Real-Time Peer-to-Peer Energy Trading for Multi-Microgrids: Improved Double Auction Mechanism and Prediction-Free Online Trading Approach
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915531882233856 |
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| author | Huang, Kaidi Cheng, Lin Zhou, Yue Shi, Fashun Xi, Yufei Zhuang, Yingrui Qi, Ning |
| author_facet | Huang, Kaidi Cheng, Lin Zhou, Yue Shi, Fashun Xi, Yufei Zhuang, Yingrui Qi, Ning |
| contents | Peer-to-peer energy trading offers a promising solution for enhancing renewable energy utilization and economic benefits within interconnected microgrids. However, existing real-time P2P markets face two key challenges: high computational complexity in trading mechanisms, and suboptimal participant decision-making under diverse uncertainties. Existing prediction-based decision-making methods rely heavily on accurate forecasts, which are typically unavailable for microgrids, while prediction-free methods suffer from myopic behaviors. To address these challenges, this paper proposes an improved double auction mechanism combined with an adaptive step-size search algorithm to reduce computational burden, and a data-driven dual-reference online optimization (DDOO) framework to enhance participant decision-making. The improved mechanism simplifies bidding procedures, significantly reducing computational burden and ensuring rapid convergence to the market equilibrium. Additionally, the prediction-free DDOO framework mitigates myopic decision-making by introducing two informative reference signals. Case studies on a 20-microgrid system demonstrate the effectiveness and scalability of the proposed mechanism and approach. The improved mechanism significantly decreases the computational time while increasing local energy self-sufficiency periods from 0.01% to 29.86%, reducing reverse power flow periods from 24.51% to 3.96%, and lowering average operating costs by 19.20%. Compared with conventional approaches such as Lyapunov optimization and model predictive control, the DDOO framework achieves a 10%-13% reduction in operating costs with an optimality gap of only 5.76%. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_02985 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-Time Peer-to-Peer Energy Trading for Multi-Microgrids: Improved Double Auction Mechanism and Prediction-Free Online Trading Approach Huang, Kaidi Cheng, Lin Zhou, Yue Shi, Fashun Xi, Yufei Zhuang, Yingrui Qi, Ning Systems and Control Peer-to-peer energy trading offers a promising solution for enhancing renewable energy utilization and economic benefits within interconnected microgrids. However, existing real-time P2P markets face two key challenges: high computational complexity in trading mechanisms, and suboptimal participant decision-making under diverse uncertainties. Existing prediction-based decision-making methods rely heavily on accurate forecasts, which are typically unavailable for microgrids, while prediction-free methods suffer from myopic behaviors. To address these challenges, this paper proposes an improved double auction mechanism combined with an adaptive step-size search algorithm to reduce computational burden, and a data-driven dual-reference online optimization (DDOO) framework to enhance participant decision-making. The improved mechanism simplifies bidding procedures, significantly reducing computational burden and ensuring rapid convergence to the market equilibrium. Additionally, the prediction-free DDOO framework mitigates myopic decision-making by introducing two informative reference signals. Case studies on a 20-microgrid system demonstrate the effectiveness and scalability of the proposed mechanism and approach. The improved mechanism significantly decreases the computational time while increasing local energy self-sufficiency periods from 0.01% to 29.86%, reducing reverse power flow periods from 24.51% to 3.96%, and lowering average operating costs by 19.20%. Compared with conventional approaches such as Lyapunov optimization and model predictive control, the DDOO framework achieves a 10%-13% reduction in operating costs with an optimality gap of only 5.76%. |
| title | Real-Time Peer-to-Peer Energy Trading for Multi-Microgrids: Improved Double Auction Mechanism and Prediction-Free Online Trading Approach |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2510.02985 |