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Priority-based Energy Allocation in Buildings through Distributed Model Predictive Control

Hongyi Li, Jun Xu, Qianchuan Zhao

TL;DR

This work tackles energy allocation in buildings under limited HVAC supply by introducing priority-based distributed model predictive control (DMPC). It develops two coordination schemes—one-to-one and multi-to-one priority—both enabling parallel optimization across zones while respecting energy constraints. Through RC-based prediction models and EnergyPlus integration, the authors demonstrate that the distributed approaches achieve near-centralized performance with significantly improved scalability, validated on 3-zone and 36-zone buildings. The results show that urgent zones receive energy first, maintaining comfort where it matters most, with substantial gains in computational tractability for large-scale systems.

Abstract

Many countries are facing energy shortage today and most of the global energy is consumed by HVAC systems in buildings. For the scenarios where the energy system is not sufficiently supplied to HVAC systems, a priority-based allocation scheme based on distributed model predictive control is proposed in this paper, which distributes the energy rationally based on priority order. According to the scenarios, two distributed allocation strategies, i.e., one-to-one priority strategy and multi-to-one priority strategy, are developed in this paper and validated by simulation in a building containing three zones and a building containing 36 rooms, respectively. Both priority-based strategies fully exploit the potential of predictive control solutions. The experiment shows that our scheme has good scalability and achieve the performance of centralized strategy while making the calculation tractable.

Priority-based Energy Allocation in Buildings through Distributed Model Predictive Control

TL;DR

This work tackles energy allocation in buildings under limited HVAC supply by introducing priority-based distributed model predictive control (DMPC). It develops two coordination schemes—one-to-one and multi-to-one priority—both enabling parallel optimization across zones while respecting energy constraints. Through RC-based prediction models and EnergyPlus integration, the authors demonstrate that the distributed approaches achieve near-centralized performance with significantly improved scalability, validated on 3-zone and 36-zone buildings. The results show that urgent zones receive energy first, maintaining comfort where it matters most, with substantial gains in computational tractability for large-scale systems.

Abstract

Many countries are facing energy shortage today and most of the global energy is consumed by HVAC systems in buildings. For the scenarios where the energy system is not sufficiently supplied to HVAC systems, a priority-based allocation scheme based on distributed model predictive control is proposed in this paper, which distributes the energy rationally based on priority order. According to the scenarios, two distributed allocation strategies, i.e., one-to-one priority strategy and multi-to-one priority strategy, are developed in this paper and validated by simulation in a building containing three zones and a building containing 36 rooms, respectively. Both priority-based strategies fully exploit the potential of predictive control solutions. The experiment shows that our scheme has good scalability and achieve the performance of centralized strategy while making the calculation tractable.
Paper Structure (24 sections, 1 theorem, 30 equations, 15 figures, 13 tables, 2 algorithms)

This paper contains 24 sections, 1 theorem, 30 equations, 15 figures, 13 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. System description
  3. Overview
  4. Prediction model for MPC
  5. Control methodologies
  6. Problem formulation
  7. Centralized MPC formulation
  8. Decentralized MPC formulation
  9. Distributed MPC formulation
  10. Differences among models
  11. Distributed algorithm
  12. One-to-one priority strategy
  13. Multi-to-one priority strategy
  14. Strategic Analysis
  15. Performance of subsystems with the highest priority
  16. ...and 9 more sections

Key Result

Lemma 1

Denote$\{\mathbf{u}_{1c}^*,\mathbf{v}_{1c}^*,\cdots,\mathbf{u}_{nc}^*,\mathbf{v}_{nc}^*\}$ as the optimal solution for the centralized optimization problem (cent), $\mathbf{u}_{1dc}^*,\mathbf{v}_{1dc}^*$ as the optimal solution for the decentralized optimization problem (mpc2), and $\mathbf{u}_{1d}^

Figures (15)

  • Figure 1: Framework of energy allocation.
  • Figure 2: Centralized control framework.
  • Figure 3: Decentralized control framework.
  • Figure 4: Distributed control framework.
  • Figure 5: Information exchange between subsystems.
  • ...and 10 more figures

Theorems & Definitions (3)

  • Lemma 1
  • Proof 1
  • Example 1