Improved heat metering – DH substation control using sensor fusion networks

Prof. Jerker Delsing

Y. Jomni, K. Yliniemi and Dr. J. van Deventer

EISLAB

Luleå University of Technology

Sweden

Vision

Sensors on Internet High accuracy sensor technology Sensor talks TCP/IP Minimal size < 1 cm3

Power life time > 2 year Wireless ad-hoc networking Roughed packaging Ad-hoc application integration Secure

< 1 cm3

Sensor networks

Sensor use of a locally available data

System optimization based on local sensor fusion

Sensor fusionSystem optimization

Mulle – EIS platform

• Minimal ultra-light little EIS

• < 4cm2, 22x25x10 mm, including power

• Full EIS sensor network functionality

– TCP/IP

– Ad-hoc wireless networking

– Security Temperature sensor

District heat substation traditional

Flow

Heat exchanger

withcontrol valveTr

Tap hot water

Space heating

Heat system controler

Tvv

TuTi

Tf

Heat meter

District heat substation with sensor network system

Flow

Heat exchanger

withcontrol valveTr

Tap hot water

Space heating

Heatmeter & system controler

Tvv

TuTi

Tf

Enable use of more advanced heat metering algorithms

Adaptive heat meter algorithm Feed forward heat meter algorithm

Estimation of hot water flow

Space heating and hot water flow have different time scales

Error in hot water flow estimation

Estimation of tap warm water

System optimization from using sensor fusion networks

Heat metering Clearly improved measurement accuracy

Estimation of tap hot water flow Accuracy ~2%

Estimation of tap hot water energy Accuracy ~2% Use of additional temperature improve accuracy ~1%

District heat substation with sensor network system

Flow

Heat exchanger

withcontrol valveTr

Tap hot water

Space heating

Heatmeter & system controler

Tvv

TuTi

Tf

Internet

Network of DH substations

DH-substation

InternetDH-substationDH-substation

DH-substation

System optimization from using sensor fusion networks

Sub station optimization

Maximize T Reduced forward temperature

Total system energy efficiency optimization

New customer communication - services

Usage patterns

Heat

Tap hot water Customer system optimization, examples

Indication of lowered environmental impact - i.e. CO2

Remote optimization of control loops for reduced energy cost

Conclusions

Sensor fusion networks enables Clearly improved heat metering Additional data can be generated - hot water usage System optimization enabled Customer communication enabled

http://www.csee.ltu.se/eislabfo