Use this URL to cite or link to this record in EThOS:
Title: Modelling and optimization of polygeneration energy systems
Author: Liu, Pei
ISNI:       0000 0004 2682 1050
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2010
Availability of Full Text:
Access from EThOS:
Access from Institution:
Ever-increasing energy consumption and consequent extensive greenhouse gas (GHG) emissions are two major urgent problems faced by all human beings in the 21st century. As a major contributor, the energy production section appears to be the most suitable field where further improvements could be explored to tackle these problems. Polygeneration is a typical type of next generation energy production technology with higher energy efficiency and lower/zero GHG emissions. However, methodologies guiding an efficient and stable transition from our existing energy systems to more advanced ones are still lacking. The purpose of this thesis is to provide a generic modelling and optimization framework to guide planning and design of energy systems. This framework of methodologies ad- dresses the following issues arising in the planning and designing of energy systems: a) decision making at both strategic planning level and process design level; b) selection of roadmaps, technologies, and types of equipment from many available options; c) planning or design according to both economic and environmental criteria; d) planning or design under inevitable and unpredictable future uncertainty. The thesis is organized as follows: first, a review of energy systems is presented, followed by methodologies of energy systems engineering and their applications. Then a section of polygeneration process modelling is provided, at both strategic planning and process design levels, comprising superstructure representations of polygeneration energy systems at different levels, implementations of the superstructure based modelling strategy using mixed-integer programming, multi-objective optimization for the optimal process design according to both economic and environmental criteria, and optimization under uncer- tainty to account the impacts of future uncertainties at the planning/design stage and to increase the flexibility and robustness of a process design. Finally, major achievements of this work are summarised and future research directions are recommended.
Supervisor: Pistikopoulos, Stratos Sponsor: BP ; Kwoks' Foundation
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral