Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Using Flexibility Envelopes for the Demand-Side Hierarchical Optimization of District Heating Networks

Audrey Blizard, Colin N. Jones, Stephanie Stockar

TL;DR

This work tackles the challenge of demand-side control in district heating networks (DHNs) with many connected users by introducing a scalable hierarchical optimization framework. Local low-level controllers exploit building flexibility via flexibility envelopes to minimize subsystem costs under a set of candidate total pressure drops $\Delta P_{tot}$, while a high-level controller selects the optimal $\Delta P_{tot}$ for all subsystems to minimize overall cost while preserving mass and pressure balance. The approach is implemented on a mid-scale DHN partitioned into five subgraphs, achieving a substantial bypass-flow reduction (67%) and feasible real-time operation with receding-horizon optimization, albeit with a modest increase in total mass flow. This demonstrates the potential of demand-side flexibility to enhance DHN efficiency and provides a pathway for incorporating richer cost terms such as pumping costs and heat losses in future work.

Abstract

The demand-side control of district heating networks is notoriously challenging due to the large number of connected users and the high number of states to be considered. To overcome these challenges, this paper presents a hierarchical optimization scheme using the flexibility in heating demand provided by the users to improve the performance of the network. This hierarchical scheme relies on a low level controller to calculate the costs for a subsystem over a given set of potential pressure drops for that subsystem. The high level controller then uses these calculated costs to determine the optimal set of pressure drops for every subgraph of the partitioned network. The proposed hierarchical optimization scheme is demonstrated on a representative 20 user district heating network, resulting in a 67\% reduction in bypass mass flow while ensuring all network users stay within 2 \degree C of their desired nominal temperatures.

Using Flexibility Envelopes for the Demand-Side Hierarchical Optimization of District Heating Networks

TL;DR

This work tackles the challenge of demand-side control in district heating networks (DHNs) with many connected users by introducing a scalable hierarchical optimization framework. Local low-level controllers exploit building flexibility via flexibility envelopes to minimize subsystem costs under a set of candidate total pressure drops , while a high-level controller selects the optimal for all subsystems to minimize overall cost while preserving mass and pressure balance. The approach is implemented on a mid-scale DHN partitioned into five subgraphs, achieving a substantial bypass-flow reduction (67%) and feasible real-time operation with receding-horizon optimization, albeit with a modest increase in total mass flow. This demonstrates the potential of demand-side flexibility to enhance DHN efficiency and provides a pathway for incorporating richer cost terms such as pumping costs and heat losses in future work.

Abstract

The demand-side control of district heating networks is notoriously challenging due to the large number of connected users and the high number of states to be considered. To overcome these challenges, this paper presents a hierarchical optimization scheme using the flexibility in heating demand provided by the users to improve the performance of the network. This hierarchical scheme relies on a low level controller to calculate the costs for a subsystem over a given set of potential pressure drops for that subsystem. The high level controller then uses these calculated costs to determine the optimal set of pressure drops for every subgraph of the partitioned network. The proposed hierarchical optimization scheme is demonstrated on a representative 20 user district heating network, resulting in a 67\% reduction in bypass mass flow while ensuring all network users stay within 2 \degree C of their desired nominal temperatures.
Paper Structure (13 sections, 23 equations, 8 figures, 2 tables)

This paper contains 13 sections, 23 equations, 8 figures, 2 tables.

Table of Contents

  1. Introduction
  2. System Model
  3. Thermal Model
  4. Fluids Model
  5. Flexible Building Model
  6. Hierarchical Optimization of Demand
  7. Partitioning of Network
  8. Low-Level Optimization Problem
  9. High-Level Optimization Problem
  10. Results
  11. Description of Case Study
  12. Results
  13. Conclusions

Figures (8)

  • Figure 1: Sample graph for an 18-user DHN.
  • Figure 2: Proposed non-iterative hierarchical control structure.
  • Figure 3: Nominal heat demand of residential and commercial users.
  • Figure 4: Partitioned network graph and resulting reduced graph.
  • Figure 5: Supplied mass flow and bypass mass flow in the nominal and optimized case.
  • ...and 3 more figures