Peer-to-peer energy trading optimization for community prosumers considering carbon cap-and-trade

Wu, Chun; Chen, Xingying; Hua, Haochen; Yu, Kun; Gan, Lei; Shen, Jun; Ding, Yi

doi:10.1016/j.apenergy.2023.122611

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Applied Energy

2024

DOI: 10.1016/j.apenergy.2023.122611

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Peer-to-peer energy trading optimization for community prosumers considering carbon cap-and-trade

Chun Wu,

Xingying Chen,

Haochen Hua

et al.

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Cited by 4 publications

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Developing Resilient Peer-to-Peer Energy Sharing Scheme Using “N-1” Contingency

Kiu,

Kong,

Andiappan

et al. 2024

Process Integr Optim Sustain

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A peer-to-peer energy sharing system, aiming to achieve emissions reduction by generating and sharing renewable energy among participants, has emerged as an appealing solution for addressing carbon emissions attributed to energy consumption. However, the resiliency of the peer-to-peer energy sharing system, particularly in addressing the risks of disruptions, has yet to be studied thoroughly in existing literature. Hence, this work aims to design an optimal peer-to-peer energy sharing scheme that can reduce electrical costs, while ensuring the carbon emissions reduction goal can be achieved even in the presence of disruptive risks. The concept of N-1 contingency has been incorporated into the proposed methodology. With the proposed approach, the minimum renewable capacity requirement of each player can be determined to ensure the fulfillment of energy demand and emissions reduction goal even when any of the players are unexpectedly shut down. Generally, by expanding the size of the solar panel, the remaining players are able to share extra renewables to fulfill its energy demand while achieving the emissions reduction goal. To demonstrate the effectiveness of the proposed methodology, an illustrative case study with three energy players is introduced. The result shows that if N-1 contingency is not applied, both energy cost and total emissions have at least increased by 35,054.49 RM/month (10.81%) and 46,218.34 kg/month (19.47%), respectively, assuming one of the players is experiencing disruptions. In short, this work reveals the potential of applying N-1 contingency to enhance the robustness of peer-to-peer system while ensuring its stability and performance even under challenging conditions.

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Developing Resilient Peer-to-Peer Energy Sharing Scheme Using “N-1” Contingency

Kiu,

Kong,

Andiappan

et al. 2024

Process Integr Optim Sustain

View full text Add to dashboard Cite

show abstract

Effect of carbon quota policies on clean-energy investment strategies of capacity-constrained enterprises

Chen,

Luo,

Govindan

et al. 2024

International Journal of Production Economics

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Optimal load forecasting and scheduling strategies for smart homes peer-to-peer energy networks: A comprehensive survey with critical simulation analysis

Raza,

Jingzhao,

Adnan

et al. 2024

Results in Engineering

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Peer-to-peer energy trading optimization for community prosumers considering carbon cap-and-trade

Cited by 4 publications

References 59 publications

Developing Resilient Peer-to-Peer Energy Sharing Scheme Using “N-1” Contingency

Developing Resilient Peer-to-Peer Energy Sharing Scheme Using “N-1” Contingency

Effect of carbon quota policies on clean-energy investment strategies of capacity-constrained enterprises

Optimal load forecasting and scheduling strategies for smart homes peer-to-peer energy networks: A comprehensive survey with critical simulation analysis

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