用强化学习与区块链优化能源交易，实现供需平衡与可信管理
Optimizing Day-Ahead Energy Trading with Proximal Policy Optimization and Blockchain
- 用PPO算法训练智能体，实现多目标能源优化决策
- 供需平衡误差小于2%，多数时段成本接近最优
- 适合关注绿色能源交易与可信系统设计的研究者
可再生能源在日前电力市场中的渗透率上升，带来供需平衡、电网韧性与去中心化交易信任等挑战。本文提出一种新框架，将先进强化学习方法近端策略优化（PPO）与区块链技术结合，用于优化分布式产消者（prosumers）的日前交易策略。该框架采用强化学习智能体实现多目标能源优化，并利用区块链保障数据与交易记录的不可篡改性。基于德克萨斯电力可靠性委员会（ERCOT）真实数据的仿真表明，该智能体可将供需偏差控制在2%以内，多数运行时段维持近优供电成本，且生成稳健的储能策略以应对光伏和风电波动。所有决策均记录于Algorand区块链，确保透明、可审计与安全，是构建可信多智能体能源交易系统的关键。贡献包括新颖系统架构、课程学习提升智能体鲁棒性，以及可落地的政策建议。
原文摘要 · Abstract (English)
The increasing penetration of renewable energy sources in day-ahead energy markets introduces challenges in balancing supply and demand, ensuring grid resilience, and maintaining trust in decentralized trading systems. This paper proposes a novel framework that integrates the Proximal Policy Optimization (PPO) algorithm, a state-of-the-art reinforcement learning method, with blockchain technology to optimize automated trading strategies for prosumers in day-ahead energy markets. We introduce a comprehensive framework that employs RL agent for multi-objective energy optimization and blockchain for tamper-proof data and transaction management. Simulations using real-world data from the Electricity Reliability Council of Texas (ERCOT) demonstrate the effectiveness of our approach. The RL agent achieves demand-supply balancing within 2\% and maintains near-optimal supply costs for the majority of the operating hours. Moreover, it generates robust battery storage policies capable of handling variability in solar and wind generation. All decisions are recorded on an Algorand-based blockchain, ensuring transparency, auditability, and security - key enablers for trustworthy multi-agent energy trading. Our contributions include a novel system architecture, curriculum learning for robust agent development, and actionable policy insights for practical deployment.
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