Machine Learning and Blockchain in Peer-to-Peer Energy Trading: A Cross-Layer Review of Functional Roles, Market Operation, Trust, and Privacy

Abstract

Peer-to-peer (P2P) energy trading combines local energy resources, market coordination, data-driven decisions, and transaction management. This review examines how machine learning (ML) and blockchain are used across these functions and separates market and ledger processes from physical electricity delivery. A structured review procedure was applied to a corpus of 52 peer-reviewed journal articles, including the core P2P energy-trading evidence and a limited number of closely related contextual studies, supplemented by 10 non-journal or foundational sources, using defined search families, screening criteria, and qualitative synthesis. The literature is organized by the functional role of ML and compared across architecture, market operation, trust, consensus, privacy, and implementation. The consensus discussion considers practical Byzantine fault tolerance, Istanbul Byzantine fault tolerance, proof-of-authority, and application-oriented Byzantine-fault-tolerance variants, while the privacy discussion distinguishes federated learning, differential privacy, zero-knowledge proofs, and secure multiparty computation. Two deterministic MATLAB examples are included only for illustration. In the five-prosumer forecasting example, regression reduced mean absolute error (MAE) from 0.4240 to 0.2219 kWh and the hourly grid-import mismatch from 30.3529 to 7.9029 kWh. In the 10-peer workflow, five trades settled 7.7587 kWh, corresponding to 59.35% of the horizon-level surplus–deficit denominator defined in the simulation. These examples do not validate feeder feasibility, consensus performance, cryptographic security, or deployment readiness.