تجزیه و تحلیل حساسیت از ارزیابی فن آوری، اقتصادی و پایداری نیروگاه ها با استفاده از فرایند تحلیل سلسله مراتبی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|26035||2009||11 صفحه PDF||سفارش دهید||7894 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Energy Policy, Volume 37, Issue 3, March 2009, Pages 788–798
Technological, economic and sustainability evaluation of power plants by use of the analytic hierarchy process and nine end node criteria for a reference scenario based on subjective criteria weighting has been presented in a previous paper by authors. However, criteria weight variations may substantially modify overall evaluations and rankings of power plants. The current paper presents a sensitivity analysis with four alternative scenarios (sets of criteria weights) compared with the reference scenario. The results show that priority to “technology and sustainability” favors renewable energy power plants, while priority to “economic” criteria favors mainly nuclear power plants and less the four types of fossil fuel power plant.
An integrated technological, economic and sustainability evaluation of ten types of power plants by use of the analytic hierarchy process and nine end node criteria, for a reference scenario based on subjective criteria weighting has been presented by Chatzimouratidis and Pilavachi (Chatzimouratidis and Pilavachi, 2008c). Nevertheless, criteria weight variations, due to different decision makers may lead to significant alterations in the overall evaluation and ranking of ten types of power plant. Especially, when assessments are subjective or there is uncertainty, many different possible cases should be evaluated (Ozdemir and Saaty, 2006). Therefore, sensitivity analysis is an useful tool used to analyze several aspects of the energy sector like economic policies for greenhouse gas emissions reduction (Georgopoulou et al., 2006), climate alteration due to power generation (Zwaan and Gerlagh, 2006) and power plant technology and economics (Hamed et al., 2006). Sensitivity analysis has also been applied for the analysis of power plant impact on the living standard using the analytic hierarchy process (Chatzimouratidis and Pilavachi, 2007, Chatzimouratidis and Pilavachi, 2008a and Chatzimouratidis and Pilavachi, 2008b). Alternate scenarios have been widely applied in the energy sector (Costantini et al., 2007; Diakoulaki and Karangelis, 2007; Ghanadan and Koomey, 2005; Trevisani et al., 2006). Sensitivity analysis can be applied to several levels of the energy sector. For example, it was applied to a certain element of power plant operation like nuclear safety (Marseguerra et al., 2005), a power plant type like a small hydro power plant (Kaldellis et al., 2005) or even to power plant operation and management in a deregulated market (Carraretto, 2006). Moreover, the analytic hierarchy process and sensitivity analysis has been proven to be a useful tool for analyzing a wide range of problems, such as hospital selection (Wu et al., 2007) or bankruptcy prediction (Park and Han, 2002). This study presents a sensitivity analysis comprising of four alternative scenarios covering different sets of criteria weights, which are compared with the reference scenario. Before applying sensitivity analysis, the reference scenario is presented and overall score and ranking is carried out according to this scenario. Sensitivity analysis describes alterations of scores and rankings according to variations of criteria weights with regard to this scenario. Average values based on reliable international literature and organizations were used for power plant evaluation against end node criteria. Nevertheless, different criteria weights incorporated in the sensitivity analysis can compensate for possible variations of such values. The number of cases examined should be the smaller one to give the most complete information on scores, rankings and tendencies of power plants to the decision maker, without confusing him.
نتیجه گیری انگلیسی
Power plant evaluation incorporates a great number of criteria among which the most important are technological, economic and sustainability. Decision making under certain circumstances makes it necessary to examine in advance all those possible criteria weight combinations that may lead to different conclusions. Ten types of power plant were evaluated under several criteria and nine end node subcriteria grouped appropriately. Local and global impact of criteria and subcriteria weight changes to the overall score and ranking were thoroughly analyzed. It was found that the five types of renewable energy based power plant are the best solutions ensuring the future energy of the generations to come. Especially, when the “technology and sustainability” criterion takes priority, their scores increase impressively relative to fossil fuel based and nuclear power plants. The most stable and reliable investment to the criteria weight fluctuations are wind plants that have great scores under all circumstances. When the “economic” criterion takes priority, renewable energy power plants still rank in the first positions with the exception of biomass plants that prove to have the worst economic data. Nuclear power plants are one of the best solutions, when economic criterion weight increases. They rank in the sixth position after renewable energy based plants when “technology and sustainability” criterion weight is equal or over 50%, except for the case where this criterion has 100% weight, in which they are also surpassed by coal/lignite power plants. Fossil fuel based power plants are in the last four positions in almost all cases of the “technology and sustainability” and “economic” criteria weighting, although they improve slightly their scores, when the economic criterion takes priority. Sensitivity analysis is a useful tool for decision support as it analyzes the advantages and disadvantages of each power plant type under different possible criteria weight combinations, thus making it possible for the decision maker to choose the best solution according to available data.