Please use this identifier to cite or link to this item:
Title: Performance assessment and robust optimal design of distributed energy systems in subtropical regions
Authors: Kang, Jing
Degree: Ph.D.
Issue Date: 2019
Abstract: The distributed energy systems have been proven to be energy efficient and cost-effective in many regions. However, very few studies about the application of distributed energy systems in subtropical regions, where cooling demand dominates and heating demand can be ignored, are conducted. This thesis attempts to comprehensively study the application of distributed energy systems in such regions and address the following questions which are not well answered in existing studies: • Are distributed energy systems more energy efficient when compared with existing centralized energy systems and what are the main constraints which limit the development of distributed energy systems in subtropical areas? • How to design a distributed energy system that can maximize its potentials in energy saving and cost reduction compared with the existing centralized energy system? • How to design a distributed energy system that can offer the best performance under uncertainties when the practical operating conditions may deviate from the predicted conditions? Being an innovative energy supply technology, the performance of distributed energy system compared with the centralized energy system determines its future application. Performance assessment of distributed energy systems is conducted by comparing with centralized energy systems in subtropical areas. Characteristics of distributed energy systems in application are summarized after quantitative energy performance and economic performance analysis. The impacts of major design parameters and energy policies on the system performance are studied. The main constraints for the development of distributed energy systems in subtropical regions and the benefits of applying these systems are identified. Measures to improve the performance of distributed energy systems in terms of energy saving and cost reduction and suggestions for proper application in such regions are summarised based on the analysing the results.
The design for a distributed energy system is a complicated task due to the coupling operation and mutual constraints among its subsystems. An optimal method for distributed energy systems design is therefore developed to identify the best system that can maximize the benefits compared with centralized energy systems. The system operation and equipment sizing of the distributed energy system are optimized simultaneously in the design method to ensure that the system achieves maximum energy saving and economic profits. A case study of an energy system retrofitting project is adopted to test and demonstrate the optimal design method. Performance of the optimized distributed energy system and the advantages achieved by this system in reducing primary energy consumption and operating cost are assessed. The matching performance of on-site generations, which indicates the extent of matching between the generated energy and the energy demand, and the efficiency of electric chillers are analysed and compared with that of the centralized energy system. In the practical operation of an energy system, actual operation condition variables, such as the electricity demand, are often very different from their predictions that used in the system design at planning and design stages. Such difference is taken as uncertainty. Uncertainties in design inputs (e.g. energy demand and energy price) and equipment degradations in operation result in that the actual performance of a distributed energy system deviates from the design expectations significantly. To ensure that distributed energy systems designed can operate at high performance when the actual working environment and equipment performance change over a large range, a robust optimal design method based on life-cycle performance analysis is developed. This method adopts a probabilistic approach, which is based on qualifying the uncertainties of design inputs and equipment degradations. Monte Carlo simulation method is adopted to model the uncertainty propagation and generate the probability distributions of the predicted system performance in the design process. The "probabilistic" life-cycle performance of distributed energy system is therefore obtained, and the method further identifies the optimum system which has the best life-cycle performance expectation under the above uncertain conditions concerned. A case study of a new development project is adopted to test and demonstrate the proposed robust optimal design method. The economic benefits and the performance robustness under different operating conditions of the designed distributed energy system is evaluated and analysed. Advantages of the proposed robust optimal design method compared with the design method that does not consider the life-cycle performance are identified by comparing the annual performance of distributed energy systems designed by those two methods, especially the performance in the latter years of life-cycle. The robustness of an energy system performance is identified as the ability of the system to maintain stable performance when operating conditions deviate from the design conditions. By analysing this robustness, the impacts of uncertainties on the performance of distributed energy systems can be assessed and compared. An index is proposed to quantify the performance robustness of distributed energy systems based on the stochastic results of Monte Carlo simulation. Comparison on the impacts of different uncertainties on the system performance robustness is conducted and measures to improve the performance robustness for distributed energy systems in subtropical regions are summarized.
Subjects: Hong Kong Polytechnic University -- Dissertations
Distributed generation of electric power
Electric power systems
Pages: xxiii, 160 pages : color illustrations
Appears in Collections:Thesis

Show full item record

Page views

Last Week
Last month
Citations as of May 28, 2023

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.