The document discusses a pilot project using Delft-FEWS and RTC-Tools for advanced control of the district heating network at Delft University of Technology campus. The network serves 18 buildings using 4 piping tracks connected to a CHP plant and 3 boilers. The goals are to minimize CO2 production, reduce energy usage, and use renewable sources by optimizing individual building supply temperatures based on real-time weather predictions. Initial results show potential for cost savings through price-optimal control of multiple heat sources to meet building comfort requirements given electricity price and weather forecasts. Improving building models and understanding prediction error sources could further reduce costs when extending the approach to the full TUDelft heating network.

1. Delft, 5 november 2015
Advanced control of smart thermal
grid – case Campus Delft University
Sam van der Zwan
Jorn Baayen
Tjerk Vreeken
Dirk Schwanenberg

2. District heating network of TUDelft
• 18 Buildings
• 4 tracks
• 1 CHP plant with 2 CHP units and 3 boilers
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3. District heating network of TUDelft
Goals
• Minimize CO2 production
• Reduce energy usage
• Use renewable energy sources
Required:
• Reduce temperature in the system (from HT to MT)
• Optimize supply temperature per building based on actual weather
predictions (real time)
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4. Current results: Load duration curve (CHP)
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5. Pilot: Delft-FEWS + RTC-Tools
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6. 10 november 2015
RTC-Tools is a toolbox for control of hydraulic structures.
• Collection of modules for control system development
• Model-predictive control
› Built-in model library
› External models
• Supports Modelica models (Dymola, Wolfram)
• Open source
RTC-Tools

7. 10 november 2015
Delft-FEWS
Delft-FEWS is an open data handling platform.
• Collection of modules for aggregating real-time data flows
• Flood warning
• Water quality management
• Decision support systems
› Models and controllers to support decision makers
› Models and controllers in closed-loop operation
• Free (but not open source), open standards

8. Example: District heating
• Two heat sources:
• Constant-price
• Variable-price
• Two buildings
• Building comfort requirements
Aim: Price-optimal control of heat sources, given predictions on
weather and electricity price.
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V4
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BUILDING2
SOURCE2
SOURCE1
BUILDING1

9. Example: District heating
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10. Example: District heating
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11. Outlook
• Better building models would further reduce costs
• Investigate nature of prediction error: model vs. weather prediction
error sources
• Extension to TUDelft heat network work in progress
10 november 2015