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Robust co-design framework for buildings operated by predictive control

P. Falugi, E. O'Dwyer, M. A. Zagorowska, E. C. Kerrigan, Y. Nie, G. Strbac, N. Shah

Abstract

Cost-effective decarbonisation of the built environment is a stepping stone to achieving net-zero carbon emissions since buildings are globally responsible for more than a quarter of global energy-related CO$_2$ emissions. Improving energy utilization and decreasing costs naturally requires considering multiple domain-specific performance criteria. The resulting problem is often computationally infeasible. The paper proposes an approach based on decomposition and selection of significant operating conditions to achieve a formulation with reduced computational complexity. We present a robust framework to optimise the physical design, the controller, and the operation of residential buildings in an integrated fashion, considering external weather conditions and time-varying electricity prices. The framework explicitly includes operational constraints and increases the utilization of the energy generated by intermittent resources. A case study illustrates the potential of co-design in enhancing the reliability, flexibility and self-sufficiency of a system operating under different conditions. Specifically, numerical results demonstrate reductions in costs up to $30$% compared to a deterministic formulation. Furthermore, the proposed approach achieves a computational time reduction of at least $10$ times lower compared to the original problem with a deterioration in the performance of only 0.6%.

Robust co-design framework for buildings operated by predictive control

Abstract

Cost-effective decarbonisation of the built environment is a stepping stone to achieving net-zero carbon emissions since buildings are globally responsible for more than a quarter of global energy-related CO emissions. Improving energy utilization and decreasing costs naturally requires considering multiple domain-specific performance criteria. The resulting problem is often computationally infeasible. The paper proposes an approach based on decomposition and selection of significant operating conditions to achieve a formulation with reduced computational complexity. We present a robust framework to optimise the physical design, the controller, and the operation of residential buildings in an integrated fashion, considering external weather conditions and time-varying electricity prices. The framework explicitly includes operational constraints and increases the utilization of the energy generated by intermittent resources. A case study illustrates the potential of co-design in enhancing the reliability, flexibility and self-sufficiency of a system operating under different conditions. Specifically, numerical results demonstrate reductions in costs up to % compared to a deterministic formulation. Furthermore, the proposed approach achieves a computational time reduction of at least times lower compared to the original problem with a deterioration in the performance of only 0.6%.

Paper Structure

This paper contains 27 sections, 24 equations, 7 figures, 4 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Existing literature
  3. Controller tuning
  4. Co-design
  5. Contributions
  6. Novelty
  7. Methodology
  8. Problem Formulation
  9. System dynamics
  10. Risk measures
  11. Numerical solution
  12. Multi-objective co-design problem
  13. Multiple objectives
  14. Co-design problem
  15. Problem decomposition and importance subsamples
  16. ...and 12 more sections

Figures (7)

  • Figure 1: Co-design framework structure
  • Figure 2: Data points (coloured dots) consisting in the optimal cost and technologies' size solution to (\ref{['importance_pb']}) clustered in $50$ groups differentiated by colours. Circles indicates the clusters' centroids.
  • Figure 3: Within-cluster sums of point-to-medoid distances of the clusters in Figure \ref{['Fig_cluster_noscal']}
  • Figure 4: Within-cluster sums of point-to-medoid distances for different numbers of clusters' choices
  • Figure 5: MPC tuning - Pareto front for the maximum technology size (red *) and for half technology size (blue o)
  • ...and 2 more figures