Trijenerasyon sistemlerinin modellenmesine yönelik yeni bir yaklaşım: Bir üniversite uygulaması
A new approach for the modelling of trigeneration system: A university applications
Yazar: KEZBAN BULUT
Danışman: DOÇ. DR. GÜLGÜN KAYAKUTLU
İstanbul Teknik Üniversitesi / Fen Bilimleri Enstitüsü / Endüstri Mühendisliği Ana Bilim Dalı / Endüstri Mühendisliği Bilim Dalı
Derece: Doktora, 2016
Konu: Endüstri ve Endüstri Mühendisliği = Industrial and Industrial Engineering
Dizin: Bayes ağlar = Bayes networks ; Lineer modeller =

Özet:
Gelişmiş ve gelişmekte olan ülkelerde nüfus artışı, kentsel gelişim ve sanayileşmeye paralel olarak enerji talebi büyük bir hızla artmaktadır. Geçmiş yıllarda ortalama %7-8 oranında olan elektrik enerjisi ve %4-5 olan genel enerji talep artış hızlarının önümüzdeki 15-20 yıllık süreçte de aynı şekilde devam etmesi beklenmektedir.Dolayısıyla enerji sektöründe hedef enerjinin ekonomik büyümeyi gerçekleştirecek ve sosyal gelişme hamlelerini destekleyecek şekilde, zamanında, yeterli miktarda, güvenilir, çevresel etkileri de göz önünde tutularak sağlanmasıdır.
Yerli, yeni ve yenilenebilir kaynakların kullanılması ve geliştirilmesine öncelik verilmesi, enerjinin üretiminden tüketimine kadar her aşamasında verimliliğin artırılması, yakıt esnekliğinin artırılması (üretimde alternatif enerji kaynağı kullanımına olanak sağlanması) ve enerji üretim sürecinin her aşamasında çevresel etkilerin göz önünde bulundurulması ülkemizin ana enerji politikaları içerisinde önem verilen unsurlar olmaktadır. Kojenerasyon ve trijenerasyon sistemleri tüm bu amaçların karşılanması için kullanılabilecek sistemlerin başında gelmektedir.
Trijenerasyon sistemleri aynı anda elektrik, ısıtma ve soğutma ihtiyacını karşılayabilen sistemler olduklarından geleneksel enerji üretim sistemlere göre çok daha verimli enerji üretim sistemleridir. Bu tez kapsamında bir üniversite kampüsü için yenilenebilir enerji kaynaklarını da kullanan bir trijenerasyon sistemi tasarlanmıştır. Tasarlanan tesisinin yıllık toplam maliyetini en küçükleyecek ve bir yıl için saatlik ısı,enerji ve soğutma ihtiyacını karşılayacak matematiksel bir model geliştirilmiştir. Ayrıca sistemin etkinliğini senaryolarala inceleyebilmek için Bayes Ağları kullanılmıştır.
Bu çalışma sonucunda önerilen trijenerasyon sistemi kullanımında aylık dönemsel değişimler en küçük maliyetlerle ile gösterilmiştir. Ayrıca, Bayes Ağları sayesinde sistemin her ünitesinin en etkin kullanımı da değişen senaryolara göre açıklanmıştır.

Abstract:
The population growth, urban development and industrialization cause rapid energy demand increases in the Globe. Electricity demand growth rate shows an average 7-8% in the past year and 4-5% of the overall energy demand is expected to continue in the same way in the next 15-20 years. Therefore, targets in the energy sector is supply the energy by considering the economic growth and to support social development efforts, timely, adequate, reliable, considering the environmental impact.
Because of the increase on quality standards and the demand for comfort rises, together with a higher degree of conscience towards environmental issues. The satisfaction of such comfort demands generally leads to a greater consumption of energy services while environmental conscience tries to compensate the greater consumption of fossil fuels and its consequences, by means of a more rational use of energy.
It is necessary to prefer using domestic and renewable sources, while new priorities are given to the development and efficiency in consumption is focused. National energy policies are expected to include increase in fuel flexibility and consideration of environmental impact of the energy generation processes. Cogeneration and trigeneration systems are among the systems that can be used to satisfy all of these objectives.
Cogeneration is the simultaneous generation of usable heat and power (usually electricity) in a single process. Cogeneration uses a variety of fuels and technologies across a wide range of sites, and scheme sizes. The basic elements of a cogeneration plant comprise one or more prime movers (a reciprocating engine, gas turbine, or steam turbine) electrical generators, or other machinery, where the steam or hot water generated in the process is utilized via suitable heat recovery equipment for use either in industrial processes, or in community heating and space heating.
Combined heating and power (CHP) systems and combined cooling, heating and power (CCHP) systems have become the main solutions to improve the energy efficiency and to reduce greenhouse gas (GHG) emissions, as a result of the rapid development of distributed energy supply systems.
Trigeneration is a single production process that combines the production of electrical, thermal and cooling energy starting from a single source of primary energy, namely that supplied by the fuel.Trigeneration (CCHP) can definable the evolution of cogeneration. Trigeneration systems are more efficient in power generation compared to the classical systems. Because trigeneration systems can simultaneously meet the power, heating and cooling needs from the same sources.In the recent years, systems for combined power, heat and cold production, so-called trigeneration systems, have been applied in increasing numbers.Such as universities, hospitals, airports, cold storage facilities, industrial plants especially food industry such as dairy, pasta industry, sugar factories, etc. require a supply of electricity, heat and cold.
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A typical CCHP system has several different components for meeting the energy demand. Such as gas turbine (or reciprocating engine) for the prime mover, a heat recovery steam generator (HRSG) and an auxiliary boiler to produce heating, absorption or electrical chiller. There are many options for a trigeneration system and because of these options energy management is a very complex issue.
This thesis study suggests a framework to use Linear programming and Bayesian network analysis to specify the best trigeneration system component capacity and establish future demand state scenario by using Bayesian network scenario analysis. Study contributes to the trigeneration research for combining these two methods.
The framework is constructed in three steps. In the first step, Linear programming model is developed and this model is used for university campus energy demand satisfaction. All the components of trigeneration system is defined by expert decision and for a year we analysed which source is used.In the second step Bayesian network is used for scenario analysis.In the last and third step,the best scenario is selected and Linear programming model is implemented for this trigeneration component size.
This thesis is designed to construct a model for using an optimized trigeneration system using renewable energy sources in a university campus.The Kırıkkale university is selected for this implemantation. Linear Programming Model is developed for minimization of the total annual cost of the designed facility and meeting the heat, power and cooling needs.This model is constructed for a one year period. Electricity, heat and cooling demand is satisfied for a year by trigeneration and other renewable sources. In this model, solar energy is used for renewable energy source. Solar energy is used for electricity demand by photovoltaic arrays and it used for heating by thermal collector.
In fist phase Linear programming model is constructed. The second phase of this thesis is scenario analysis phase. Firstly, Bayesian network is constructed for initial situation. Then six different scenarios are analyzed by Bayesian network.
Bayesian Networks (BNs), also known as Bayesian belief networks or Bayes nets, are a kind of probabilistic graphical model that has getting very popular to practitioners day by day. This popularity is coming from due to the powerful probability theory involved, which makes them able to deal with a wide range of problems.
Bayesian networks are also called belief networks and Bayesian belief networks. Previously, the term causal probabilistic networks has also been used. A BN is a network of nodes connected by directed links with a probability function attached to each node. The network (or graph) of a BN is a directed acyclic graph (DAG), i.e., there is no directed path starting and ending at the same node.
Bayesian modeling techniques have many characteristic that make them useful in many real-life data analysis and management questions. Some of these characteristics are providing natural way to handle missing data, combining of data with expert knowledge, learning about causal relationships between variables, provide a method for avoiding over fitting of data.
Bayesian networks have many advantages such as suitability for small and incomplete data sets, structural learning possibility, combination of different sources
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of knowledge, explicit treatment of uncertainty, support for decision analysis, and fast responses.
A Bayesian network (BN) is used to model a domain containing uncertainty in some manner. This uncertainty can be due to imperfect understanding of the domain, incomplete knowledge of the state of the domain at the time where a given task is to be performed, randomness in the mechanisms governing the behavior of the domain, or a combination of these.
Bayesian Network is used to analyze different combination of resources in scenarios. Application of the optimization model gives an annual based system utilization plan minimizing the costs with seasonal changes. Besides, using Bayesian Network gave the opportunity to propose system utilization scenarios for mazimizing the effectivity.
In this study, we suggested a new modeling approach for modeling of trigeneration system. We used this new approach for specifying the system components of trigeneration system for a university campus. In the analysis, Bayesian method is used to handle the uncertainties in the energy production process by trigeneration systems.