Simulation based validation of a reinforcement learning-based operation strategy for sector-coupled district heating system

Heating represents a major share of energy use in Germany, making it a critical sector in the energy transition. District heating systems (DHS) are key to decarbonising heat supply, particularly when integrating renewable energy sources and sector-coupled components. However, exploiting this flexibility requires advanced operational strategies. Artificial intelligence (AI), particularly reinforcement learning (RL), offers a promising approach, but its performance must be validated under realistic operating conditions before deployment.This study presents a structured framework for evaluating AI-driven operational strategies using a physics-based dynamic simulation model of a real German DHS. A detailed digital twin was developed in MATLAB/Simulink using the Simscape toolbox, capturing thermo-hydraulic behaviour of heat pumps, stratified thermal energy storage, electric heating rods, a gas boiler, and the network. The AI model, based on reinforcement learning, determines generation dispatch with the objective of increasing renewable utilisation and reducing fossil backup operation.The simulation model is first validated against measured operation for four characteristic historical weeks, establishing a reliable physical reference. Baseline and AI-controlled operation are then compared under identical boundary conditions. Results show robust AI performance under moderate and warm conditions, while systematic underdelivery occurs during colder and highly dynamic periods, revealing limitations of the simplified training environment.The findings demonstrate that high-fidelity dynamic simulation provides a robust, risk-free environment for pre-deployment validation of AI-based control strategies in sector-coupled DHS.