Functional Architecture of European Electricity Trading Markets: Requirements for AI Supported Trading Systems under Regulatory Constraints

arXiv:2609.29108 · cs.AI, cs.SY, eess.SY, q-fin.GN, q-fin.TR · Submitted 2026-09-24 · Read on arXiv

cs.AI, cs.SY, eess.SY, q-fin.GN, q-fin.TR

Submitted: 2026-09-24

Updated: 2026-09-30

Comments: 12 pages, 1 table. Published in Swissi AI Journal under CC BY 4.0

Journal ref: Swissi AI Journal, Volume 2026, Article SAIJ-cwo7xrcdsaut (2026)

DOI: 10.5281/zenodo.21901249

License: http://creativecommons.org/licenses/by/4.0/

The gist: 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,

Terminology

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.

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