EE- Enerji Bilim ve Teknoloji Lisansüstü Programı - Doktora
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Konu "Cooling systems" ile EE- Enerji Bilim ve Teknoloji Lisansüstü Programı - Doktora'a göz atma
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ÖgePlanning And Stochastic Evaluation Of Combined Cooling Heat And Power Systems Under Uncertainty(Energy Institute, 2019-01-29) Ersöz, İbrahim ; Çolak, Üner ; 10255199 ; Energy Sciences and Technologies ; Enerji Bilim ve TeknolojiCCHP (Combined Cooling Heat and Power) systems are the most well-known technologies for efficient energy usage and it usually refers to simultaneous production of cooling, heating and power from a single energy source. CCHP plants are built as decentralized systems, and they are operated close to where it is needed. Thus, CCHP systems are considered as more efficient, profitable, reliable and environmentally friendly systems compared with conventional generating plants. Nonetheless, CCHP systems or any other energy conversion systems should be designed and operated effectively to gain the expected advantages. It is not an easy decision for a SME (small and medium size enterprises) to invest in CCHP systems. Decisions for investments are generally taken by the conventional method, which relies on the result of an economic analysis with the assumption that variables will remain stable over the time the analysis is made. However, this kind of systems is dynamic and all the parameters are subject to change until the day the CCHP system expires economically. Thereby, CCHP systems work under uncertainty conditions during their economic life. The technical and financial performance of the system is affected by various parameters which include the fluctuation of energy loads, working hours, energy prices, exchange rates and interest rates. Accordingly, evaluating only the scenario where the current values of variables are taken into account may not help investors in decision making owing to uncertainties, the probability of occurrence of uncertainties and their outcomes. The analysis held in this study has been based on real and current operational data of an existing industrial facility located in Istanbul. Beside that, This study has two stages to assess the uncertainties in CCHP systems. The main purpose of the first part of the study is to specify a model and a methodology to select the best CCHP scheme in the presence of uncertainties. Differing from previous studies, this study examined the uncertainties in CCHP systems and evaluated the impacts of these uncertainties on the operational decision-making process as well as the stochastic impacts on the decision making process of the given investment. In the first stage, the system has been evaluated as a sole CHP system in the light of the updated value of the variables, then the system has been designed as a CCHP system by adding the absorption chiller with the intention of covering the cooling demand partly or fully. Setting the correct load capacity and scheduling is important while deciding on whether the most profitable system should be CHP or CCHP for a given plant. For this propose, macro program in Microsoft Excel has been run in order to determine the most proper capacity for the absorption chiller that will maximize the total annual saving. After determining the most proper cooling load of the absorption chiller, the system has been re-evaluated in the economical aspect. In another subsection, sensitivity analysis has been applied with the purpose of seeing the impact of the variables on the result. Following this, a new formula has been created to analyse and calculate the effects of variables on the result of the objective function on a percentage basis. Genetic algorithm is used to see the best case scenario in given constrains of uncertain variables. The result of this forms a reference for the comparison of the actual situation with the best case scenario. As a last step, possible results of the total annual saving have been re-calculated by using probabilistic models under non-parametric stochastic method. The analyses conducted in first stage have specifically addressed the variables that affect the economic feasibility of the investment and the uncertainties that may affect the investment any time in the systems economic life span. The main objective is to analyse all the possibilities and changes of the uncertain parameters during the life of the system to help investors see the possibility of the occurrence of the best and worst case scenario before making investment decision. Moreover, it is shown that some certain criteria should be satisfied in order for CCHP power plants to be more feasible. The results concluded from this stage are mentioned more in details in the last section of the manuscript. This stage of study has revealed that an evaluation made solely by considering the current values of the variables of the system is not sufficient to analyze the profitability of the investment. Apart from the conventional evaluation, the random changes of the independent variables at any time should be evaluated in order to see how they affect the profitability. Accordingly, it has been concluded that the deterministic evaluation is not sufficient to assess CCHP systems by its own and the stochastic evaluation gives a broader point of view in terms of overseeing all possible risks. The first stage of the thesis presented a very wide range of possibilities to assess the profitability of the system. At second stage, a re-evaluation was carried out in order to make a clearer analysis considering the historical data of the independent variables that affect the applicability of the system and the correlations between the variables, if any, and the probability density functions of the variables. Second stage of the study has been aimed to estimate how the profitability of a CCHP system, which is considered investable based on current values, will change throughout its economic life by adopting stochastic methods. Accordingly, the system has been analysed under four different simulation methods, namely parametric method, historical trend method, Monte Carlo method and scenario-based method, and their results have been compared. Among all the studied methods, the Monte Carlo and the historical trend methods directly take historical data as a reference. The parametric method, on the other hand, uses only the parameters of the mean and standard deviation from the historical data as a reference and thereafter assumes that all parameters will follow the normal distribution. Differing from these methods, the scenario-based method tries to determine where the objective function will be concentrated by considering all probable scenarios. In this regard, the parametric method gives results across the widest range, offering an unclear prediction about future results. The Monte Carlo method gives the highest mean value, while the historical trend method gives probabilities in a narrower range. The scenario-based method, meanwhile, offers a broader prediction than the historical trend method and also predicts a lower mean value for tas. Second stage of the study has showed that Investments in energy systems, including CCHP systems, face uncertainty. To answer whether an investment will remain profitable in the midst of these uncertainties, different methods can be applied either using past data or considering all possible scenarios. Although each method used in this study has certain advantages and disadvantages, all four methods can be used to evaluate CCHP systems at the investment stage. Since prices in almost all countries, particularly in the energy market, may not move in line with the historical trend, this study has shown that the scenario-based method is most appropriate to adopt given the comparisons and contrasts it provides.