A framework for the assessment of optimal and cost-effective energy decarbonisation pathways of a UK-based healthcare facility

Morales Sandoval, Daniel A.; Saikia, Pranaynil; De la Cruz-Loredo, Ivan; Zhou, Yue; Ugalde-Loo, Carlos E.; Bastida, Héctor; Abeysekera, Muditha

doi:10.1016/j.apenergy.2023.121877

Cited by 2 publications

(2 citation statements)

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“…The optimisation approach has been borrowed from (Morales Sandoval et al, 2023). To prevent duplication of published work, interested readers are referred to the reference for full details.…”

Section: Optimisation Algorithm and Optimisation Modulementioning

confidence: 99%

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Dynamic verification of an optimisation algorithm for power dispatch of integrated energy systems

Morales Sandoval,

De La Cruz-Loredo,

Saikia

et al. 2024

Front. Energy Res.

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The urgent need to achieve net-zero carbon emissions by 2050 has led to a growing focus on innovative approaches to producing, storing, and consuming energy. Integrated energy systems (IES) have emerged as a promising solution, capitalising on synergies between energy networks and enhancing efficiency. Such a holistic approach enables the integration of renewable energy sources and flexibility provision from one energy network to another, reducing emissions while facilitating strategies for operational optimisation of energy systems. However, emphasis has been mostly made on steady-state methodologies, with a dynamic verification of the optimal solutions not given sufficient attention. To contribute towards bridging this research gap, a methodology to verify the outcomes of an optimisation algorithm is presented in this paper. The methodology has been applied to assess the operation of a civic building in the UK dedicated to health services. This has been done making use of real energy demand data. Optimisation is aimed at improving power dispatch of the energy system by minimising operational costs and carbon emissions. To quantify potential discrepancies in power flows and operational costs obtained from the optimisation, a dynamic model of the IES that better captures real-world system operation is employed. By incorporating slow transients of thermal systems, control loops, and non-linearity of components in the dynamic model, often overlooked in traditional optimisation modules, the methodology provides a more accurate assessment of energy consumption and operational costs. The effectiveness of the methodology is assessed through model-in-the-loop co-simulations between MATLAB/Simulink and Apros alongside a series of scenarios. Results indicate significant discrepancies in power flows and operational costs between the optimisation and the dynamic model. These findings illustrate potential limitations of conventional operational optimisation modules in addressing real-world complexities, emphasising the significance of dynamic verification methods for informed energy management and decision-planning.

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Section: Optimisation Algorithm and Optimisation Modulementioning

confidence: 99%

“…The following assumptions were considered for system modelling within the optimisation module (based on the data provided by QEH and information available in the literature) (Morales Sandoval et al, 2023):…”

Section: System Modelling For Optimisationmentioning

confidence: 99%