基于情景模糊动态MABAC的可再生能源选择方法
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摘要
为构建一套有效选择可再生能源的指标体系和评价方法,考虑多种能源的不同特性对环境与经济承载力的制约,围绕经济、环境、技术、社会政治及能源来源质量5个维度,提出能够全面评价可再生能源的指标体系和基于情景模糊动态MABAC (多属性边界逼近区域比较法)的评价方法.在评价方法构建上,采用情景模糊集(picture fuzzy set,PFS)对MABAC进行改进,实现在多准则决策问题中利用模糊信息进行精准决策.在此基础上,将时间因素与MABAC相拟合,进一步求得可再生能源与边界逼近区域的贴近度,确定与地方环境承载力相适宜的可再生能源.结果表明:在考虑时间因素的条件下,风能、太阳能、水能与边界逼近区域的贴近度分别为0. 102 66、-0. 133 90、0. 040 16,说明风能为经济效益最佳的可再生能源,太阳能为可利用能源;在未考虑时间因素条件下,所得到的贴近度则与之存在差异,风能、太阳能、水能与边界逼近区域的贴近度分别为0. 011 29、-0. 058 00、0. 023 40.研究显示,基于情景模糊动态MABAC评价结果与实际情况更相符,且通过模型的验证过程来看,该模型不仅可以提高结果的精确性,也同样易于实施和便于推广.
In order to construct an effective indicator system and evaluation method for renewable energy,consider the constraints of different energy characteristics on environmental and economic carrying capacity, and propose five dimensions of economic,environmental,technological,socio-political and energy source quality. It can comprehensively evaluate the indicator system of renewable energy and the evaluation method based on the scenario fuzzy dynamic multi-attribute boundary approximation regional comparison( MABAC) method. In the construction of evaluation method,the improvement of MABAC is improved by using picture fuzzy set( PFS).Realize the use of fuzzy information for precise decision making in multi-criteria decision-making problems. On this basis,the time factor is fitted with MABAC to further obtain the closeness of renewable energy and boundary approximation,and determine the appropriate environmental carrying capacity renewable energy. The results show that under the condition of time factor,the closeness of wind energy,solar energy,water energy and boundary approximation area are 0. 102,66,-0. 133,90,0. 040,16,respectively,indicating that wind energy is the most economically beneficial renewable energy source. Solar energy is available energy; obtained without considering the time factor The closeness is different from the similarity. The closeness of wind energy,water energy,solar energy and boundary approximation area are 0. 011,29,-0. 058,00,0. 023,40. The research shows that the results based on the scene fuzzy dynamic MABAC are more consistent with the actual situation,and through the model In the verification process,the model can not only improve the accuracy of the results,but also be easy to implement and promote.
In order to construct an effective indicator system and evaluation method for renewable energy,consider the constraints of different energy characteristics on environmental and economic carrying capacity, and propose five dimensions of economic,environmental,technological,socio-political and energy source quality. It can comprehensively evaluate the indicator system of renewable energy and the evaluation method based on the scenario fuzzy dynamic multi-attribute boundary approximation regional comparison( MABAC) method. In the construction of evaluation method,the improvement of MABAC is improved by using picture fuzzy set( PFS).Realize the use of fuzzy information for precise decision making in multi-criteria decision-making problems. On this basis,the time factor is fitted with MABAC to further obtain the closeness of renewable energy and boundary approximation,and determine the appropriate environmental carrying capacity renewable energy. The results show that under the condition of time factor,the closeness of wind energy,solar energy,water energy and boundary approximation area are 0. 102,66,-0. 133,90,0. 040,16,respectively,indicating that wind energy is the most economically beneficial renewable energy source. Solar energy is available energy; obtained without considering the time factor The closeness is different from the similarity. The closeness of wind energy,water energy,solar energy and boundary approximation area are 0. 011,29,-0. 058,00,0. 023,40. The research shows that the results based on the scene fuzzy dynamic MABAC are more consistent with the actual situation,and through the model In the verification process,the model can not only improve the accuracy of the results,but also be easy to implement and promote.
引文
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[24]GIGOVIC'L,PAMUCAR D,LUKIC'D,et al.GIS-fuzzy DEMATELMCDA model for the evaluation of the sites for ecotourism development:a case study of‘Dunavski kljucˇ’Region,Serbia[J].Land Use Policy,2016(58):348-365.
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[2]任东明.论中国可再生能源政策体系形成与完善[J].电器与能效管理技术,2014(10):1-4.REN Dongming.On the formation and improvement of China's renewable energy policy system[J].Electric Apparatus and Energy Efficiency Management Technology,2014(10):1-4.
[3]郭焦锋,曹小奇.可再生能源的战略选择[J].新经济导刊,2012(11):81-85.GUO Jiaofeng,CAO Xiaoqi.Strategic options for renewable energy[J].New Economy Guide,2012(11):81-85.
[4]ERDOGDU E.On the wind energy in Turkey[J].Renewable&Sustainable Energy Reviews,2009,13(6):1361-1371.
[5]谌紫娟.江西省能源结构优化调整研究[D].南昌:江西师范大学,2012.
[6]ZHANG Caiqing,LU Yanchao.Study on the generating energy structure optimization based on the hybrid intelligence algorithm and the comprehensive influence factor[J].Dynamics of Continuous,Discrete and Impulsive Systems-Series B:Applications&Algorithms,2006(13):765-769.
[7]周大地.亚太国家温室气体排放清单及其缓解策略:会议报告和工作组讨论纪要[J].人类环境杂志,1996(4):220-227.ZHOU Dadi.Asia-Pacific National greenhouse gas emission inventory and mitigation strategies:meeting report and working group discussion minutes[J].Journal of the Human Environment,1996(4):220-227.
[8]KAHRAMAN C,HSAN K,CEBI S.A comparative analysis for multi-attribute selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy analytic hierarchy process[J].Energy,2009,34(10):1603-1616.
[9]KAYA T,KAHRAMAN C.Multi-criteria renewable energy planning using an integrated fuzzy VIKOR&AHP methodology:the case of istanbul[J].Energy,2010,35(6):2517-2527.
[10]MRAN爦,EREN M,SHIRAZ S E,et al.Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey[J].Renewable Energy,2015(75):617-625.
[11]吴昌伦.中国可再生能源发展展望[J].中国能源,1997(3):42-44.WU Changlun.Prospects for China's renewable energy development[J].China Energy,1997(3):42-44.
[12]于渤,黎永亮,崔志.基于可持续理论的能源资源价值分析模型[J].中国管理科学,2005,13(S1):499-503.YU Bo,LI Yongliang,CUI Zhi.Energy resource value analysis model based on sustainable development theory[J].China Management Science,2005,13(S1):499-503.
[13]娄伟.新能源城市理论体系构建研究[J].创新,2014(2):10-17.LOU Wei.Research on the construction of theoretical system of new energy cities[J].Innovation,2014(2):10-17.
[14]TASRI A,SUSILAWATI A.Selection among renewable energy alternatives based on a fuzzy analytic hierarchy process in Indonesia[J].Sustainable Energy Technologies&Assessments,2014(7):34-44.
[15]王德元,陈汉平,杨海平等.生物质能利用技术综合评价研究[J].能源工程,2009(1):25-29.WANG Deyuan,CHEN Hanping,YANG Haiping,et al.Comprehensive evaluation of biomass energy utilization technology[J].Energy Engineering,2009(1):25-29.
[16]阮娴静,杨青.我国新能源汽车技术指标体系及评价模型[J].科技管理研究,2010(8):32-34.RUAN Xianjing,YANG Qing.China new energy automobile technical index system and evaluation model[J].Research on Science and Technology Management,2010(8):32-34.
[17]ARRIBAL C.PV-wind hybrid system performance:a new approach and a case study[J].Renewable Energy,2010,35(1):128-137.
[18]PAL N.Emerging trend in life cycle assessment of various photovoltaic systems[J].Cell,2014(4):1206-1217.
[19]ANNETTE E,VLADIMIRS C.Assessment of sustainability indicators for renewable energy technologies[J].Renewable and Sustainable Energy Reviews,2009(13):1082-1088.
[20]BENAYOUN R,ROY B,SUSSMANN B.ELECTRE:une méthode pour guider le choix en présence de points de vue multiples SEMA(metra international)[J].Direction Scientifique,1966,49(6):179-191.
[21]HWANG C L,YOONK.Multiple attribute decision making:methods and applications[J].New York:Spriner-Verlag,1981:173-184.
[22]PAMUCˇAR D,C'IROVIC'G.The selection of transport and handling resources in logistics centers using multi-attributive border approximation area comparison(MABAC)[J].Expert Systems with Applications,2015,42(6):3016-3028.
[23]BOZANIC D,PAMUCAR D,KAROVIC'S.Use of the fuzzy AHP-MABAC hybrid model in ranking potential locations for preparing laying-up positions[J].Military Technical Courier,2016,64(3):705-729.
[24]GIGOVIC'L,PAMUCAR D,LUKIC'D,et al.GIS-fuzzy DEMATELMCDA model for the evaluation of the sites for ecotourism development:a case study of‘Dunavski kljucˇ’Region,Serbia[J].Land Use Policy,2016(58):348-365.
[25]XUE Yixi,YOU Jianxin.An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information[J].Applied Soft Computing,2016(38):703-713.
[26]ATTANASSOV K T.Intuitionistic fuzzy sets[J].Fuzzy Sets and Systems,1986,20(1):87-96.
[27]TORRA V,NARUKAWAY.On hesitant fuzzy sets and decision[C]//TORRA V,IEEE International Conference on Fuzzy Systems,IEEE,2009:1378-1382.
[28]CUONG B C,KREINOVICH V.Picture fuzzy sets-a new concept for computational intelligence problems[J].In Proceeding of 2013third world congress on information and communication technologies,2013(12):1-6.
[29]LE H S.DPFCM:a novel distributed picture fuzzy clustering method on picture fuzzy sets[J].Expert Systems with Applications,2015,42(1):51-66.
[30]YAGER R R.On ordered weighted averaging aggregation operators in multicriteria decision making[J].Readings in Fuzzy Sets for Intelligent Systems,1993,18(1):80-87.
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