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

• Published in: Energy and buildings 2018-06, Vol.169, p.397-416
• Main Authors: Reynolds, Jonathan, Ahmad, Muhammad Waseem, Rezgui, Yacine
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Building energy modelling; Buildings; Construction industry; Distribution management; Energy conversion; Energy distribution; Energy efficiency; Energy management; Energy modeling; Energy modelling; Energy storage; Interoperability; Mathematical models; Modelling; Multi-vector energy systems; Optimisation; Optimization; Power-to-Gas; Solar energy; System effectiveness; Urban energy systems
• ISSN: 0378-7788; 1872-6178
• DOI: 10.1016/j.enbuild.2018.03.065

•This paper provides a holistic review of modelling techniques for district energy systems including both supply and demand.•Emphasis was placed on techniques applicable for use in real-time, operational optimisation.•Models based on artificial intelligence techniques were found to be suitable in most cases.•The requirements for a future, holistic, district optimisation platform are outlined. 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.