基于多目标加权灰靶决策模型的节能服务公司选择研究
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摘要
用能方对节能服务公司(Energy Service Company,ESCO)的选择关系到合同能源管理(Energy Performance Contracting,EPC)能否顺利实施。从用能方的角度,采用灰色系统理论中的多目标加权灰靶决策模型,对存在多决策目标的ESCO选择问题进行研究。通过层次分析法确定ESCO的11个决策目标的决策权数,根据综合效果测度值的比较,最终实现最优对策的选择。本文为用能方的ESCO选择问题提供了一种新的思路。
Energy User(EU)can benefit from implementing Energy Performance Contract(EPC)projects.However,it is unclear how an EU can select an Energy Service Company(ESCO)efficiently from multiple candidates so as to maximize its interests.This arouses two particular questions:one is which method can be used by the EU to select asuitable ESCO,and the other is which indicators can be adopted to achieve effective outcomes toward ESCO selection.In practice the main concern of the EU is how to determine an optimal ESCOdepending on insufficient ESCO information.This paper aims to investigate the optimal ESCO selection problem from the perspective of the EU by using the weighted multi-objective gray target decision model.The weighted multi-objective gray target decision model is first introduced,followed by determining main decision-making objectives of ESCO selection based on a two-round expert consultation.Then according to different types of the decision-making objectives(i.e.benefit-based,cost-based,moderate),the objective effect sample matrix are formulated and the objective effects' threshold values are set.Finally,by calculating the uniform effect measurement matrix,the dimension of the three ESCOs' objective effect sample matrix is dispelled.The ultimate optimum of ESCO is identified by judging the three ESCOs' synthetic effect measurement values.The study reveals that in the process of determining an optimum ESCO,special attention should be paid on aspects including‘the qualification of ESCO',‘the reputation of ESCO',‘the quality of energy-saving equipment',‘energy-saving retrofit period',‘energy-saving benefit sharing period',‘energy-saving design',‘participants' energy-saving benefit sharing proportion',‘the damage extent to the original building components',‘energy-saving during the contract period',‘feasibility to spot energy-saving volume'and‘pre-payment'.It is also proved that applying the weighted multi-objective gray target decision model to optimum ESCO selection could largely help the EU to address the problem effectively even the EU faces a dilemma of scarce data and information about the ESCO.
Energy User(EU)can benefit from implementing Energy Performance Contract(EPC)projects.However,it is unclear how an EU can select an Energy Service Company(ESCO)efficiently from multiple candidates so as to maximize its interests.This arouses two particular questions:one is which method can be used by the EU to select asuitable ESCO,and the other is which indicators can be adopted to achieve effective outcomes toward ESCO selection.In practice the main concern of the EU is how to determine an optimal ESCOdepending on insufficient ESCO information.This paper aims to investigate the optimal ESCO selection problem from the perspective of the EU by using the weighted multi-objective gray target decision model.The weighted multi-objective gray target decision model is first introduced,followed by determining main decision-making objectives of ESCO selection based on a two-round expert consultation.Then according to different types of the decision-making objectives(i.e.benefit-based,cost-based,moderate),the objective effect sample matrix are formulated and the objective effects' threshold values are set.Finally,by calculating the uniform effect measurement matrix,the dimension of the three ESCOs' objective effect sample matrix is dispelled.The ultimate optimum of ESCO is identified by judging the three ESCOs' synthetic effect measurement values.The study reveals that in the process of determining an optimum ESCO,special attention should be paid on aspects including‘the qualification of ESCO',‘the reputation of ESCO',‘the quality of energy-saving equipment',‘energy-saving retrofit period',‘energy-saving benefit sharing period',‘energy-saving design',‘participants' energy-saving benefit sharing proportion',‘the damage extent to the original building components',‘energy-saving during the contract period',‘feasibility to spot energy-saving volume'and‘pre-payment'.It is also proved that applying the weighted multi-objective gray target decision model to optimum ESCO selection could largely help the EU to address the problem effectively even the EU faces a dilemma of scarce data and information about the ESCO.
引文
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[10]杨锋,何慕佳,梁樑.基于多属性逆向拍卖的节能服务公司选择研究[J].中国管理科学,2016,23(5):98-106.
[11]Bertoldi P,Rezessy S,Vine E.Energy service companies in European countries:Current status and a strategy to foster their development[J].Energy Policy,2006,34:1818-1832.
[12]Fang W S,Miller S M,Yeh C C.The effect of ESCOs on energy use[J].Energy Policy,2012,51:558-568.
[13]Suhonen N,Okkonen L.The energy services company(ESCo)as business model for heat entrepreneurship—a case study of North Karelia,Filand[J],Energy Policy,2013,61:783-787.
[14]Vine E.An international survey of the energy service company(ESCO)industry[J].Energy Policy,2005,33:691-794.
[15]邓聚龙.灰色控制系统[J].华中工学院学报,1982,7(3):9-18.
[16]刘思峰,袁文峰,盛克勤.一种新型多目标智能加权灰靶决策模型[J].控制与决策,2010,25(8):1159-1163.
[17]刘思峰.灰色系统理论的产生与发展[J].南京航空航天大学学报,2004,36(2):1159-1163.
[18]杨耀红,谭攀静.基于多目标加权灰靶决策的供应链供应商选择[J].华北水利水电学院学报,2012,(2):137-139.
[19]代岚,雷莎莎,周维雅.GIS和灰色就差法在应急避难场所选址中的应用[J].中国应急救援,2016,(3):21-25.
[20]高旭阔,张晓雪.低碳经济下既有建筑节能改造方案优选研究[J].节能,2015,(11):53-57.
[21]谢伟,刘宝新.多目标智能加权灰靶决策模型在军事运输方式选择中的应用[J].军事交通学院学报,2016,18(10):15-19.
[22]梁冰,秦冰,孙维吉,等.智能加权灰靶决策模型在煤与瓦斯突出危险评价中的应用[J].煤炭学报,2013,38(9):1611-1615.
[23]谢千东,赵辉.多目标加权灰靶在空调装备维修决策的应用研究[J].装备制造技术,2015,(12):100-103.
[24]高彩云,崔希民.基于多因素加权灰靶决策模型的滑坡灾害危险性评价[J].中南大学学报(自然科学版),2016,47(2):524-530.
[2]Painuly J P,Park H,Lee M K,et al.Promoting energy efficiency financing and BEMCOs in developing countries:mechanisms and barriers[J].Journal of Cleaner Production,2003,11(6):659-665.
[3]张冬梅.我国节能服务市场主体的博弈分析[J].生态经济,2012,(5):107-109.
[4]刘桦,李晨.建筑节能服务公司的国内外比较研究[J].建筑经济,2012,(11):76-79.
[5]Guertler P,Smith W.Energy efficiency in the sustainable refurbishment of high-rise residential buildings:Mapping out an integrated policy approach[J].Energy for sustainable Development,2006,(4):37-45.
[6]Bannai M,Tomita Y,Ishida Y,et al.Risk hedging against the fuel price fluctuation in energy service business[J].Energy,2007,32(11):2051-2060.
[7]Okey N,Akma U.Analysis of BEMCO activities using country indicators[J].Renewable and Sustainable Energy Reviews,2010,(14):2760-2771.
[8]Okay E,Okay N,Konukman A E S,et al.Views on Turkey’s impending ESCO market:Is it promising?[J].Energy Policy,2008,36:1821-1835.
[9]Stuart E,Larsen P H,Goldman C A,et al.A method to estimate the size and remaining market potential of the US ESCO(energy service company)industry[J].Energy,2014,(77):362-371.
[10]杨锋,何慕佳,梁樑.基于多属性逆向拍卖的节能服务公司选择研究[J].中国管理科学,2016,23(5):98-106.
[11]Bertoldi P,Rezessy S,Vine E.Energy service companies in European countries:Current status and a strategy to foster their development[J].Energy Policy,2006,34:1818-1832.
[12]Fang W S,Miller S M,Yeh C C.The effect of ESCOs on energy use[J].Energy Policy,2012,51:558-568.
[13]Suhonen N,Okkonen L.The energy services company(ESCo)as business model for heat entrepreneurship—a case study of North Karelia,Filand[J],Energy Policy,2013,61:783-787.
[14]Vine E.An international survey of the energy service company(ESCO)industry[J].Energy Policy,2005,33:691-794.
[15]邓聚龙.灰色控制系统[J].华中工学院学报,1982,7(3):9-18.
[16]刘思峰,袁文峰,盛克勤.一种新型多目标智能加权灰靶决策模型[J].控制与决策,2010,25(8):1159-1163.
[17]刘思峰.灰色系统理论的产生与发展[J].南京航空航天大学学报,2004,36(2):1159-1163.
[18]杨耀红,谭攀静.基于多目标加权灰靶决策的供应链供应商选择[J].华北水利水电学院学报,2012,(2):137-139.
[19]代岚,雷莎莎,周维雅.GIS和灰色就差法在应急避难场所选址中的应用[J].中国应急救援,2016,(3):21-25.
[20]高旭阔,张晓雪.低碳经济下既有建筑节能改造方案优选研究[J].节能,2015,(11):53-57.
[21]谢伟,刘宝新.多目标智能加权灰靶决策模型在军事运输方式选择中的应用[J].军事交通学院学报,2016,18(10):15-19.
[22]梁冰,秦冰,孙维吉,等.智能加权灰靶决策模型在煤与瓦斯突出危险评价中的应用[J].煤炭学报,2013,38(9):1611-1615.
[23]谢千东,赵辉.多目标加权灰靶在空调装备维修决策的应用研究[J].装备制造技术,2015,(12):100-103.
[24]高彩云,崔希民.基于多因素加权灰靶决策模型的滑坡灾害危险性评价[J].中南大学学报(自然科学版),2016,47(2):524-530.