European electricity trading markets designed for AI under rules
Functional Architecture of European Electricity Trading Markets: Requirements for AI Supported Trading Systems under Regulatory Constraints
Artificial Intelligence
Summary
Trading electric power across Europe is complicated because many rules and physical limits affect how energy moves and is bought or sold. The authors analyze these complexities and create a detailed plan for how artificial intelligence (AI) could help make trading decisions safely within these rules. Their framework ensures AI works with clear controls and checks instead of making unchecked predictions. They also identify where current coordination across borders often breaks down, suggesting where improvements are needed.
What this means in practice
- •For energy market operators: Implement AI components within electricity trading systems that meet regulatory rules and improve decision-making reliability in cross-border markets.
- •For financial compliance teams: Design audit-ready AI tools that manage trading permissions and exposure accounting consistent with EU trading regulations.
A position paper. It proposes an approach and reports no results.
Authors
Walter Kurz, Wojtek Stricker
Abstract
European electricity trading in the EU operates as a constrained multi-layer system in which legal design, exchange microstructure, and network physics are executed jointly across forward, day-ahead, intraday, and balancing horizons. This paper develops a functional architecture for AI-supported trading that is aligned with market-coupling mechanics, cross-zonal transfer constraints, and compliance obligations under REMIT, MiFID II, MiFIR, and EMIR. The contribution is a formal system specification composed of a decision-state vector, residual-exposure accounting, constrained optimization objective, executable-action permission gate, and fail-closed AI control logic with auditable records. The analysis maps major Nominated Electricity Market Operator (NEMO) venues and related exchange operators into an operational venue topology and identifies where cross-border coordination fails in practice: interface-level timing, permission heterogeneity, and balancing-layer coupling. The resulting framework proposes how AI can be deployed as a bounded decision component inside regulated market operation with explicit governance, rather than as an unconstrained prediction layer.