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Energy Quality Management for Building Clusters and Districts Using a Multi-Objective Optimization Approach
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Building Service and Energy Systems.
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

As society develops, energy needs and the warnings of global warming have become main areas of focus in many areas of human life. One such aspect, the building sector, needs to take responsibility for a significant portion of energy use. Researchers need to concentrate on applying innovative methods for controlling the growth of energy use. Apart from improving energy efficiency by reducing energy use and improving the match between energy supply and demand, energy quality issues have become a key topic of interest. Energy quality management (EQM) is a technique that aims to optimally utilize the exergy content of various renewable energy sources. The evaluation of the optimum energy systems for specific districts is an essential part of EQM.

The optimum energy system must follow the concept of “sustainability.” In other words, the optimization process should select the most suitable energy systems, which fulfill various sustainable requirements such as high energy/exergy performance, low environmental impacts and economic cost, as well as acceptable system reliability. A common approach to dealing with complex criteria involves multi-objective optimization, whereby multi-objective optimization is applied in the context of EQM of building clusters and districts (BCDs). In the present thesis, a multi-objective optimization process is proposed that applies a genetic algorithm (GA) to address non-linear optimization problems. Subsequently, four case studies are used to analyze how the multi-objective optimization process supports EQM of BCDs. Detailed information about these cases is provided below:

1. Basic case (UK): This case is used to investigate the application possibility of the approach in BCD energy system design and to analyze the optimal scenario changes, along with variations of optimization objective combinations. This approach is proven to be time-effective

2. Case 1 (Norway): The use of renewable energy sources can be highly intermittent and dependent on local climatic conditions; therefore, energy system reliability is a key parameter be considered for the renewable energy systems. This section defines system reliability as a constraint function and analyzes the system changes caused by the varying reliability constraints. According to the case, system reliability has been proven to be one of the most important objectives for the optimization of renewable energy systems.

3. Case 2 (China): In this section, the approach is applied in order to search for the optimal hybrid system candidates for a net-zero exergy district (NZEXD) in China. Economic analysis is included in this case study. Through the optimization process, the proposed approach is proven to be flexible and capable of evaluating distinct types of energy scenarios with different objective functions. Moreover, the approach is able to solve practical issues, such as identifying the most feasible options to the stepwise energy system transition for a specific case.

4. Case 3 (China): This section makes two major contributions. The first is to test the expansibility of inserting additional objectives into the approach; a parametric study is then applied to investigate the effects of different energy parameters. The second contribution is the conclusion that the optimum energy systems might vary significantly, depending on certain parameters.

According to the analyses in these case studies, the multi-objective optimization approach is capable of being a tool for future BCDs’ energy system design. It should also be noted that the findings from the case studies – especially the parametric study – might provide some interesting research topics for future work.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. , 79 p.
Series
TRITA-IES, 2015:06
National Category
Civil Engineering
Research subject
Civil and Architectural Engineering
Identifiers
URN: urn:nbn:se:kth:diva-179063ISBN: 978-91-7595-786-9 (print)OAI: oai:DiVA.org:kth-179063DiVA: diva2:881564
Public defence
2016-01-14, SalF3, Lindstedtsvägen 26, KTH, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20151211

Available from: 2015-12-11 Created: 2015-12-10 Last updated: 2015-12-11Bibliographically approved
List of papers
1. Energy Quality Management for New Building Clusters and Districts
Open this publication in new window or tab >>Energy Quality Management for New Building Clusters and Districts
2013 (English)Licentiate thesis, monograph (Other academic)
Abstract [en]

The level of concern regarding the total energy consumption in new building clusters and urban districts (BCDs) has increased recently. Rising living standards have led to a significant increase in building energy consumption over the past few decades. A great potential for energy savings exists through energy quality management (EQM) for new BCDs. Quality of energy measures the useful work potential of certain energy. EQM in this thesis is defined as reducing energy demand, applying distributed renewable energy sources, and utilizing energy technology in sustainable way. According to this definition, tasks of EQM include energy supply system optimization and energy demand prediction.

Based on EQM, the optimization of BCDs’ energy supply systems aims to search for the most appropriate scenario, which is a trade-off between various aspects, such as energy performance and environmental impacts as well as system reliability. A novel multi-objective optimization approach for new BCDs is established in this thesis. Optimization algorithm is known as Genetic Algorithm (GA), which is used to address non-linear optimization problems. Two case studies are included in this thesis: the U.K. eco-town residential BCDs case and the Norway office BCDs case.

The U.K. case examines the application possibility of the approach in practical design. Optimization objectives involved in this case are the life-cycle global warming potential of the system and the system exergy efficiency. The total life-cycle global warming potential is minimized while the exergy efficiency is maximized. Different types of energy supply system scenarios are recommended with different optimization objective combinations (equal-importance, slightly exergy efficiency-oriented and slightly environment-oriented). The results show that the proposed approach can feasibly be an optimal design tool in practical use.

To provide deeper insights into the problem, the Norway case checks the expansibility of inserting additional objectives into the approach. Loss of Power Supply Probability (LPSP), which is one of the system reliability indicators, is additionally included in the optimization objectives. For this case, the approach guarantees the optimal scenarios that cannot exceed the desired LPSP with minimum life-cycle global warming potential and maximum exergy efficiency. Optimal scenarios with different desired LPSP values (0, 1%, and 5%) are compared. Comparison results demonstrate that optimal scenarios change significantly along with variations of the desired LPSP values. Therefore, system reliability is proven as one of the most important objectives for renewable energy system optimization. In the future, this approach can be applied to complex problems with more objectives.

Besides energy supply system optimization, an effective and precise BCDs energy demand model is needed. This model should be capable of providing reliable inputs (energy demand and load profiles) for energy supply system optimization and reducing unnecessary energy consumption. In principle, energy demand in BCDs is a complex task because numerous design criteria influence energy performance, which is hard to plan and pre-calculate. Establishing such a model would require a thorough decision base that prioritizes these design criteria and generally distinguishes the more important criteria from the less important ones. The study uses general survey aims to collect and identify the design criteria that affect the BCDs energy demand model and to evaluate the priorities of each criterion using the fuzzy Analytical Hierarchy Process (AHP) method. Four main criteria – location, building characteristics, government, and outdoor surrounding characteristics – are established, along with 13 secondary criteria. The results show that the use of the AHP method can accurately guide the energy demand model and automatically rank significant criteria. The method can provide the weighting value for each criterion as well as the relative ranking for the energy demand model.

This thesis aims to provide a systematic and holistic EQM method for BCDs energy system design at the beginning of the decision-making stage.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2013. 113 p.
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-118561 (URN)
Presentation
2013-03-15, B26, Brinellvägen 23, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20130221

Available from: 2013-02-21 Created: 2013-02-20 Last updated: 2015-12-11Bibliographically approved
2. Energy quality management for building clusters and districts (BCDs) through multi-objective optimization
Open this publication in new window or tab >>Energy quality management for building clusters and districts (BCDs) through multi-objective optimization
2014 (English)In: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 79, 525-533 p.Article in journal (Refereed) Published
Abstract [en]

Renewable energy systems entail a significant potential to meet the energy requirements of building clusters and districts (BCDs) provided that local energy sources are exploited efficiently. Besides improving the energy efficiency by reducing energy consumption and improving the match between energy supply and demand, energy quality issues have become a key topic of interest. Energy quality management is a technique that aims at optimally utilizing the exergy content of various renewable energy sources. In addition to minimizing life-cycle CO2 emissions related to exergy losses of an energy system, issues such as system reliability should be addressed. The present work contributes to the research by proposing a novel multi-objective design optimization scheme that minimizes the global warming potential during the life-cycle and maximizes the exergy performance, while the maximum allowable level of the loss of power supply probability (LPSP) is predefined by the user as a constraint. The optimization makes use of Genetic Algorithm (GA). Finally, a case study is presented, where the above methodology has been applied to an office BCD located in Norway. The proposed optimization scheme is proven to be efficient in finding the optimal design and can be easily enlarged to encompass more relevant objective functions.

Keyword
Building clusters and districts, Exergy efficiency, Genetic algorithm, Life cycle CO2 equivalent, LPSP, Renewable energy system
National Category
Energy Systems
Identifiers
urn:nbn:se:kth:diva-142337 (URN)10.1016/j.enconman.2013.12.051 (DOI)000333946700055 ()2-s2.0-84893061812 (Scopus ID)
Note

QC 20140228

Available from: 2014-02-28 Created: 2014-02-28 Last updated: 2017-12-05Bibliographically approved
3. Transition path towards hybrid systems in China: Obtaining net-zero exergy district using a multi-objective optimization method
Open this publication in new window or tab >>Transition path towards hybrid systems in China: Obtaining net-zero exergy district using a multi-objective optimization method
Show others...
2014 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 85, 524-535 p.Article in journal (Refereed) Published
Abstract [en]

A hybrid energy system including both off-site and distributed energy sources, energy conversion technologies and operation methods, is a necessary step on a transition path towards a sustainable energy system. The challenge is to identify such a combination of design options that result in minimum life cycle cost (LCC) and maximum exergy efficiency (EE) at each phase of the transition path. In this paper, a time-effective multi-objective optimization method based on genetic algorithm (GA), is proposed for the transition path problem. The proposed model makes use of a fitness function approach to reduce the model into one objective function and to reduce the computational time. In a case study, the model is applied to a potential net-zero exergy district (NZEXD) in Hangzhou, China. Here, three possible hybrid energy scenarios and three preference treatment strategies are analyzed. The study suggests that the proposed approach is workable for the identification of the most feasible options to be gradually integrated in an NZEXD in a multi-stage process. In the Hangzhou case, with the reduction of investments in distributed energy components and escalating market prices of fossil fuels, distributed energy system (DES) may have more feasibility in the near future.

Keyword
Exergy efficiency, Genetic algorithm, Hybrid energy system, Life cycle cost, Net-zero exergy district, Transition path
National Category
Energy Engineering
Identifiers
urn:nbn:se:kth:diva-161040 (URN)10.1016/j.enbuild.2014.09.074 (DOI)000348880900052 ()2-s2.0-84908360984 (Scopus ID)
Note

QC 20150311

Available from: 2015-03-12 Created: 2015-03-06 Last updated: 2017-12-04Bibliographically approved
4. Parametric analysis of energy quality management for district in China using multi-objective optimization approach
Open this publication in new window or tab >>Parametric analysis of energy quality management for district in China using multi-objective optimization approach
Show others...
2014 (English)In: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 87, 636-646 p.Article in journal (Refereed) Published
Abstract [en]

Due to the increasing energy demands and global warming, energy quality management (EQM) for districts has been getting importance over the last few decades. The evaluation of the optimum energy systems for specific districts is an essential part of EQM. This paper presents a deep analysis of the optimum energy systems for a district sited in China. A multi-objective optimization approach based on Genetic Algorithm (GA) is proposed for the analysis. The optimization process aims to search for the suitable 3E (minimum economic cost and environmental burden as well as maximum efficiency) energy systems. Here, life cycle CO2 equivalent (LCCO2), life cycle cost (LCC) and exergy efficiency (EE) are set as optimization objectives. Then, the optimum energy systems for the Chinese case are presented. The final work is to investigate the effects of different energy parameters. The results show the optimum energy systems might vary significantly depending on some parameters.

Keyword
3E energy system, Energy quality management, Exergy efficiency, Genetic algorithm, Life cycle analysis, Parametric analysis
National Category
Energy Engineering
Identifiers
urn:nbn:se:kth:diva-152854 (URN)10.1016/j.enconman.2014.07.064 (DOI)000343337200066 ()2-s2.0-84907332263 (Scopus ID)
Note

QC 20141125

Available from: 2014-10-02 Created: 2014-10-02 Last updated: 2017-12-05Bibliographically approved

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