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Peer-to-peer energy trading for improving economic and resilient operation of microgrids

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  • Spiliopoulos, Nikolas
  • Sarantakos, Ilias
  • Nikkhah, Saman
  • Gkizas, George
  • Giaouris, Damian
  • Taylor, Phil
  • Rajarathnam, Uma
  • Wade, Neal

Abstract

Peer-to-peer (P2P) energy exchange is a popular market mechanism which enables the transaction of energy in local communities. The advantages of this method for different stakeholders has been demonstrated in the literature. However, the effect of P2P energy exchange on economic and resilient operation of microgrids (MGs) has not been studied yet. This paper presents a P2P energy exchange framework for improving economic and resilient operation of MGs. The proposed method considers the synergistic benefits of system operator and end-users in terms of: a) economic benefit from participation in the market, b) system resilience improvement, c) battery lifetime improvement, and d) carbon emissions reduction. The proposed method optimizes the battery sizing and operation, while categorizing and prioritizing the end-users. In order to evaluate the effectiveness of the proposed method, it has been applied to four different geographical locations with various Time-of-Use tariff schemes, and is tested against different fault scenarios. The results show that the proposed method can improve the resilience up to 80%, while battery lifetime can be prolonged by 32%–37%.

Suggested Citation

  • Spiliopoulos, Nikolas & Sarantakos, Ilias & Nikkhah, Saman & Gkizas, George & Giaouris, Damian & Taylor, Phil & Rajarathnam, Uma & Wade, Neal, 2022. "Peer-to-peer energy trading for improving economic and resilient operation of microgrids," Renewable Energy, Elsevier, vol. 199(C), pages 517-535.
    • Handle: RePEc:eee:renene:v:199:y:2022:i:c:p:517-535
    DOI: 10.1016/j.renene.2022.08.061
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    6. Dong, Min & Su, Juan & Zhao, Jing & Dong, Yanjun & Du, Songhuai, 2024. "Fraudulent balancing operation strategy for multi-agent P2P electricity trading considering neighborhood scene public energy storage," Applied Energy, Elsevier, vol. 375(C).
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    8. Osman Akbulut & Muhammed Cavus & Mehmet Cengiz & Adib Allahham & Damian Giaouris & Matthew Forshaw, 2024. "Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques," Energies, MDPI, vol. 17(10), pages 1-23, May.

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