Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.799421
Title: Generalised modelling framework for multi-energy systems with model predictive control applications
Author: Long, Sebastian
ISNI:       0000 0004 8504 8029
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2019
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Abstract:
There is growing interest in the topic of multi-energy systems, which offer numerous benefits. For example, they can provide flexibility to counteract the intermittency of renewable generation and increase energy efficiency. In order to maximise these benefits, advanced control methods are required. The model predictive control methodology is a promising candidate for such applications as it is capable of incorporating economic and operational objectives whilst respecting various technical, regulatory and environmental constraints. In order to implement such a control strategy effectively, it is necessary to develop appropriate system models. This thesis presents a novel generalised modelling framework for multi-energy systems that is particularly well suited, though not limited to, predictive control applications. The proposed approach is capable of representing energy converter arrangements of arbitrary complexity containing multiple energy vectors, as well as multi-directional energy flow, multi-generation and multi-mode devices, a wide range of controllable producers/consumers, energy storage and flexible loads. The effectiveness of the approach is demonstrated by way of two representative case studies based on buildings at the University of Manchester. One provides an example of a novel predictive control methodology that exploits multi-energy interactions and storage to achieve electricity demand smoothing. The second demonstrates how the flexible prosumer components, developed within proposed modelling framework, are integrated into an automatic energy management control scheme. The simulation results show how the controller minimises the cost of purchasing energy whilst satisfying operational constraints, and managing various types of flexible demand. Finally, a software library that can be used to implement multi-energy system models developed within the framework is introduced.
Supervisor: Marjanovic, Ognjen ; Parisio, Alessandra Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.799421  DOI: Not available
Keywords: multi-energy systems ; control-oriented modelling ; model predictive control ; demand side management
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