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Model reduction for Model Predictive Control of district and communal heating systems within cooperative energy systems

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  • Lyons, Ben
  • O’Dwyer, Edward
  • Shah, Nilay

Abstract

The benefits of applying advanced control approaches such as Model Predictive Control to the building energy domain are well understood. Furthermore, to facilitate the decarbonisation of the sector, district heating, communal heating and heat pumps are set to become more common, leading to a greater need to employ advanced approaches to enable flexible integration with the power grid whereby buildings can provide flexibility services to mitigate grid stress. The development of models that are complex enough to capture the behaviour of large numbers of buildings without introducing excessive computational effort remains a challenge. In this paper, an approach is proposed in which model reduction techniques based on Hankel Singular Value Decomposition are applied in cooperation with state-of-the-art building energy modelling tools to produce models of large numbers of buildings that remain tractable within an MPC framework. The approach is demonstrated using a case study in which a MPC is developed for a 95-flat communal heating system. Centralised and decentralised approaches are considered, particularly in their respective ability to incorporate externally imposed constraints on the supply.

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  • Lyons, Ben & O’Dwyer, Edward & Shah, Nilay, 2020. "Model reduction for Model Predictive Control of district and communal heating systems within cooperative energy systems," Energy, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:energy:v:197:y:2020:i:c:s0360544220302851
    DOI: 10.1016/j.energy.2020.117178
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    4. Wang, Haichao & Zhou, Yang & Li, Xiangli & Wu, Xiaozhou & Wang, Hai & Elnaz, Abdollahi & Granlund, Katja & Lahdelma, Risto & Teppo, Esa, 2023. "Study on the performance of a forced convection low temperature radiator for district heating," Energy, Elsevier, vol. 283(C).
    5. Saloux, Etienne & Candanedo, José A., 2021. "Model-based predictive control to minimize primary energy use in a solar district heating system with seasonal thermal energy storage," Applied Energy, Elsevier, vol. 291(C).
    6. Coccia, Gianluca & Mugnini, Alice & Polonara, Fabio & Arteconi, Alessia, 2021. "Artificial-neural-network-based model predictive control to exploit energy flexibility in multi-energy systems comprising district cooling," Energy, Elsevier, vol. 222(C).
    7. Lizárraga-Morazán, Juan Ramón & Picón-Núñez, Martín, 2023. "Optimal sizing and control strategy of low temperature solar thermal utility systems," Energy, Elsevier, vol. 263(PC).

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