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Modelling of integrated multi-energy systems : drivers, requirements, and opportunities

Mancarella, P. and Andersson, G. and Peças-Lopes, J. A. and Bell, K. R. W. (2016) Modelling of integrated multi-energy systems : drivers, requirements, and opportunities. In: 19th Power Systems Computation Conference, 2016-06-20 - 2016-06-24, Porto Antico Conference Centre.

Text (Mancarella-etal-PSCC2016-Modelling-of-integrated-multi-energy-systems)
Mancarella_etal_PSCC2016_Modelling_of_integrated_multi_energy_systems.pdf - Accepted Author Manuscript

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There is growing recognition that decarbonisation of existing uses of electricity is only ‘part of the story’ and that closer attention needs to be given to demand for energy in heating or cooling and in transport, and to all the energy vectors and infrastructures that supply the end-use demand. In this respect, concepts such as ‘multi-energy systems’ (MES) have been put forward and are gaining increasing momentum, with the aim of identifying how multiple energy systems that have been traditionally operated, planned and regulated in independent silos can be integrated to improve their collective technical, economic, and environmental performance. This paper addresses the need for modelling of MES which is capable of assessing interactions between different sectors and the energy vectors they are concerned with, so as to bring out the benefits and potential unforeseen or undesired drawbacks arising from energy systems integration. Drivers for MES modelling and the needs of different users of models are discussed, along with some of the practicalities of such modelling, including the choices to be made in respect of spatial and temporal dimensions, what these models might be used to quantify, and how they may be framed mathematically. Examples of existing MES models and tools and their capabilities, as well as of studies in which such models have been used in the authors’ own research, are provided to illustrate the general concepts discussed. Finally, challenges, opportunities and recommendations are summarised for the engagement of modellers in developing a new range of analytical capabilities that are needed to deal with the complexity of MES