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Blockchain-Enabled Cross-Chain Coordinated Trading Strategy for Electricity-Carbon-Green Certificate in Virtual Power Plants: Multi-Market Coupling and Low-Carbon Operation Optimization

Author

Listed:
  • Chao Zheng

    (Yunnan Power Dispatching Control Center, Yunnan Power Grid Co., Ltd., Kunming 650011, China)

  • Wei Huang

    (Kunming Power Dispatching Control Center, Kunming Power Supply Bureau, Yunnan Power Grid Co., Ltd., Kunming 650010, China)

  • Suwei Zhai

    (Yunnan Power Dispatching Control Center, Yunnan Power Grid Co., Ltd., Kunming 650011, China)

  • Kaiyan Pan

    (Dongfang Electronics Cooperation, Yantai 264010, China)

  • Xuehao He

    (Electric Power Research Institute, China Southern Power Grid Yunnan Power Grid Co., Ltd., Kunming 650217, China)

  • Xiaojie Liu

    (Dongfang Electronics Cooperation, Yantai 264010, China)

  • Shi Su

    (Electric Power Research Institute, China Southern Power Grid Yunnan Power Grid Co., Ltd., Kunming 650217, China)

  • Cong Shen

    (Key Laboratory of Control, Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Qian Ai

    (Key Laboratory of Control, Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

In the context of global climate governance and the low-carbon energy transition, virtual power plant (VPP), a key technology for integrating distributed energy resources, is urgently needed to solve the problem of decentralization and lack of synergy in electricity, carbon, and green certificate trading. Existing studies mostly focus on single energy or carbon trading scenarios and lack a multi-market coupling mechanism supported by blockchain technology, resulting in low transaction transparency and a high risk of information tampering. For this reason, this paper proposes a synergistic optimization strategy for electricity/carbon/green certificate virtual power plants based on blockchain cross-chain transactions. First, Latin Hypercubic Sampling (LHS) is used to generate new energy output and load scenarios, and the K-means clustering method with improved particle swarm optimization are combined to cut down the scenarios and improve the prediction accuracy; second, a relay chain cross-chain trading framework integrating quota system is constructed to realize organic synergy and credible data interaction among electricity, carbon, and green certificate markets; lastly, the multi-energy optimization model of the virtual power plant is designed to integrate carbon capture, Finally, a virtual power plant multi-energy optimization model is designed, integrating carbon capture, power-to-gas (P2G) and other technologies to balance the economy and low-carbon goals. The simulation results show that compared with the traditional model, the proposed strategy reduces the carbon emission intensity by 13.3% (1.43 tons/million CNY), increases the rate of new energy consumption to 98.75%, and partially offsets the cost through the carbon trading revenue, which verifies the Pareto improvement of environmental and economic benefits. This study provides theoretical support for the synergistic optimization of multi-energy markets and helps to build a low-carbon power system with a high proportion of renewable energy.

Suggested Citation

  • Chao Zheng & Wei Huang & Suwei Zhai & Kaiyan Pan & Xuehao He & Xiaojie Liu & Shi Su & Cong Shen & Qian Ai, 2025. "Blockchain-Enabled Cross-Chain Coordinated Trading Strategy for Electricity-Carbon-Green Certificate in Virtual Power Plants: Multi-Market Coupling and Low-Carbon Operation Optimization," Energies, MDPI, vol. 18(13), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3443-:d:1691524
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    References listed on IDEAS

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    1. Xenophon, Aleksis Kazubiernis & Hill, David John, 2020. "Adaptive mechanisms to refund emissions payments," Applied Energy, Elsevier, vol. 278(C).
    2. Barrera-Santana, J. & Marrero, Gustavo A. & Ramos-Real, Francisco J., 2022. "Income, energy and the role of energy efficiency governance," Energy Economics, Elsevier, vol. 108(C).
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