Predictive control co-design for enhancing flexibility in residential housing with battery degradation

Conference paper

Falugi, P., O’Dwyer, E., Kerrigan, E. C., Atam, E., Zagorowska, M. A., Strbac, G. and Shah, N. 2021. Predictive control co-design for enhancing flexibility in residential housing with battery degradation. 7th IFAC Conference on Nonlinear Model Predictive Control NMPC 2021. Bratislava, Slovakia 11 - 14 Jul 2021 Elsevier for the International Federation of Automatic Control. https://doi.org/10.1016/j.ifacol.2021.08.517
AuthorsFalugi, P., O’Dwyer, E., Kerrigan, E. C., Atam, E., Zagorowska, M. A., Strbac, G. and Shah, N.
TypeConference paper
Abstract

Buildings are responsible for about a quarter of global energy-related CO2 emissions. Consequently, the decarbonisation of the housing stock is essential in achieving net-zero carbon emissions. Global decarbonisation targets can be achieved through increased efficiency in using energy generated by intermittent resources. The paper presents a co-design framework for simultaneous optimal design and operation of residential buildings using Model Predictive Control (MPC). The framework is capable of explicitly taking into account operational constraints and pushing the system to its efficiency and performance limits in an integrated fashion. The optimality criterion minimises system cost considering time-varying electricity prices and battery degradation. A case study illustrates the potential of co-design in enhancing flexibility and self-sufficiency of a system operating under different conditions. Specifically, numerical results from a low-fidelity model show substantial carbon emission reduction and bill savings compared to an a-priori sizing approach.

Year2021
Conference7th IFAC Conference on Nonlinear Model Predictive Control NMPC 2021
PublisherElsevier for the International Federation of Automatic Control
Accepted author manuscript
License
File Access Level
Anyone
Publication dates
Online09 Sep 2021
Publication process dates
Deposited04 Jul 2023
JournalIFAC-PapersOnLine
Journal citation54 (6), pp. 8-13
ISSN2405-8963
Digital Object Identifier (DOI)https://doi.org/10.1016/j.ifacol.2021.08.517
Web address (URL) of conference proceedingshttps://www.sciencedirect.com/journal/ifac-papersonline/vol/54/issue/6
Copyright holder© 2021, The Author(s)
Permalink -

https://repository.uel.ac.uk/item/8w3w1

Log in to edit

Download files

Accepted author manuscript
MPC_and_Co_design (Accepted Manuscript).pdf
License: CC BY-NC-ND 4.0
File access level: Anyone

  • 71
    total views
  • 28
    total downloads
  • 8
    views this month
  • 1
    downloads this month

Export as

Related outputs

Machine Learning-Enhanced Benders Decomposition Approach for the Multi-Stage Stochastic Transmission Expansion Planning Problem
Borozan, S., Giannelos, S., Falugi, P., Moreira, A. and Strbac, G. 2024. Machine Learning-Enhanced Benders Decomposition Approach for the Multi-Stage Stochastic Transmission Expansion Planning Problem. Electric Power Systems Research. 237, p. Art. 110985. https://doi.org/10.1016/j.epsr.2024.110985
An integrated planning framework for optimal power generation portfolio including frequency and reserve requirements
Ayo, O, Falugi, P. and Strbac, G 2024. An integrated planning framework for optimal power generation portfolio including frequency and reserve requirements. IET Energy Systems Integration. In Press.
Automatic scenario generation for efficient solution of robust optimal control problems
Zagorowska, M., Falugi, P., O'Dwyer, E. and Kerrigan E. C. 2024. Automatic scenario generation for efficient solution of robust optimal control problems. International Journal of Robust and Nonlinear Control. 34 (2), pp. 1370-1396. https://doi.org/10.1002/rnc.7038
Automatic Scenario Generation for Robust Optimal Control Problems
Zagorowska, M., Falugi, P., O'Dwyer, E. and Kerrigan, E. C. 2023. Automatic Scenario Generation for Robust Optimal Control Problems. IFAC 2023: 22nd World Congress of the International Federation of Automatic Control. Yokohama, Japan 09 - 14 Jul 2023 Elsevier for the International Federation of Automatic Control. https://doi.org/10.1016/j.ifacol.2023.10.1743
Automating the data-driven predictive control design process for building thermal management
Falugi, P., O'Dwyer, E., Shah, N. and Kerrigan, E. C. 2022. Automating the data-driven predictive control design process for building thermal management. ECOS 2022 35th International Conference. Copenhagen, Denmark 03 - 07 Jul 2022 Danmarks Tekniske Universitet (DTU). https://doi.org/10.11581/dtu.00000267
A Modelling Workflow for Predictive Control in Residential Buildings
O’Dwyer, E., Atam, E., Falugi, P., Kerrigan, E. C., Zagorowska, M. A. and Shah, N. 2022. A Modelling Workflow for Predictive Control in Residential Buildings. in: Vahidinasab, V. and Mohammadi-Ivatloo, B. (ed.) Active Building Energy Systems: Operation and Control Springer, Cham. pp. 99-128
MPC and Optimal Design of Residential Buildings with Seasonal Storage: A Case Study
Falugi, P., O’Dwyer, E., Zagorowska, M. A., Atam, E., Kerrigan, E. C., Strbac, G. and Shah, N. 2022. MPC and Optimal Design of Residential Buildings with Seasonal Storage: A Case Study. in: Vahidinasab, V. and Mohammadi-Ivatloo, B. (ed.) Active Building Energy Systems: Operation and Control Springer, Cham. pp. 129-160
Fast and accurate method for computing non-smooth solutions to constrained control problems
Nita, L., Vila, E. M. G., Zagorowska, M. A., Kerrigan, E. C., Nie, Y., McInerney, I. and Falugi, P. 2022. Fast and accurate method for computing non-smooth solutions to constrained control problems. European Control Conference (ECC) 2022. London, UK 12 - 15 Jul 2022 IEEE. https://doi.org/10.23919/ECC55457.2022.9838569
Data-Driven Predictive Control With Improved Performance Using Segmented Trajectories
O’Dwyer, E., Kerrigan, E. C., Falugi, P., Zagorowska, M. and Shah, N. 2022. Data-Driven Predictive Control With Improved Performance Using Segmented Trajectories. IEEE Transactions on Control Systems Technology . 31 (3), pp. 1355 - 1365. https://doi.org/10.1109/TCST.2022.3224330
Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty
Falugi, P., Giannelos S, Jain A., Borozan S., Moreira A., Bhakar R., Mathur J. and Strbac G. 2021. Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty. Energies. 14 (22), p. 7813. https://doi.org/10.3390/en14227813
Robust and automatic data cleansing method for short-term load forecasting of distribution feeders
Huyghues-Beaufond, N., Tindemans, S., Falugi, P., Sun, M. and Strbac, G. 2020. Robust and automatic data cleansing method for short-term load forecasting of distribution feeders. Applied Energy. 261 (Art. 114405). https://doi.org/10.1016/j.apenergy.2019.114405