4.7 Review

Holistic modelling techniques for the operational optimisation of multi-vector energy systems

期刊

ENERGY AND BUILDINGS
卷 169, 期 -, 页码 397-416

出版社

ELSEVIER SCIENCE SA
DOI: 10.1016/j.enbuild.2018.03.065

关键词

Energy modelling; Multi-vector energy systems; Power-to-Gas; Building energy modelling; Urban energy systems; Energy management; Optimisation

资金

  1. EPSRC (Engineering and Physical Sciences Research Council)
  2. BRE (Building Research Establishment)
  3. European Commission [723562, 731125]
  4. H2020 Societal Challenges Programme [731125] Funding Source: H2020 Societal Challenges Programme

向作者/读者索取更多资源

Modern district energy systems are highly complex with several controllable and uncontrollable variables. To effectively manage a multi-vector district requires a holistic perspective in terms of both modelling and optimisation. Current district optimisation strategies found in the literature often consider very simple models for energy generation and conversion technologies. To improve upon the state of the art, more realistic and accurate models must be produced whilst remaining computationally and mathematically simple enough to calculate within short periods. Therefore, this paper provides a comprehensive review of modelling techniques for common district energy conversion technologies including Power-to Gas. In addition, dynamic building modelling techniques are reviewed, as buildings must be considered active and flexible participants in a district energy system. In both cases, a specific focus is placed on artificial intelligence-based models suitable for implementation in the real-time operational optimisation of multi-vector systems. Future research directions identified from this review include the need to integrate simplified models of energy conversion units, energy distribution networks, dynamic building models and energy storage into a holistic district optimisation framework. Finally, a future district energy management solution is proposed. It leverages semantic modelling to allow interoperability of heterogeneous data sources to provide added value inferencing from contextually enriched information. (C) 2018 The Authors. Published by Elsevier B.V.

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