Aggregate Model of District Heating Network for Integrated Energy Dispatch: A Physically Informed Data-Driven Approach

TL;DR

This paper introduces a physically informed data-driven aggregate model for district heating networks that accurately captures source-load relationships while being robust to measurement imperfections, aiding integrated energy system operation.

Contribution

It develops a novel physically informed estimator and an efficient algorithm for modeling DHNs without revealing detailed network structures, improving accuracy and robustness.

Findings

The model accurately describes DHN source-load relationships.

The estimator is robust to low-quality measurements.

Simulation confirms the method's effectiveness.

Abstract

The district heating network (DHN) is essential in enhancing the operational flexibility of integrated energy systems (IES). Yet, it is hard to obtain an accurate and concise DHN model for the operation owing to complicated network features and imperfect measurements. Considering this, this paper proposes a physical-ly informed data-driven aggregate model (AGM) for the DHN, providing a concise description of the source-load relationship of DHN without exposing network details. First, we derive the analytical relationship between the state variables of the source and load nodes of the DHN, offering a physical fundament for the AGM. Second, we propose a physics-informed estimator for the AGM that is robust to low-quality measurements, in which the physical constraints associated with the parameter normalization and sparsity are embedded to improve the accuracy and robustness. Finally, we propose a physics-enhanced algorithm to solve the nonlinear estimator with non-closed constraints efficiently. Simulation results verify the effectiveness of the proposed method.