Real District Heating Network Research Articles

Leakage Localization in District Heating Networks Based on Real Network and Measurement Data

Fast localization of leakages in district heating networks is a demanding yet important task for network operation. Every leakage results in a loss of medium thus of energy. In case of small leakages, the medium lost can be replenished to maintain network operation. However, if the loss of medium is too large, it may be inevitable to shut down network operation. This can be prevented by separating the damaged network part using exclusion areas. For this, the leakage must be assigned to the correct exclusion area as fast and accurately as possible. This is a major challenge for current district heating networks without existing or with leakage localization systems delivering results too slow, so that other methods of leakage detection and localization are needed here. In a joint research project, three model- and data-based approaches are developed to localize leakages in real time. These are based on operating data which are typically measured for operational needs such as pressure or flow rate. The first approach evaluates the pressure wave which traverses the network at speed of sound when a leakage occurs. The second, purely data-driven approach uses machine learning models to localize leakages. The training phase of this approach is based on a large number of simulated leakages using a network model. The last approach uses the same network model to numerically compute the network state and localize leakages depending on available measurement data. In previous publications these approaches are evaluated using simulated measurement data with encouraging results. In this paper the work is continued and carried out to measurement data of a real district heating network. It is shown that each method is individually applicable to localize leakages under real conditions and provides plausible results. This encourages further development for using synergies which may deliver even better results.

An intelligent semantic system for real-time demand response management of a thermal grid

“Demand Response” energy management of thermal grids requires consideration of a wide range of factors at building and district level, supported by continuously calibrated simulation models that reflect real operation conditions. Moreover, cross-domain data interoperability between concepts used by the numerous hardware and software is essential, in terms of Terminology, Metadata, Meaning and Logic. This paper leverages domain ontology to map and align the semantic resources that underpin building and district energy management, with a focus on the optimization of a thermal grid informed by real-time energy demand. The intelligence of the system is derived from simulation-based optimization, informed by calibrated thermal models that predict the network’s energy demand to inform (near) real-time generation. The paper demonstrates that the use of semantics helps alleviate the endemic energy performance gap, as validated in a real district heating network where 36% reduction on operation cost and 43% reduction on CO2 emission were observed compared to baseline operational data.

Online Leakage Attribution to Exclusion Areas Prototype Application

Localisation of leakages within district heating networks is a challenging but highly essential task. In case of a leakage, especially in sudden cases, there occur miscellaneous adverse effects. In any case, the lost medium must be fed back to the network. If the amount of lost medium is too large, it is inevitable to shut down the network. This can be prevented by the use of exclusion areas if these can separate the damaged parts of the network. However, in order to effectively use these exclusion areas, the leakage must be attributed to one or more exclusion areas as quickly as possible.One possible approach is based on the propagation of the pressure wave, which may arise as an initial reaction to the occurrence of a leakage. The first step of the prototype presented here is to recognise the leakage entry based on the measurement data. Subsequently, all pressure measurement data are evaluated to find the point in time at which these pressures dropped due to leakage. It is not possible to deduct the leakage position on the basis of these times directly as the data quality meets the operational requirements but is far too low. The prototype can use these time points despite their big errors. For all possible leakage positions the theoretical wave propagation is calculated taking into account the valve states. The leakage is attributed to the exclusion area where the theoretical wave propagation fits best.Leakages within a real district heating network were simulated to test and evaluate the prototype. For example, about five percent of the transport medium of the network was replenished within a time span of one and a half hours. We present the results of the prototype based on these real data.

Dynamic equation-based thermo-hydraulic pipe model for district heating and cooling systems

Simulation and optimisation of district heating and cooling networks requires efficient and realistic models of the individual network elements in order to correctly represent heat losses or gains, temperature propagation and pressure drops. Due to more recent thermal networks incorporating meshing decentralised heat and cold sources, the system often has to deal with variable temperatures and mass flow rates, with flow reversal occurring more frequently. This paper presents the mathematical derivation and software implementation in Modelica of a thermo-hydraulic model for thermal networks that meets the above requirements and compares it to both experimental data and a commonly used model. Good correspondence between experimental data from a controlled test set-up and simulations using the presented model was found. Compared to measurement data from a real district heating network, the simulation results led to a larger error than in the controlled test set-up, but the general trend is still approximated closely and the model yields results similar to a pipe model from the Modelica Standard Library. However, the presented model simulates 1.7 (for low number of volumes) to 68 (for highly discretized pipes) times faster than a conventional model for a realistic test case. A working implementation of the presented model is made openly available within the IBPSA Modelica Library. The model is robust in the sense that grid size and time step do not need to be adapted to the flow rate, as is the case in finite volume models.

Study and demonstration of pressure wave-based leak detection in a district heating network

A district heating network (DHN) is one of the most important infrastructures in cities and towns of countries with colder weather. Citizens generally use DHN services for hot water supply all year round and also for space heating during winter. It is important that in case of an accident this service would be restored as soon as possible, causing minimal damage or inconveniences for the customers. In addition, it is important to minimise losses of the DHN operator. This study demonstrated a possibility of leak location in DHN using the data from the pressure sensors in the network, employing the negative pressure wave (NPW) method. The mass balance in the DHN is measured at the heating source, which acts as a confirmation of a leak in a closed system. The experiments in the real DHN were performed and the data was used to trace the leak location. Numerical modelling tools were used to model pressure transients during pipe break accident under various conditions in order to predict the effectiveness and limitations of the leak detection system.

Function method for dynamic temperature simulation of district heating network

A thermal dynamic characteristic of district heating network was analyzed with an emphasis on simulating dynamic temperature distribution. Therefore, a new physical method (namely function method) and corresponding computer codes for dynamic temperature simulation were proposed. The function method takes into account variousfactors, such as flow time, heat capacity of the pipe and heat loss, in one single step by using Fourier series expansion as the mathematical basis to obtain the analytical solution of transient energy equation. Further, two key parameters, delay time and relative attenuation degree, were described to represent the time delay and heat loss respectively and were used to obtain the temperature distribution of the network. Moreover, the function method is validated by applying it to a real district heating network at different temperature change stages. For comparison purpose, the node method was also used, and the simulated supply temperatures at substations were compared with measurements. Comparisons of both methods were also performed considering different fluid velocities at heat source using numerical results. The results showed that the function method could be recommended to simulate the dynamic temperature of district heating network accurately and quickly for efficient system performance.

ANALYSIS OF DISTRICT HEATING NETWORK MONITORING BY NEURAL NETWORKS CLASSIFICATION

The paper proposes an alternative approach to the problem of district heating monitoring parameters selection, based on conditions, taken, for instance, from a specific real district heating network supplemented with tests data and expert knowledge.

Analysis of district heating network monitoring by neural networks classification

Abstract The paper proposes an alternative approach to the problem of district heating monitoring parameters selection, based on conditions, taken, for instance, from a specific real district heating network supplemented with tests data and expert knowledge.