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<p>This paper presents a secure and efficient peer-to-peer (P2P) energy trading framework that integrates data privacy, optimized energy allocation, and tamper-proof financial settlement. The proposed model ensures privacy of participants’ energy and price information using additive secret sharing with message authentication codes, enabling verification against data tampering during transmission. Based on the reconstructed information, the market operator (MO) determines the final trading price (FTP) through a negotiation process, accommodating both buyers’ and sellers’ market modes. Energy allocation is optimized using a modified Vogel’s Approximation Method (VAM), ensuring fairness, minimizing buyers’ energy bills, and maximizing sellers’ satisfaction while respecting supply-demand constraints. The financial transactions are executed via a blockchain-enabled model, providing secure, transparent, and immutable settlement without requiring multiple contracts between participants. The framework is evaluated on a 4-year real-time dataset of 22 participants, demonstrating effective data privacy enforcement, accurate price determination, optimized energy allocation, and significant reduction in buyers’ bills with increased sellers’ revenues compared to existing models. The results validate the proposed model’s efficiency, scalability, and practical applicability in real-time smart grid operations.</p>

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A Secure & reserved pricing mechanism for peer-to-peer energy trading using additive secret sharing and blockchain

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月30日

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  • Iqra Nazir,
  • Nermish Mushtaq,
  • Hassam Ishfaq,
  • Sania Kanwal,
  • Waqas Amin,
  • Muhammad Afzal

摘要

This paper presents a secure and efficient peer-to-peer (P2P) energy trading framework that integrates data privacy, optimized energy allocation, and tamper-proof financial settlement. The proposed model ensures privacy of participants’ energy and price information using additive secret sharing with message authentication codes, enabling verification against data tampering during transmission. Based on the reconstructed information, the market operator (MO) determines the final trading price (FTP) through a negotiation process, accommodating both buyers’ and sellers’ market modes. Energy allocation is optimized using a modified Vogel’s Approximation Method (VAM), ensuring fairness, minimizing buyers’ energy bills, and maximizing sellers’ satisfaction while respecting supply-demand constraints. The financial transactions are executed via a blockchain-enabled model, providing secure, transparent, and immutable settlement without requiring multiple contracts between participants. The framework is evaluated on a 4-year real-time dataset of 22 participants, demonstrating effective data privacy enforcement, accurate price determination, optimized energy allocation, and significant reduction in buyers’ bills with increased sellers’ revenues compared to existing models. The results validate the proposed model’s efficiency, scalability, and practical applicability in real-time smart grid operations.