Optimizing Day-Ahead Energy Trading with Proximal Policy Optimization and Blockchain

TL;DR

This paper presents a novel framework combining reinforcement learning and blockchain to optimize day-ahead energy trading, enhancing grid stability, security, and transparency in decentralized renewable energy markets.

Contribution

It introduces a new system architecture integrating PPO-based RL with blockchain for secure, transparent energy trading and demonstrates its effectiveness with real-world data.

Findings

Demand-supply balancing within 2% accuracy

Near-optimal supply costs maintained

Robust battery storage policies developed

Abstract

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.