agreed fixed price. To demonstrate the efficacy of the proposed work, we compare the analytical results with feed-in-tariff (FiT) techniques in terms of consumers’ energy bills and producers’ revenue. For experimental analysis, 20 participants are considered, where the percentage reduction in the bill of each consumer and the percentage increment of revenue of each producer are compared to FiT. On average, the overall bill of the consumer is reduced by 32.8 % , and the producers’ revenue is increased by 64.83 % compared to FiT. It has been shown further that the proposed model shows better performance as compared to FiT with an increase in the number of participants. The analysis of carbon emission reduction in the proposed model has been analyzed, where, for 10 participants, the carbon emission reduction is approximately 28.48 kg/kWh, and for 100 participants is 342.397 kg/kWh.

Suggested Citation

  • Nongmaithem Nandini Devi & Surmila Thokchom & Thoudam Doren Singh & Gayadhar Panda & Ramasamy Thaiyal Naayagi, 2023. "Multi-Stage Bargaining of Smart Grid Energy Trading Based on Cooperative Game Theory," Energies, MDPI, vol. 16(11), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4278-:d:1153831
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    References listed on IDEAS

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    1. Robert L. Fares & Michael E. Webber, 2017. "The impacts of storing solar energy in the home to reduce reliance on the utility," Nature Energy, Nature, vol. 2(2), pages 1-10, February.
    2. Tushar, Wayes & Saha, Tapan Kumar & Yuen, Chau & Morstyn, Thomas & McCulloch, Malcolm D. & Poor, H. Vincent & Wood, Kristin L., 2019. "A motivational game-theoretic approach for peer-to-peer energy trading in the smart grid," Applied Energy, Elsevier, vol. 243(C), pages 10-20.
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    Cited by:

    1. Wang He & Min Liu & Chaowen Zuo & Kai Wang, 2023. "Massive Multi-Source Joint Outbound and Benefit Distribution Model Based on Cooperative Game," Energies, MDPI, vol. 16(18), pages 1-19, September.

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