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Achieving Lower District Heating Network Temperatures Using Feed-Forward MPC.

Nathan Zimmerman1, Konstantinos Kyprianidis2, Carl-Fredrik Lindberg2,3

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Summary

Implementing advanced control strategies can significantly reduce district heating network temperatures, leading to substantial energy savings. This study demonstrates how predictive control lowers supply and return temperatures, optimizing energy efficiency in heating systems.

Keywords:
DHNMPCcontroldistrict heatingdynamic modellingenergy savingsfeed-forward

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Area of Science:

  • Energy Systems Engineering
  • Control Systems Theory
  • Sustainable Energy

Background:

  • Current district heating networks often operate at higher temperatures than necessary.
  • Existing control methods rely on operator experience and outdoor temperature, limiting efficiency.
  • Future energy demands necessitate optimized and lower operating temperatures for district heating.

Purpose of the Study:

  • To evaluate the feasibility of reducing supply and return temperatures in district heating networks.
  • To implement and assess feed-forward model predictive control (MPC) for temperature optimization.
  • To determine the impact of predictive control on energy savings and network stability.

Main Methods:

  • Development of a dynamic process model for district heating networks.
  • Implementation of two feed-forward MPC scenarios using historical load and load prediction.
  • Evaluation of controller performance in maintaining lower network temperatures and managing return temperatures.

Main Results:

  • Both control scenarios demonstrated significant load reduction, between 12.5% and 13.7%.
  • Accurate prediction of end-user demand and feedback improved return temperature stability.
  • The proposed control approach enables lower supply temperatures and enhances production-side energy savings.

Conclusions:

  • Feed-forward MPC is a viable strategy for lowering district heating network temperatures.
  • Optimized control reduces energy consumption and improves the efficiency of heat production.
  • Accurate demand forecasting is crucial for sustaining lower return temperatures and maximizing energy savings.