基于不确定性优化方法的能源环境系统规划模型研究
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摘要
对于我国大中型城市,能源系统管理中的供需矛盾、能源结构不合理、能源分配失调、减排控制不力和能源技术落后等障碍带来了一系列能源、经济、环境和社会问题。而且,数据、报告和相关研究显示,在未来的几十年内,随着社会经济的快速发展,这些问题还有进一步加剧的趋势,从而制约我国城市的可持续发展。由此,需要开展能源与环境系统综合规划,为能源开发与利用、能源结构优化、能源合理调配、环境质量改善和能源技术革新等决策问题提供科学依据。
然而,能源与环境系统规划面临诸多挑战。在能源储量及时空分布、能源技术发展、气候变化、环境容量、社会经济规划和国际能源格局等因素的交互作用下,将煤炭、石油和天然气等化石能源以及风能、太阳能和核能等可再生能源,通过能源生产、输配、利用和减排等多级时空调配过程,满足包括工业生产、居民生活、市政、商业、交通等在内的终端需求,从而实现能源、经济、环境和社会综合效益的最大化。其中,系统内部因子多重属性及其多层互动关系呈现出显著的不确定性和动态特征。此外,由于人类认知水平的局限性,致使因素的定量表征具有不确定性和时空异质性。进而,使得以实现能源、经济、环境和社会可持续发展为目标的能源政策分析和战略制定过程异常复杂。传统的能源模型大都缺乏对这些复杂性和不确定性的准确反映,并且国外成熟技术由于对我国具体国情考虑不足而难以适用。
因此,本研究以我国大中型城市能源与环境系统管理中的具体问题为目标,从能源、环境、经济和社会系统耦合分析的角度出发,针对多重不确定性、复杂性和动态性等一系列技术难点和挑战,开发一套不确定性能源环境系统优化方法体系,并以北京市和齐齐哈尔市为例,为其能源系统规划及节能减排等决策问题提供了科学依据。具体包括：(1)针对电力系统中电力供需矛盾问题,将极小极大遗憾规划方法与区间规划整合起来,建立不确定性条件下的电力系统规划模型,将电力供需风险定量化,有效权衡电力生产目标与实际需求间的矛盾,产出切实可行的电力规划方案。(2)为处理能源系统中存在的高度不确定性,引入射线区间理论,建立基于模糊射线区间规划方法的能源系统规划模型,该模型可扩宽决策空间,增强系统稳定性,产生更强健的系统结果,提高能源规划的科学性和可行性。(3)在对城市能源系统展开全面分析的基础上,开发不确定条件下的北京与齐齐哈尔市能源系统规划模型。在北京市能源模型中采用区间机会约束规划、模糊规划、违约风险分析、情景分析和能源替代效应,产出可再生能源利用和污染气体减排情景下的能源规划方案。针对齐齐哈尔能源系统的特征,建立基于区间规划、鲁棒优化与贝叶斯预测方法的齐齐哈尔能源模型,不仅提高了模型的稳定性与能源需求预测的准确性,而且可以定量分析经济增长对能源系统的影响,为齐齐哈尔的能源规划提供决策支持。
结果显示新开发的不确定性能源系统规划模型,在反映处理系统复杂性和不确定性特征上相较于过去的优化方法有很大创新,增强系统的稳定性和规划结果的可行性.此外,本研究还补充了完全适于我国国情的不确定性能源模型研究,为决策者提供科学有效的经济结构调整、能源优化配置、能源技术组合以及环境污染控制等方面的规划方案。
For many Chinese cities, the contradiction of energy supply and demand, unreasonable energy structure, energy misallocation, inadequate pollution reduction control, backward energy technology, and other obstacles in energy system management cause a series of energy, economic, environmental, and social problomes. According to related data, reports, and researches, in the future decades, with the rapid development of soc-economy, these issues will get further exacerbated, which will severely restrict the process of sustainable development in China. Therefore, it needs to conduct the comprehensive planning of energy and environmental system, which can provide scientific basis fbrdeveloping and utilizing energy resources, optimizing energy structure, improving environemtal quality, promoting energy technological innovation, and other decision problems.
However, energy and environmental planning systems consist of plenty of complexities. Under the interactions among energy reservesand time-space distribution, energy technology development, climate change, environmental capacity, economic and social planning, and international energy situation, throughthe processes of energy production, transmission and distribution, utilization, pollution reduction,andmulti-level spatio-temporal allocation, the fossil energy (e.g., coal, oil, natural gas, etc) and renewable energy (e.g., wind, solar, and nuclearpower, etc) can satisfy the terminal demands from industry, household, municipal sectors, commerce, and transportation, and finally the comprehensive benefits of energy, economy, environment, and societycan get maximized. The multi-attribute of internal system factors and their mult i-layer interactions present striking uncertain and dynamic features. Moreover, the limitations of human cognitive level lead to uncertainty and spatial and temporal heterogeneity in quantitative expression of system factors. And then, all these challenges complex the processes of energy policy analysis and strategy making. Conventional energy models often cannot actually reflect these complexities and uncertainties, and most maturity models lack the consideration of China's actual conditions and are difficult to apply.
Therefore, aiming atthe specific problems of energy and environmental system management in major Chinese cities, from the perspective of energy-environment-economic system, to solve the technical difficulties and challenges such as multiple uncertainty, complexity, and dynamic, this research would develop a set of inexact energy-environmental system optimization approaches, and then would be applied into the energy andenvironmental planning of Beijing and Qiqihar city, which could provide scientific basis for energy system planning, energy-saving and emission reduction. Specifically, this study can:(1) To address the contradiction of electricity supply and demand, based on the integration of minimax regret programming with interval programming methods, an inexact electric power andenvironmental system planning model would be developed to quantify the risk of electricity supply and demand, which could effectively balance the conflict between electricity pre-target and actual demand, and generate achievable electricity schemes.(2) In order to tackle the high degree of uncertainty in energy-environmental systems, a fuzzy radial interval programming method based energy and environmental system planning model would be developed by introducing radial interval theory, which could expand the decision space, enhance system reliability, generate more robust system solutions, and finally improve the scientificity and feasibility of planning processes.(3) Inexact Beijing and Qiqihar energy-environmental system planning models would be established based on comprehensive systemanalysis. Interval chance-constraint programming, fuzzy programming, constraint-violation analysis, scenario analysis, and energy-substitution effect methods would be integrated into Beijing energy-environmental model, which could analyze and compare energy activities schemes under various scenarios of pollution emissions reduction. According to the specific features of Qiqihar energy-environmental system, Qiqihar energy-environmental model would be developed based on Bayesian interval-parameter robust programming method, which could not only enhance the model reliability and the prediction accuracy of energy demand, but also quantitatively analyze the impact of economic development on energy-environmental system. The obtained solutions could provide decision support for the energy planning of Qiqihar city.
The generated results indicate that the developed energy-environmental system planning models under uncertainty have many advantages over the previous optimization approaches, which could improve systems reliability and solutions feasibility. Furthermore, this study complements the inexact energy and environmental model totally suitable for China's situation, which could provide decision makers with scientific and efficient planning schemes, including economic structure adjustment, optimal energy allocation, energy technology combination, and environmental pollution control.
然而,能源与环境系统规划面临诸多挑战。在能源储量及时空分布、能源技术发展、气候变化、环境容量、社会经济规划和国际能源格局等因素的交互作用下,将煤炭、石油和天然气等化石能源以及风能、太阳能和核能等可再生能源,通过能源生产、输配、利用和减排等多级时空调配过程,满足包括工业生产、居民生活、市政、商业、交通等在内的终端需求,从而实现能源、经济、环境和社会综合效益的最大化。其中,系统内部因子多重属性及其多层互动关系呈现出显著的不确定性和动态特征。此外,由于人类认知水平的局限性,致使因素的定量表征具有不确定性和时空异质性。进而,使得以实现能源、经济、环境和社会可持续发展为目标的能源政策分析和战略制定过程异常复杂。传统的能源模型大都缺乏对这些复杂性和不确定性的准确反映,并且国外成熟技术由于对我国具体国情考虑不足而难以适用。
因此,本研究以我国大中型城市能源与环境系统管理中的具体问题为目标,从能源、环境、经济和社会系统耦合分析的角度出发,针对多重不确定性、复杂性和动态性等一系列技术难点和挑战,开发一套不确定性能源环境系统优化方法体系,并以北京市和齐齐哈尔市为例,为其能源系统规划及节能减排等决策问题提供了科学依据。具体包括：(1)针对电力系统中电力供需矛盾问题,将极小极大遗憾规划方法与区间规划整合起来,建立不确定性条件下的电力系统规划模型,将电力供需风险定量化,有效权衡电力生产目标与实际需求间的矛盾,产出切实可行的电力规划方案。(2)为处理能源系统中存在的高度不确定性,引入射线区间理论,建立基于模糊射线区间规划方法的能源系统规划模型,该模型可扩宽决策空间,增强系统稳定性,产生更强健的系统结果,提高能源规划的科学性和可行性。(3)在对城市能源系统展开全面分析的基础上,开发不确定条件下的北京与齐齐哈尔市能源系统规划模型。在北京市能源模型中采用区间机会约束规划、模糊规划、违约风险分析、情景分析和能源替代效应,产出可再生能源利用和污染气体减排情景下的能源规划方案。针对齐齐哈尔能源系统的特征,建立基于区间规划、鲁棒优化与贝叶斯预测方法的齐齐哈尔能源模型,不仅提高了模型的稳定性与能源需求预测的准确性,而且可以定量分析经济增长对能源系统的影响,为齐齐哈尔的能源规划提供决策支持。
结果显示新开发的不确定性能源系统规划模型,在反映处理系统复杂性和不确定性特征上相较于过去的优化方法有很大创新,增强系统的稳定性和规划结果的可行性.此外,本研究还补充了完全适于我国国情的不确定性能源模型研究,为决策者提供科学有效的经济结构调整、能源优化配置、能源技术组合以及环境污染控制等方面的规划方案。
For many Chinese cities, the contradiction of energy supply and demand, unreasonable energy structure, energy misallocation, inadequate pollution reduction control, backward energy technology, and other obstacles in energy system management cause a series of energy, economic, environmental, and social problomes. According to related data, reports, and researches, in the future decades, with the rapid development of soc-economy, these issues will get further exacerbated, which will severely restrict the process of sustainable development in China. Therefore, it needs to conduct the comprehensive planning of energy and environmental system, which can provide scientific basis fbrdeveloping and utilizing energy resources, optimizing energy structure, improving environemtal quality, promoting energy technological innovation, and other decision problems.
However, energy and environmental planning systems consist of plenty of complexities. Under the interactions among energy reservesand time-space distribution, energy technology development, climate change, environmental capacity, economic and social planning, and international energy situation, throughthe processes of energy production, transmission and distribution, utilization, pollution reduction,andmulti-level spatio-temporal allocation, the fossil energy (e.g., coal, oil, natural gas, etc) and renewable energy (e.g., wind, solar, and nuclearpower, etc) can satisfy the terminal demands from industry, household, municipal sectors, commerce, and transportation, and finally the comprehensive benefits of energy, economy, environment, and societycan get maximized. The multi-attribute of internal system factors and their mult i-layer interactions present striking uncertain and dynamic features. Moreover, the limitations of human cognitive level lead to uncertainty and spatial and temporal heterogeneity in quantitative expression of system factors. And then, all these challenges complex the processes of energy policy analysis and strategy making. Conventional energy models often cannot actually reflect these complexities and uncertainties, and most maturity models lack the consideration of China's actual conditions and are difficult to apply.
Therefore, aiming atthe specific problems of energy and environmental system management in major Chinese cities, from the perspective of energy-environment-economic system, to solve the technical difficulties and challenges such as multiple uncertainty, complexity, and dynamic, this research would develop a set of inexact energy-environmental system optimization approaches, and then would be applied into the energy andenvironmental planning of Beijing and Qiqihar city, which could provide scientific basis for energy system planning, energy-saving and emission reduction. Specifically, this study can:(1) To address the contradiction of electricity supply and demand, based on the integration of minimax regret programming with interval programming methods, an inexact electric power andenvironmental system planning model would be developed to quantify the risk of electricity supply and demand, which could effectively balance the conflict between electricity pre-target and actual demand, and generate achievable electricity schemes.(2) In order to tackle the high degree of uncertainty in energy-environmental systems, a fuzzy radial interval programming method based energy and environmental system planning model would be developed by introducing radial interval theory, which could expand the decision space, enhance system reliability, generate more robust system solutions, and finally improve the scientificity and feasibility of planning processes.(3) Inexact Beijing and Qiqihar energy-environmental system planning models would be established based on comprehensive systemanalysis. Interval chance-constraint programming, fuzzy programming, constraint-violation analysis, scenario analysis, and energy-substitution effect methods would be integrated into Beijing energy-environmental model, which could analyze and compare energy activities schemes under various scenarios of pollution emissions reduction. According to the specific features of Qiqihar energy-environmental system, Qiqihar energy-environmental model would be developed based on Bayesian interval-parameter robust programming method, which could not only enhance the model reliability and the prediction accuracy of energy demand, but also quantitatively analyze the impact of economic development on energy-environmental system. The obtained solutions could provide decision support for the energy planning of Qiqihar city.
The generated results indicate that the developed energy-environmental system planning models under uncertainty have many advantages over the previous optimization approaches, which could improve systems reliability and solutions feasibility. Furthermore, this study complements the inexact energy and environmental model totally suitable for China's situation, which could provide decision makers with scientific and efficient planning schemes, including economic structure adjustment, optimal energy allocation, energy technology combination, and environmental pollution control.
引文
[1]陈长虹,李莉,黄成,等Leap模型在上海市能源消耗及大气污染物减排预测中的应用[J].能源技术,2005,26：16-21
[2]康旭东,穆海林,桂树森,等.亚洲地区能源-经济-环境系统模型研究[J].石油化工高等学校学报,2010,23(4)：94-97
[3]张方,董远,王志文.能源消费,经济增长和碳排放：中国面临的挑战[J].科技和产业,2012,12(5)：142-148
[4]唐志永,孙予罕,江绵恒.低碳复合能源系统——中国未来能源的解决方案和发展模式[J].中国科学：化学,2013,43(1)：116-124
[5]工毓,张瑜.浅谈中国城市能源发展现状及对策[J].世界华商经济年鉴(理论版),2012,7：70-71
[6]梁浩,龙惟定.城市能源系统综合规划模型的研究与应用[J].山东建筑大学学报,2010,25(5)：524-528
[7]龙惟定,白玮,梁浩,等.低碳城市的能源系统[J].暖通空调,2009,39(8)：79-84
[8]吉平,周孝信,宋云亭,等.区域可再生能源规划模型述评与展望[J].电网技术,2013,8：2071-2079
[9]Nissing C., von Blottnitz H. An economic model for energisation and its integration into the urban energy planning process [J]. Energy Policy,2010,38:2370-2378
[10]Akkemik K. A., Oguz F. Regulation, efficiency and equilibrium:a general equili-brium analysis of liberalization in the Turkish electricity market [J]. Energy,2011, 36:3282-3292
[11]Mahmood A., Marpaung C. O. P. Carbon pricing and energy efficiency improve-ment:why to miss the interaction for developing economies? An illustrative CGE based application to the Pakistan case [J]. Energy Policy,2014,67:87-10
[12]Kanudia A., Shukla P. R. Modelling of uncertainties and price elastic demands in energy-environment planning for India [J]. Omega,1998,26(3):409-423
[13]Seebregts A., Kram T., Schaeffer G. J., et al. Endogenous learning and technology clustering:analysis with MARKAL model of the western European energy system [J]. International Journal of Global Energy Issues,2000,14(1-4):289-319
[14]Schulz T. F., Kypreos S., Barreto L., et al. Intermediate steps towards the 2000 W society in Switzerland:an energy-economic scenario analysis [J]. Energy Policy, 2008,36(4):1303-1317
[15]Lehtila A., Pirila P. Reducing energy related emissions:using an energy systems optimization model to support policy planning in Finland [J]. Energy Policy,1996, 24:805-819
[16]Cormio C., Dicorato M., Minoia A., et al. A regional energy planning methodology including renewable energy sources and environmental constraints [J]. Renewable and Sustainable Energy Reviews,2003,7:99-130
[17]Dicorato M., Forte G., Trovato M. Environmental-constrained energy planning using energy-efficiency and distributed-generation facilities [J]. Renewable Energy, 2008,33(6):1297-1313
[18]Weber C., Shah N. Optimisation based design of a district energy system for an eco-town in the United Kingdom [J]. Energy,2011,36:1292-1308
[19]Kambo N. S., Handa B. R., Bose R. K. A linear goal programming model for urban energy-economy-environment interaction [J]. Energy and Buildings,1991,16: 537-551
[20]Groscurth H. M., Bruckner T., Kummel R. Modeling of energy-services supplysystems [J]. Energy,1995,20(9):941-958
[21]Huang Y., Bor Y., J., Peng C. Y. The long-term forecast of Taiwan's energy supply and demand:LEAP model application [J]. Energy Policy,2011,39(11):6790-6803
[22]Amirnekooei K., Ardehali M. M., Sadri A. Integrated resource planning for Iran: development of reference energy system, forecast, and long-term energy-environment plan [J]. Energy,2012,46:1374-1385
[23]Sundberg G., Karlsson B. G. Interaction effects in optimising a municipal energy system [J]. Energy,2000,25:877-891
[24]Pelet X., Favrat D., Leyland G. Multiobjective optimization of integrated energy systems for remote communities considering economics and CO2 emissions [J]. International Journal of Thermal Sciences,2005,44:1180-1189
[25]Soderman J., Pettersson F. Structural and operational optimisation of distributed energy systems [J]. Applied Thermal Engineering,2006,26:1400-1408
[26]Doukas H. C., Andreas B. M., Psarras J. E. Multi-criteria decision aid for the formulation of sustainable technological energy priorities using linguistic variables [J]. European Journal of Operational Research,2007,182(2):844-855
[27]Terrados J., Almonacid G., Hontoria L. Regional energy planning through SWOT analysis and strategic planning tools:impact on renewable development [J]. Renewable and Sustainable Energy Reviews,2007,11(6):1275-1287
[28]Ma T., Nakamori Y. Modeling technological change in energy systems:from opti-mization to agent-based modeling [J]. Energy,2009,34:873-879
[29]Cai R. X., Jin H. G., Gao L., et al. Development of multifunctional energy systems [J]. Energy,2010,35:4375-4382
[30]Barker T., Anger A., Dessens O., et al. Integrated modeling of climate control and air pollution:methodology and results from one-way coupling of an energy-environment-economy (E3MG) and atmospheric chemistry model (p-TOMCAT) in decar-burizing scenarios for Mexico to 2050 [J]. Environmental Science & Policy,2010,13(8):661-670
[31]Connolly D., Lund H., Mathiesen B. V., et al. Modelling the existing Irish energy-system to identify future energy costs and the maximum wind penetration feasible [J]. Energy,2010,35:2164-2173
[32]Rout U. K., Voβ A., Singh A., et al. Energy and emissions forecast of China over a long-time horizon [J]. Energy,2011,36(1):1-11
[33]Oliveira C., Antunes C. H. A multi-objective multi-sectoral economy-energy-envi-ronment model:Application to Portugal [J]. Energy,2011,36:2856-2866
[34]Gupta A., Saini R. P., Sharma M. P. Modeling of hybrid energy system:problem formulation and model development [J]. Renewable Energy,2011,36:459-465
[35]Gupta A., Saini R. P., Sharma M. P. Modelling of hybrid energy systemPart Ⅱ: combined dispatch strategies and solution algorithm [J]. Renewable Energy,2011, 36:466-473
[36]Catalina T., Virgone J., Blanco E. Multi-source energy systems analysis using a multi-criteria decision aid methodology [J]. Renewable Energy,2011,36: 2245-2252
[37]朱光远,胡广芝,张立存.县级能源目标规划模型[J].河南科学,1990,2：62-79
[38]许瑞林,黄福荃.江苏省能源模型研究[J].能源研究与利用,1992,3：3-10
[39]王林,江邵安.地区能源规划模型[J].内蒙古工业大学学报(自然科学版),1994,13(1)：8-17
[40]夏传文,刘亦文.CGE模型在节能政策领域的应用设想[J].湖南师范大学自然科学学报,2009,32(4)：119-123
[41]郭正权,刘海滨,牛东晓.基于CGE模型的我国碳税政策对能源与二氧化碳排放影响的模拟分析[J].煤炭工程,2012,1：138-140
[42]陈文颖,吴宗鑫.用MARKAL模型研究中国未来可持续能源发展战略[J].清华大学学报(自然科学版),2001,41(12)：103-106
[43]陈文颖,高鹏飞,何建坤.用MARKAL-MACRO模型研究碳减排对中国能源系统的影响[J].清华大学学报(自然科学版),2004,44(3)：342-346
[44]李华林,陈文颖,吴宗鑫,等.西部可持续能源开发利用模型[J].清华大学学报(自然科学版),2006,46(9)：1615-1619
[45]迟春洁,于渤,张弛.基于LEAP模型的中国未来能源发展前景研究[J].技术经济与管理研究,2004,11(5)：73-74
[46]高虎,张颖,王灿,等.基于LEAP的中国电力行业C02排放情景分析[J].清华大 学学报(自然科学版),2007,47(3)：365-368
[47]林晓梅.基于长期能源可替代规划系统(LEAP)模型的江苏省能源需求预测及温室气体减排研究[D].南京：南京大学,2009
[48]王彦超.基于LEAP模型的吉林省民用建筑能耗情景预测研究[D].吉林：吉林大学,2012
[49]黄东风.利用MESSAGE模型优化浙江电源结构初探[J].能源工程,2006,26(4)：6-9
[50]张阿玲,李继峰.构建中国的能源-经济-环境系统评价模型[J].2007,47(9)：1537-1540
[51]施应玲,刘媛媛.基于环境约束的电源结构优化研究[J].中国电力,2009,42(9)：16-19
[52]Kambo N. S., Handa B. R. A linear goal programming model for urban energy-eco-nomy-environment interaction [J]. Energy and Buildings,1991,16(1-2):537-551
[53]HosierR. H. Urban energy systems in Tanzania:a tale of three cities [J]. Energy Policy,1993,21(5):510-523
[54]Rolfsman B. Optimal supply and demand investments in municipal energy systems [J]. Energy Conversion andManagement,2004,45:595-611
[55]Sugihara H., Komoto J., Tsuji K. A multi-objective optimization model for deter-mining urban energy systems under integrated energy service in a specific area [J]. Electrical Engineering in Japan,2004,147(3):20-31
[56]Shrestha R. M., Malla S., Liyanage M. H. Scenario-based analyses of energy sys-tem development and its environmental implications in Thailand [J]. Energy Policy, 2007,35:3179-3193
[57]Zia H., Devadas V. Energy management in Lucknow city [J]. Energy Policy,2007, 35:4847-4868
[58]Phdungsilp A. Integrated energy and carbon modeling with a decision support sys-tem:policy scenarios for low-carbon city development in Bangkok [J]. Energy Policy,2010,38:4808-4817
[59]Zhou S. Y., Chen H., Li S. C. Resources use and greenhouse gas emissions in urban economy:ecological input-output modeling for Beijing 2002 [J]. Communications inNonlinear Science and Numerical Simulation,2010,15:3201-3231
[60]Liang S., Zhang T. Z. Managing urban energy system:a case of Suzhou in China [J]. Energy Policy,2011,39:2910-2918
[61]Liu D. C., Yang X. O., Tian X. C., et al. Study on integrated simulation model of economic, energy and environment safety system under the low-carbon policy in Beijing [J]. Procedia Environmental Sciences,2011,5:120-130
[62]Chingcuanco F., Miller E. J. A microsimulation model of urban energy use:mode- lling residential space heating demand in ILUTE [J]. Computers,Environment and Urban Systems,2012,36(2):186-194
[63]Xydis G. Development of an integrated methodology for the energy needs of a major urban city:the case study of Athens, Greece [J]. Renewable and Sustainable Energy Reviews,2012,16(9):6705-6716
[64]Kavgic M., Mumovic D., Summerfield A., et al. Uncertainty and modeling energy consumption:sensitivity analysis for a city-scale domestic energy model [J]. Energy and Buildings,2013,60:1-11
[65]Mourmouris J. C., Potolias C. A multi-criteria methodology for energy planning and developing renewable energy sources at a regional level:a case study in Thassos, Greece [J]. Energy Policy,2013,52:522-530
[66]Sampaio H. C., Dias R. A., Balestieri J. A. P. Sustainable urban energy planning: the case study of a tropical city [J]. Applied Energy,2013,104:924-935
[67]Yeo I. A., Yoon S. H, Yee J. J. Development of an environment and energy geographical information system (E-GIS) construction model to support environ-menttally friendly urban planning [J]. Applied Energy,2013,104:723-739
[68]刘豹,许树柏.天津地区能源模型体系[J].信息与控制,1981,6：33-39
[69]陈长虹,科林格林,吴昌华MARKAL模型在上海市能源结构调整与大气污染物排放中的应用[J].上海环境科学,2002,21(9)：515-519
[70]刘兆勇,吴祥生,刘安田MARKAL模型与建筑节能[J].重庆大学学报(自然科学版),2002,25(8)：111-113
[71]佟庆,白泉,刘滨,等.MARKAL模型在北京市中远期能源发展研究中的应用[J].中国能源,2004,26(6)：36-40
[72]余岳峰,胡建一,章树荣,等.上海能源系统MARKAL模型与情景分析[J].上海交通大学学报,2008,42(3)：360-364
[73]曹斌,林剑艺,崔胜辉,等.基于LEAP的厦门市节能与温室气体减排潜力情景分析[J].生态学报,2010,30(12)：3358-3367
[74]高新宇.北京市可再生能源综合规划模型与政策研究[D].北京市：北京市工业大学,2011
[75]冯悦怡,张力小.城市节能与碳减排政策情景分析——以北京市为例[J].资源科学,2012,34(3)：541-550
[76]李宁.北京市3E-S(能源-经济环境、安全)系统MARKAL模型研究开发[D].北京市：清华大学,2010
[77]贾彦鹏,刘仁志.基于TIMES模型的北京市节能减排分析[J].应用基础与工程科学学报,2013,21(5)：857-865
[78]Dhar S. B. Power system long-range decision analysis under fuzzy environment [J]. IEEE Transactions on Power Apparatus and Systems,1979,98(2):585-596
[79]Borges A. R., Antunes C. H. A fuzzy multiple objective decision support model for energy-economy planning [J]. European Journal Operational Research,2003,145: 304-316
[80]Sadeghi M., Hosseini H. M. Energy supply planning in Iran by using fuzzy linear programming approach (regarding uncertainties of investment costs) [J]. Energy Policy,2006,34:993-1003
[81]Fleten S. E., Kristoffersen T. K. Short-term hydropower production planning by stochastic programming [J]. Computers & Operations Research,2008,35: 2656-2671
[82]Kahraman C., Kaya I., Cebi S. A comparative analysis for multiattribute selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy analytic hierarchy process [J]. Energy,2009,34:1603-1616
[83]Jinturkar A. M., Deshmukh S. S. A fuzzy mixed integer goal programming app-roach for cooking and heating energy planning in rural India [J]. Expert Systems with Applications,2011,38:11377-11381
[84]Kaya T., Kahraman C. Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology [J]. Expert Systems with Applications,2011, 38:6577-6585
[85]Michalik G., Khan M. E., Bonwick W. J., et al. Structural modeling of energy demand in the residential sector:the use of linguistic variables to include uncertain-ty of customers'behavior [J]. Energy,1997,22(10):949-958
[86]Zangeneh A., Jadid S., Rahimi-Kian A. A fuzzy environmental-technical-economic model for distributed generation planning [J]. Energy,2011,36(5):3437-3445
[87]Weber K., Martinsen D. From system cost minimization to sustainability maximi-zation:a new fuzzy program approach to energy systems analysis [J]. Fuzzy Sets and Systems,2013,231:1-25
[88]Kabak M., Kose E., Kirilmaz O., et al. A fuzzy multi-criteria decision making app-roach to assess building energy performance [J]. Energy and Buildings,2014,72: 382-389
[89]Zhang L., Zhou D. Q., Zhou P., et al. Modelling policy decision of sustainable ener-gy strategies for Nanjing city:a fuzzy integral approach [J]. Renewable Energy, 2014,62:197-203
[90]王化兰,任艳.基于改进型遗传算法的能源模糊优化的研究[J].计算机工程与设计,2007,28(15)：3652-3655
[91]虞新兴.基于多Agent的模糊决策方法在能源系统中应用研究[D].上海：华东理工大学,2011
[92]陈艳菊.2-型模糊环境下的能源系统优化[J].模糊系统与数学,2012,26(1)： 107-114
[93]谭忠富,何楠,周风翱,等.基于三角模糊数的清洁能源电价补偿机制[J].水电能源科学,2013,31(3)：251-255
[94]许野.不确定条件下的区域能源规划模型[J].冶金能源,2013,32(4)：3-6
[95]李群,张刘冬,殷明慧,等.基于模糊决策方法的含风电场电力系统机组组合[J].电网技术,2013,37(9)：2480-2485
[96]Teghem J., Dufrane D., Thauvoye M. An interactive method for multi-objective linear programming under uncertainty [J]. European Journal of Operational Resear-ch,1986,26(1):65-82
[97]Diwekar U. M., Rubin E. S., Frey H. C. Optimal design of advanced power systems under uncertainty [J]. Energy conversion and management,1997, 38(15-17):1725-1735
[98]KanudiaA.,LoulouR.Robustresponsestoclimatechangeviastochastic MARKAL:the case of Quebec [J]. European Journal of Operational Research,1998,106:15-30
[99]Krukanont P., Tezuka T. Implications of capacity expansion under uncertainty and value of information:the near-term energy planning of Japan [J]. Energy,2007, 32:1809-1824
[100]Rong A., Lahdelma R. CO2 emissions trading planning in combined heat and power production via multi-period stochastic optimization [J]. European Journal of Opera-tional Research,2007,176:1874-1895
[101]Fuss S., Szolgayova J. Fuel price and technological uncertainty in a real options model for electricity planning [J]. Applied Energy,2010,7:2938-2944
[102]Mavrotas G., Florios K., Vlachou D. Energy planning of a hospital using mathe-matical programming and Monte Carlo simulation for dealing with uncertainty in the economic parameters [J]. Energy conversion and management,2010,51: 722-731
[103]Carpaneto E., Chicco G., Mancarella P., et al. Cogeneration planning under uncer-tainty Part I:Multiple time frame approach [J]. Applied Energy,2011,88: 1059-1067
[104]Chen C. W., Fan Y. Y. Bioethanol supply chain system planning under supply and demand uncertainties [J]. Transportation Research Part E:Logistics and Transpor-tation Review,2012,48:150-164
[105]Choi G. B., Lee S. G., Lee J. M. Modeling and stochastic dynamic optimization for optimal energy resource allocation [J]. Computer Aided Chemical Engineering, 2012,31:765-769
[106]Tian G. D., Liu Y. M., Ke H., et al. Energy evaluation method and its optimization models for process planning with stochastic characteristics:a case study in dis-assembly decision-making [J]. Computers & Industrial Engineering,2012,63: 553-563
[107]杨宁,文福栓.基于机会约束规划的输电系统规划方法[J].电力系统自动化,2004,28(14)：23-27
[108]臧宝锋,胡汉辉,王建兴.电力市场条件下的发电投资均[J].电力系统自动化,2006,30(2)：41-44
[109]万中,阳彩霞,郝爱云.地市级可再生资源能源利用优化[J].经济数学,2009,26(2)：45-51
[110]彭翔宇.能源冲击与中国经济波动——基于动态随机一般均衡模型的分析金融学[D].北京市：对外经济贸易大学,2011
[111]黄建.基于LEAP的中国电力需求情景及其不确定性分析[J].资源科学,2012,34(11)：2124-2132
[112]魏巍贤,高中元,彭翔宇.能源冲击与中国经济波动——基于动态随机一般均衡模型的分析,2012,1(379)：51-64
[113]宋杰鲲,陆悠悠,李婷婷,等.山东省能源经济环境系统目标规划模型[J].干旱区资源与环境,2013,27(8)：21-25
[114]Lin Q. G., Huang G. H. IPEM:an interval-parameter energy systems planning mo-del [J]. Energy Sources,2008,30:1382-1399
[115]LinQ. G., Huang G. H. Planning of energy system management and GHG-emission control in the Municipality of Beijing:an inexact-dynamic stochastic programming model [J]. Energy Policy,2009,37:4463-4473
[116]Cai Y. P., Huang G. H., Yang Z. F., et al. Community-scale renewable energy sys-tems planning under uncertainty:an interval chance-constrained programming app-roach [J]. Renewable and Sustainable Energy Reviews,2009,13(4):721-735
[117]Cai Y. P., Huang G. H., Tan Q., et al. An integrated approach for climate-change impact analysis and adaptation planning under multi-level uncertainties. Part I:Me-thodology [J]. Renewable and Sustainable Energy Reviews,2011,15:2779-2790
[118]Cai Y. P., Huang G. H., Tan Q., et al. An integrated approach for climate-change impact analysis and adaptation planning under multi-level uncertainties. Part Ⅱ. Ca-se study [J]. Renewable and Sustainable Energy Reviews,2011,15(6):3051-3073
[119]Liu Y., Huang G. H., Cai Y. P., et al. Development of an inexact optimization mo-del for coupled coal and power management in North China [J]. Energy Policy 2009,37(11):4345-4363
[120]Chen W. T., Li Y.P., Huang G. H., et al. A two-stage inexact-stochastic program-ming model for planning carbon dioxide emission trading under uncertainty [J]. Applied Energy,2010,87(3):1033-1047
[121]Hu Q., Huang G. H., Cai Y. P., et al. Feasibility-based inexact fuzzy programming for electric power generation systems planning under dual. Applied Energy,2011, 88(12),4642-4654
[122]Li G.C., Huang G.H., Sun W., Ding X.W. An inexact optimization model for en-ergy-environment systems management in the mixed fuzzy, dual-interval and sto-chastic environment. Renewable Energy,2014,64,153-163
[123]Zhu H., Huang W. W., Huang G. H. Planning of regional energy systems:an in-exact mixed-integer fractional programming model [J]. Applied Energy,2014,13: 500-514
[124]王守相,王成山,刘若沁.基于模糊区间算法的配电网潮流计算[J].电力系统自动化,2000,2：19-22
[125]张晓萱,黄国和,席北斗,等.不确定性的电厂动力配煤优化模型[J].中国电机工程学报,2008,36(6)：73-76
[126]李彩,黄国和,蔡宴朋.能源供需优化模型在北京市能源系统的应用[J].能源工程,2009,4：1-5
[127]胡情,黄国和,李薇.基于清洁能源可获得量违约分析的电力规划[J].中国电力,2011,46(6)：103-107
[128]庞天一,孙钊,温静雅,等.规模化养殖场沼气工程替代传统模式发电的GHG减排不确定性优化模型[J].吉林大学学报(理学版),2013,51(1)：153-157
[129]吴健中,杨培庆.能源模型的多目标规划问题[J].上海交通大学学报,1982,2：115-124
[130]徐燕椿.能源模型的方法和应用[J].能源工程,1982,2：49-51
[131]蒋金荷,姚愉芳.气候变化政策研究中经济——能源系统模型的构建[J].数量经济技术经济研究,2002,7：41-45
[132]吴永平,宋华岭,李金克.煤炭资源供应链国际化系统的复杂性研究[J].中国矿业,2007,16(6)：33-36
[133]孙岩.能源复杂系统多Agent建模方法研究[D].上海：华东理工大学,2011
[134]王亚男.能源型城市的环境问题分析[J].科技信息,2013,7：454-454
[135]王晓明.能源系统的复杂性分析及其应用研究[D].天津：天津大学,2002
[136]Huang G. H., Baetz B. W., Patry G. G. An interval linear programming approach for municipal solid waste management planning under uncertainty [J]. Civil Engi-neering Systems,1992,9:319-335
[137]尚金成,张立庆.电力节能减排与资源优化配置技术的研究与应用[J].电网技术,2007,31(22)：58-63
[138]杨燕.全国电力利用效率比较及其影响因素分析[D].北京市：华北电力大学,2008
[139]Loulou R., Kanudia A. Minimax regret strategies for greenhouse gas abatement: methodology and application [J]. Operations Research Letters,1999,25:219-230
[140]Muela E., Schweickardt G., Garces F. Fuzzy possibilistic model for medium-term power generation planning with environmental criteria [J]. Energy Policy,2007,35: 5643-5655
[141]Rachmatullah C, Aye L, Fuller RJ. Scenario planning for the electricity generation in Indonesia [J]. Energy Policy,2007,35:2352-2359
[142]Tummala R. V. M. Decision analysis with business applications [M]. New York and London:Intext Educational Publishers,1973
[143]Quaddusa M. A., Goh T. N. Electric power generation expansion:planning with multiple objectives [J]. Applied Energy,1985,19(4):301-319
[144]Li Y. P., Huang G. H. Minimax regret analysis for municipal solid wastemanage-ment:an interval-stochastic programming approach [J]. Journal of the Air & Waste Management Association,2006,56:931-944
[145]Li Y. P., Huang G. H., Nie S. L. A robust interval-based minimax-regret analysis approach for the identification of optimal water-resources-allocation strategies under uncertainty[J]. Resources, Conservationand Recycling,2009,54:86-96
[146]Huang G. H., Baetz B. W., Patry G. G. A grey fuzzy linear programming approach for municipal solid waste management planning under uncertainty [J]. Civil Engi-neering Systems,1993,10:123-146
[147]Huang G. H., Baetz B. W., Patry G. G. Grey fuzzy integer programming:an appli-cation to regional waste management planning under uncertainty. Socio-Economic Planning Sciences,1995,29:17-38
[148]Ben-Tai A., Nemirovski A. Robust solutions of linear programming problems con-taminated with uncertain data [J]. Mathematical Programming,2000,88:411-424
[149]Bertsimas D., Sim M. Robust discrete optimization and network flows [J]. Mathe-matical Programming,2003,98:49-71
[150]Bertsimas D., Sim M. The price of robustness [J]. Operations Research,2004,52: 35-53
[151]Bertsimas D., Thiele A. A robust optimization approach to inventory theory [J]. Operations Research,2006,54 (1):150-168
[152]Tan Q., Huang G. H., Cai Y. P. Radial-interval linear programming for environ-mental management under varied protection levels [J]. Journal of the Air & Waste Management Association,2010,60:1078-1093
[153]Tan Q., Huang G. H., Cai Y. P. Waste management with recourse:an inexact dy-namic programming model containing fuzzy boundary intervals in objectives and constraints [J]. Journal of Environmental Management,2010,91:1898-1913
[154]Tan Q., Huang G. H., Cai Y. P. Radial interval chance-constrained programming for agricultural non-point source water pollution control under uncertainty [J]. Agricultural Water Management,2011,98:1595-1606
[155]Labriet M., Kanudia A., Loulou R. Climate mitigation under an uncertain techno-logy future:a TIAM-World analysis [J]. Energy Economics,2012,34(S3): S366-S377
[156]Esmaili M., Amjady N., Shayanfar H. A. Stochastic congestion management in power markets using efficient scenario approaches [J]. Energy Conversation and Management,2010,51(11):2285-2293
[157]马卫华,胡彦霞.北京市能源发展研究的战略思考[J].华北电力大学学报(社会科学版),2007,(1)：43-47
[158]程伟良,王凯,李柏杰.北京市能源分布状况及转化利用分析研究[J].中国电力教育,2012,(z1)：1-2
[159]王朝华.节能优先应该成为“十二五”时期北京市能源发展的基础[J].中国集体经济,2011,(33)：36-37
[160]国家发展与改革委员会.北京市“十一五”规划
[161]国家发展与改革委员会.北京市“十二五”规划
[162]北京市统计年鉴.北京市统计局(1978-2012)
[163]国务院常务会议.城市总体规划(2004-2020)
[164]国家发展与改革委员会.北京市轨道交通建设规划(2004-2015)
[165]Zhu S. L., Jiang K. J. Analyses of technical options for mitigating CO2 emissions from an urban transport system:a case study of Beijing City [J]. International Journal of Environment and Pollution,2003,19(5):483-497
[166]Cai Y. P., Huang G. H., Yang Z. F., et al. Identification of optimal strategies for energy management systems planning under multiple uncertainties [J]. Applied Energy,2009,86:480-495
[167]Burn D. H., Lence B. J. Comparison of optimization formulations for waste-load allocations. Journalof Environmental Engineering,1992,118:597-612
[168]Ellis J. H. Acid rain control strategies:options exist despite scientific uncertainty. Environmental Science & Technology,1994,22:1248-1255
[169]Ellis J. H., Bowman M. L. Critical loads and development of acid rain control options. Journal of Environmental Engineering,1994,120:273-289
[170]Huang Y. F., Baetz B. W., Huang G. H., et al. Violation analysis for solid waste management systems:an interval fuzzy programming approach. Journal of Envi-ronmental Management,2002,65:431-446
[171]Tan, Q., Huang, G. H., Wu, C. Z., et al. Development of an inexact fuzzy robust programming model for integrated evacuation management under uncertainty. Journal of Urban Planning and Development,2009,135(1):39-49
[172]Tan, Q., Huang, G. H., Wu, C. Z., et al. IF-EM:an interval-parameter fuzzy linear programming model for environment-oriented evacuation planning under uncertainty. Journal of Advanced Transportation,2011,45(4):286-303
[173]Uri N. D. Energy demand and interfuel substitution in India. European Economic Review,1979,12(2):181-190
[174]Kemfert C, Welsch H. Energy-Capital-Labor Substitution and the Economic Ef-fects of CO2 Abatement:Evidence for Germany. Journal of Policy Modeling,2000, 22(6):641-660
[175]Ahn H., Gumus M., Kaminsky P. Pricing and manufacturing decisions when de-mand is a function of prices in multiple periods. Operations Research,2007,55(6): 1039-1057
[176]Ma H., Oxley L., Gibson J. Substitution possibilities and determinants of energy intensity for China. Energy Policy,2009,37(5):1793-1804
[177]Lin X, Janak S. L, Floudas C.A. A New robust optimization approach for schedu-ling under uncertainty:I. Bounded uncertainty. Computers & Chemical Engineering, 2004,28:1069-1085
[178]Janak S. L, Lin X. X, Floudas C. A. A new robust optimization approach for sche-duling under uncertainty II. Uncertainty with known probability distribution. Com-puters & Chemical Engineering,2007,31:171-195
[179]Pearl J. Probabilistic reasoning in intelligent systems:networks of plausible infe-rence. San Francisco:Morgan Kaufmann Publishers Inc.,1988.
[180]网慧敏.基于先验信息的统计预测方法及其应用研究[D].北京市：华北电力大学,2011
[181]许昌林,魏立力.一元线性回归模型的贝叶斯分析[J].重庆工学院学报(自然科学),2009,23(3)：98-100
[182]齐齐哈尔市统计局.2011年齐齐哈尔市国民经济和社会发展统计公报
[183]黑龙江省统计局.黑龙江统计年鉴(1990-2012)
[184]齐齐哈尔市统计局.齐齐哈尔经济统计年鉴(1989-2012)
[185]“十二五”时期齐齐哈尔结构调整和转型的思考[EB/OL].www.chinaneast.gov.cn
[186]我市“十二五”节能降耗取得重大进展[EB/OL].http://www.qqhrstats.gov.cn
[187]黑龙江省统计局齐齐哈尔市节能降耗成绩显著,存在问题须高度重视[EB/OL].http://www.hlj.stats.gov.cn/jjfx/dsfx/14604.htm
[188]齐齐哈尔市国民经济和社会发展第十二个五年规划纲要[EB/OL].http://www.qqhr.gov.cn
[189]董丽娟.齐齐哈尔市发展CNG汽车综合效益分析及推广[J].黑龙江科技信息,2009,22：41-41
[190]齐齐哈尔市人民政府.齐齐哈尔市城市总体规划(2010-2020)
[191]齐齐哈尔市2012年政府工作报告[EB/OL].http://www.qqhr.gov.cn
[192]黑龙江省人民政府.《黑龙江省新能源和可再生能源产业发展规划(2010-2020年)》
[193]田国光,甄微.黑龙江省齐齐哈尔市地热资源浅析[J].黑龙江科技信息,2011,25：32-32
[194]齐齐哈尔市生物发电：一个项目多方受益[EB/OL]. http://qiqihar.dbw.cn
[195]齐齐哈尔市2011年国民经济和社会发展计划执行情况与2012年国民经济和社会发展计划(草案)[EB/OL].http://www.qqhr.gov.cn
[2]康旭东,穆海林,桂树森,等.亚洲地区能源-经济-环境系统模型研究[J].石油化工高等学校学报,2010,23(4)：94-97
[3]张方,董远,王志文.能源消费,经济增长和碳排放：中国面临的挑战[J].科技和产业,2012,12(5)：142-148
[4]唐志永,孙予罕,江绵恒.低碳复合能源系统——中国未来能源的解决方案和发展模式[J].中国科学：化学,2013,43(1)：116-124
[5]工毓,张瑜.浅谈中国城市能源发展现状及对策[J].世界华商经济年鉴(理论版),2012,7：70-71
[6]梁浩,龙惟定.城市能源系统综合规划模型的研究与应用[J].山东建筑大学学报,2010,25(5)：524-528
[7]龙惟定,白玮,梁浩,等.低碳城市的能源系统[J].暖通空调,2009,39(8)：79-84
[8]吉平,周孝信,宋云亭,等.区域可再生能源规划模型述评与展望[J].电网技术,2013,8：2071-2079
[9]Nissing C., von Blottnitz H. An economic model for energisation and its integration into the urban energy planning process [J]. Energy Policy,2010,38:2370-2378
[10]Akkemik K. A., Oguz F. Regulation, efficiency and equilibrium:a general equili-brium analysis of liberalization in the Turkish electricity market [J]. Energy,2011, 36:3282-3292
[11]Mahmood A., Marpaung C. O. P. Carbon pricing and energy efficiency improve-ment:why to miss the interaction for developing economies? An illustrative CGE based application to the Pakistan case [J]. Energy Policy,2014,67:87-10
[12]Kanudia A., Shukla P. R. Modelling of uncertainties and price elastic demands in energy-environment planning for India [J]. Omega,1998,26(3):409-423
[13]Seebregts A., Kram T., Schaeffer G. J., et al. Endogenous learning and technology clustering:analysis with MARKAL model of the western European energy system [J]. International Journal of Global Energy Issues,2000,14(1-4):289-319
[14]Schulz T. F., Kypreos S., Barreto L., et al. Intermediate steps towards the 2000 W society in Switzerland:an energy-economic scenario analysis [J]. Energy Policy, 2008,36(4):1303-1317
[15]Lehtila A., Pirila P. Reducing energy related emissions:using an energy systems optimization model to support policy planning in Finland [J]. Energy Policy,1996, 24:805-819
[16]Cormio C., Dicorato M., Minoia A., et al. A regional energy planning methodology including renewable energy sources and environmental constraints [J]. Renewable and Sustainable Energy Reviews,2003,7:99-130
[17]Dicorato M., Forte G., Trovato M. Environmental-constrained energy planning using energy-efficiency and distributed-generation facilities [J]. Renewable Energy, 2008,33(6):1297-1313
[18]Weber C., Shah N. Optimisation based design of a district energy system for an eco-town in the United Kingdom [J]. Energy,2011,36:1292-1308
[19]Kambo N. S., Handa B. R., Bose R. K. A linear goal programming model for urban energy-economy-environment interaction [J]. Energy and Buildings,1991,16: 537-551
[20]Groscurth H. M., Bruckner T., Kummel R. Modeling of energy-services supplysystems [J]. Energy,1995,20(9):941-958
[21]Huang Y., Bor Y., J., Peng C. Y. The long-term forecast of Taiwan's energy supply and demand:LEAP model application [J]. Energy Policy,2011,39(11):6790-6803
[22]Amirnekooei K., Ardehali M. M., Sadri A. Integrated resource planning for Iran: development of reference energy system, forecast, and long-term energy-environment plan [J]. Energy,2012,46:1374-1385
[23]Sundberg G., Karlsson B. G. Interaction effects in optimising a municipal energy system [J]. Energy,2000,25:877-891
[24]Pelet X., Favrat D., Leyland G. Multiobjective optimization of integrated energy systems for remote communities considering economics and CO2 emissions [J]. International Journal of Thermal Sciences,2005,44:1180-1189
[25]Soderman J., Pettersson F. Structural and operational optimisation of distributed energy systems [J]. Applied Thermal Engineering,2006,26:1400-1408
[26]Doukas H. C., Andreas B. M., Psarras J. E. Multi-criteria decision aid for the formulation of sustainable technological energy priorities using linguistic variables [J]. European Journal of Operational Research,2007,182(2):844-855
[27]Terrados J., Almonacid G., Hontoria L. Regional energy planning through SWOT analysis and strategic planning tools:impact on renewable development [J]. Renewable and Sustainable Energy Reviews,2007,11(6):1275-1287
[28]Ma T., Nakamori Y. Modeling technological change in energy systems:from opti-mization to agent-based modeling [J]. Energy,2009,34:873-879
[29]Cai R. X., Jin H. G., Gao L., et al. Development of multifunctional energy systems [J]. Energy,2010,35:4375-4382
[30]Barker T., Anger A., Dessens O., et al. Integrated modeling of climate control and air pollution:methodology and results from one-way coupling of an energy-environment-economy (E3MG) and atmospheric chemistry model (p-TOMCAT) in decar-burizing scenarios for Mexico to 2050 [J]. Environmental Science & Policy,2010,13(8):661-670
[31]Connolly D., Lund H., Mathiesen B. V., et al. Modelling the existing Irish energy-system to identify future energy costs and the maximum wind penetration feasible [J]. Energy,2010,35:2164-2173
[32]Rout U. K., Voβ A., Singh A., et al. Energy and emissions forecast of China over a long-time horizon [J]. Energy,2011,36(1):1-11
[33]Oliveira C., Antunes C. H. A multi-objective multi-sectoral economy-energy-envi-ronment model:Application to Portugal [J]. Energy,2011,36:2856-2866
[34]Gupta A., Saini R. P., Sharma M. P. Modeling of hybrid energy system:problem formulation and model development [J]. Renewable Energy,2011,36:459-465
[35]Gupta A., Saini R. P., Sharma M. P. Modelling of hybrid energy systemPart Ⅱ: combined dispatch strategies and solution algorithm [J]. Renewable Energy,2011, 36:466-473
[36]Catalina T., Virgone J., Blanco E. Multi-source energy systems analysis using a multi-criteria decision aid methodology [J]. Renewable Energy,2011,36: 2245-2252
[37]朱光远,胡广芝,张立存.县级能源目标规划模型[J].河南科学,1990,2：62-79
[38]许瑞林,黄福荃.江苏省能源模型研究[J].能源研究与利用,1992,3：3-10
[39]王林,江邵安.地区能源规划模型[J].内蒙古工业大学学报(自然科学版),1994,13(1)：8-17
[40]夏传文,刘亦文.CGE模型在节能政策领域的应用设想[J].湖南师范大学自然科学学报,2009,32(4)：119-123
[41]郭正权,刘海滨,牛东晓.基于CGE模型的我国碳税政策对能源与二氧化碳排放影响的模拟分析[J].煤炭工程,2012,1：138-140
[42]陈文颖,吴宗鑫.用MARKAL模型研究中国未来可持续能源发展战略[J].清华大学学报(自然科学版),2001,41(12)：103-106
[43]陈文颖,高鹏飞,何建坤.用MARKAL-MACRO模型研究碳减排对中国能源系统的影响[J].清华大学学报(自然科学版),2004,44(3)：342-346
[44]李华林,陈文颖,吴宗鑫,等.西部可持续能源开发利用模型[J].清华大学学报(自然科学版),2006,46(9)：1615-1619
[45]迟春洁,于渤,张弛.基于LEAP模型的中国未来能源发展前景研究[J].技术经济与管理研究,2004,11(5)：73-74
[46]高虎,张颖,王灿,等.基于LEAP的中国电力行业C02排放情景分析[J].清华大 学学报(自然科学版),2007,47(3)：365-368
[47]林晓梅.基于长期能源可替代规划系统(LEAP)模型的江苏省能源需求预测及温室气体减排研究[D].南京：南京大学,2009
[48]王彦超.基于LEAP模型的吉林省民用建筑能耗情景预测研究[D].吉林：吉林大学,2012
[49]黄东风.利用MESSAGE模型优化浙江电源结构初探[J].能源工程,2006,26(4)：6-9
[50]张阿玲,李继峰.构建中国的能源-经济-环境系统评价模型[J].2007,47(9)：1537-1540
[51]施应玲,刘媛媛.基于环境约束的电源结构优化研究[J].中国电力,2009,42(9)：16-19
[52]Kambo N. S., Handa B. R. A linear goal programming model for urban energy-eco-nomy-environment interaction [J]. Energy and Buildings,1991,16(1-2):537-551
[53]HosierR. H. Urban energy systems in Tanzania:a tale of three cities [J]. Energy Policy,1993,21(5):510-523
[54]Rolfsman B. Optimal supply and demand investments in municipal energy systems [J]. Energy Conversion andManagement,2004,45:595-611
[55]Sugihara H., Komoto J., Tsuji K. A multi-objective optimization model for deter-mining urban energy systems under integrated energy service in a specific area [J]. Electrical Engineering in Japan,2004,147(3):20-31
[56]Shrestha R. M., Malla S., Liyanage M. H. Scenario-based analyses of energy sys-tem development and its environmental implications in Thailand [J]. Energy Policy, 2007,35:3179-3193
[57]Zia H., Devadas V. Energy management in Lucknow city [J]. Energy Policy,2007, 35:4847-4868
[58]Phdungsilp A. Integrated energy and carbon modeling with a decision support sys-tem:policy scenarios for low-carbon city development in Bangkok [J]. Energy Policy,2010,38:4808-4817
[59]Zhou S. Y., Chen H., Li S. C. Resources use and greenhouse gas emissions in urban economy:ecological input-output modeling for Beijing 2002 [J]. Communications inNonlinear Science and Numerical Simulation,2010,15:3201-3231
[60]Liang S., Zhang T. Z. Managing urban energy system:a case of Suzhou in China [J]. Energy Policy,2011,39:2910-2918
[61]Liu D. C., Yang X. O., Tian X. C., et al. Study on integrated simulation model of economic, energy and environment safety system under the low-carbon policy in Beijing [J]. Procedia Environmental Sciences,2011,5:120-130
[62]Chingcuanco F., Miller E. J. A microsimulation model of urban energy use:mode- lling residential space heating demand in ILUTE [J]. Computers,Environment and Urban Systems,2012,36(2):186-194
[63]Xydis G. Development of an integrated methodology for the energy needs of a major urban city:the case study of Athens, Greece [J]. Renewable and Sustainable Energy Reviews,2012,16(9):6705-6716
[64]Kavgic M., Mumovic D., Summerfield A., et al. Uncertainty and modeling energy consumption:sensitivity analysis for a city-scale domestic energy model [J]. Energy and Buildings,2013,60:1-11
[65]Mourmouris J. C., Potolias C. A multi-criteria methodology for energy planning and developing renewable energy sources at a regional level:a case study in Thassos, Greece [J]. Energy Policy,2013,52:522-530
[66]Sampaio H. C., Dias R. A., Balestieri J. A. P. Sustainable urban energy planning: the case study of a tropical city [J]. Applied Energy,2013,104:924-935
[67]Yeo I. A., Yoon S. H, Yee J. J. Development of an environment and energy geographical information system (E-GIS) construction model to support environ-menttally friendly urban planning [J]. Applied Energy,2013,104:723-739
[68]刘豹,许树柏.天津地区能源模型体系[J].信息与控制,1981,6：33-39
[69]陈长虹,科林格林,吴昌华MARKAL模型在上海市能源结构调整与大气污染物排放中的应用[J].上海环境科学,2002,21(9)：515-519
[70]刘兆勇,吴祥生,刘安田MARKAL模型与建筑节能[J].重庆大学学报(自然科学版),2002,25(8)：111-113
[71]佟庆,白泉,刘滨,等.MARKAL模型在北京市中远期能源发展研究中的应用[J].中国能源,2004,26(6)：36-40
[72]余岳峰,胡建一,章树荣,等.上海能源系统MARKAL模型与情景分析[J].上海交通大学学报,2008,42(3)：360-364
[73]曹斌,林剑艺,崔胜辉,等.基于LEAP的厦门市节能与温室气体减排潜力情景分析[J].生态学报,2010,30(12)：3358-3367
[74]高新宇.北京市可再生能源综合规划模型与政策研究[D].北京市：北京市工业大学,2011
[75]冯悦怡,张力小.城市节能与碳减排政策情景分析——以北京市为例[J].资源科学,2012,34(3)：541-550
[76]李宁.北京市3E-S(能源-经济环境、安全)系统MARKAL模型研究开发[D].北京市：清华大学,2010
[77]贾彦鹏,刘仁志.基于TIMES模型的北京市节能减排分析[J].应用基础与工程科学学报,2013,21(5)：857-865
[78]Dhar S. B. Power system long-range decision analysis under fuzzy environment [J]. IEEE Transactions on Power Apparatus and Systems,1979,98(2):585-596
[79]Borges A. R., Antunes C. H. A fuzzy multiple objective decision support model for energy-economy planning [J]. European Journal Operational Research,2003,145: 304-316
[80]Sadeghi M., Hosseini H. M. Energy supply planning in Iran by using fuzzy linear programming approach (regarding uncertainties of investment costs) [J]. Energy Policy,2006,34:993-1003
[81]Fleten S. E., Kristoffersen T. K. Short-term hydropower production planning by stochastic programming [J]. Computers & Operations Research,2008,35: 2656-2671
[82]Kahraman C., Kaya I., Cebi S. A comparative analysis for multiattribute selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy analytic hierarchy process [J]. Energy,2009,34:1603-1616
[83]Jinturkar A. M., Deshmukh S. S. A fuzzy mixed integer goal programming app-roach for cooking and heating energy planning in rural India [J]. Expert Systems with Applications,2011,38:11377-11381
[84]Kaya T., Kahraman C. Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology [J]. Expert Systems with Applications,2011, 38:6577-6585
[85]Michalik G., Khan M. E., Bonwick W. J., et al. Structural modeling of energy demand in the residential sector:the use of linguistic variables to include uncertain-ty of customers'behavior [J]. Energy,1997,22(10):949-958
[86]Zangeneh A., Jadid S., Rahimi-Kian A. A fuzzy environmental-technical-economic model for distributed generation planning [J]. Energy,2011,36(5):3437-3445
[87]Weber K., Martinsen D. From system cost minimization to sustainability maximi-zation:a new fuzzy program approach to energy systems analysis [J]. Fuzzy Sets and Systems,2013,231:1-25
[88]Kabak M., Kose E., Kirilmaz O., et al. A fuzzy multi-criteria decision making app-roach to assess building energy performance [J]. Energy and Buildings,2014,72: 382-389
[89]Zhang L., Zhou D. Q., Zhou P., et al. Modelling policy decision of sustainable ener-gy strategies for Nanjing city:a fuzzy integral approach [J]. Renewable Energy, 2014,62:197-203
[90]王化兰,任艳.基于改进型遗传算法的能源模糊优化的研究[J].计算机工程与设计,2007,28(15)：3652-3655
[91]虞新兴.基于多Agent的模糊决策方法在能源系统中应用研究[D].上海：华东理工大学,2011
[92]陈艳菊.2-型模糊环境下的能源系统优化[J].模糊系统与数学,2012,26(1)： 107-114
[93]谭忠富,何楠,周风翱,等.基于三角模糊数的清洁能源电价补偿机制[J].水电能源科学,2013,31(3)：251-255
[94]许野.不确定条件下的区域能源规划模型[J].冶金能源,2013,32(4)：3-6
[95]李群,张刘冬,殷明慧,等.基于模糊决策方法的含风电场电力系统机组组合[J].电网技术,2013,37(9)：2480-2485
[96]Teghem J., Dufrane D., Thauvoye M. An interactive method for multi-objective linear programming under uncertainty [J]. European Journal of Operational Resear-ch,1986,26(1):65-82
[97]Diwekar U. M., Rubin E. S., Frey H. C. Optimal design of advanced power systems under uncertainty [J]. Energy conversion and management,1997, 38(15-17):1725-1735
[98]KanudiaA.,LoulouR.Robustresponsestoclimatechangeviastochastic MARKAL:the case of Quebec [J]. European Journal of Operational Research,1998,106:15-30
[99]Krukanont P., Tezuka T. Implications of capacity expansion under uncertainty and value of information:the near-term energy planning of Japan [J]. Energy,2007, 32:1809-1824
[100]Rong A., Lahdelma R. CO2 emissions trading planning in combined heat and power production via multi-period stochastic optimization [J]. European Journal of Opera-tional Research,2007,176:1874-1895
[101]Fuss S., Szolgayova J. Fuel price and technological uncertainty in a real options model for electricity planning [J]. Applied Energy,2010,7:2938-2944
[102]Mavrotas G., Florios K., Vlachou D. Energy planning of a hospital using mathe-matical programming and Monte Carlo simulation for dealing with uncertainty in the economic parameters [J]. Energy conversion and management,2010,51: 722-731
[103]Carpaneto E., Chicco G., Mancarella P., et al. Cogeneration planning under uncer-tainty Part I:Multiple time frame approach [J]. Applied Energy,2011,88: 1059-1067
[104]Chen C. W., Fan Y. Y. Bioethanol supply chain system planning under supply and demand uncertainties [J]. Transportation Research Part E:Logistics and Transpor-tation Review,2012,48:150-164
[105]Choi G. B., Lee S. G., Lee J. M. Modeling and stochastic dynamic optimization for optimal energy resource allocation [J]. Computer Aided Chemical Engineering, 2012,31:765-769
[106]Tian G. D., Liu Y. M., Ke H., et al. Energy evaluation method and its optimization models for process planning with stochastic characteristics:a case study in dis-assembly decision-making [J]. Computers & Industrial Engineering,2012,63: 553-563
[107]杨宁,文福栓.基于机会约束规划的输电系统规划方法[J].电力系统自动化,2004,28(14)：23-27
[108]臧宝锋,胡汉辉,王建兴.电力市场条件下的发电投资均[J].电力系统自动化,2006,30(2)：41-44
[109]万中,阳彩霞,郝爱云.地市级可再生资源能源利用优化[J].经济数学,2009,26(2)：45-51
[110]彭翔宇.能源冲击与中国经济波动——基于动态随机一般均衡模型的分析金融学[D].北京市：对外经济贸易大学,2011
[111]黄建.基于LEAP的中国电力需求情景及其不确定性分析[J].资源科学,2012,34(11)：2124-2132
[112]魏巍贤,高中元,彭翔宇.能源冲击与中国经济波动——基于动态随机一般均衡模型的分析,2012,1(379)：51-64
[113]宋杰鲲,陆悠悠,李婷婷,等.山东省能源经济环境系统目标规划模型[J].干旱区资源与环境,2013,27(8)：21-25
[114]Lin Q. G., Huang G. H. IPEM:an interval-parameter energy systems planning mo-del [J]. Energy Sources,2008,30:1382-1399
[115]LinQ. G., Huang G. H. Planning of energy system management and GHG-emission control in the Municipality of Beijing:an inexact-dynamic stochastic programming model [J]. Energy Policy,2009,37:4463-4473
[116]Cai Y. P., Huang G. H., Yang Z. F., et al. Community-scale renewable energy sys-tems planning under uncertainty:an interval chance-constrained programming app-roach [J]. Renewable and Sustainable Energy Reviews,2009,13(4):721-735
[117]Cai Y. P., Huang G. H., Tan Q., et al. An integrated approach for climate-change impact analysis and adaptation planning under multi-level uncertainties. Part I:Me-thodology [J]. Renewable and Sustainable Energy Reviews,2011,15:2779-2790
[118]Cai Y. P., Huang G. H., Tan Q., et al. An integrated approach for climate-change impact analysis and adaptation planning under multi-level uncertainties. Part Ⅱ. Ca-se study [J]. Renewable and Sustainable Energy Reviews,2011,15(6):3051-3073
[119]Liu Y., Huang G. H., Cai Y. P., et al. Development of an inexact optimization mo-del for coupled coal and power management in North China [J]. Energy Policy 2009,37(11):4345-4363
[120]Chen W. T., Li Y.P., Huang G. H., et al. A two-stage inexact-stochastic program-ming model for planning carbon dioxide emission trading under uncertainty [J]. Applied Energy,2010,87(3):1033-1047
[121]Hu Q., Huang G. H., Cai Y. P., et al. Feasibility-based inexact fuzzy programming for electric power generation systems planning under dual. Applied Energy,2011, 88(12),4642-4654
[122]Li G.C., Huang G.H., Sun W., Ding X.W. An inexact optimization model for en-ergy-environment systems management in the mixed fuzzy, dual-interval and sto-chastic environment. Renewable Energy,2014,64,153-163
[123]Zhu H., Huang W. W., Huang G. H. Planning of regional energy systems:an in-exact mixed-integer fractional programming model [J]. Applied Energy,2014,13: 500-514
[124]王守相,王成山,刘若沁.基于模糊区间算法的配电网潮流计算[J].电力系统自动化,2000,2：19-22
[125]张晓萱,黄国和,席北斗,等.不确定性的电厂动力配煤优化模型[J].中国电机工程学报,2008,36(6)：73-76
[126]李彩,黄国和,蔡宴朋.能源供需优化模型在北京市能源系统的应用[J].能源工程,2009,4：1-5
[127]胡情,黄国和,李薇.基于清洁能源可获得量违约分析的电力规划[J].中国电力,2011,46(6)：103-107
[128]庞天一,孙钊,温静雅,等.规模化养殖场沼气工程替代传统模式发电的GHG减排不确定性优化模型[J].吉林大学学报(理学版),2013,51(1)：153-157
[129]吴健中,杨培庆.能源模型的多目标规划问题[J].上海交通大学学报,1982,2：115-124
[130]徐燕椿.能源模型的方法和应用[J].能源工程,1982,2：49-51
[131]蒋金荷,姚愉芳.气候变化政策研究中经济——能源系统模型的构建[J].数量经济技术经济研究,2002,7：41-45
[132]吴永平,宋华岭,李金克.煤炭资源供应链国际化系统的复杂性研究[J].中国矿业,2007,16(6)：33-36
[133]孙岩.能源复杂系统多Agent建模方法研究[D].上海：华东理工大学,2011
[134]王亚男.能源型城市的环境问题分析[J].科技信息,2013,7：454-454
[135]王晓明.能源系统的复杂性分析及其应用研究[D].天津：天津大学,2002
[136]Huang G. H., Baetz B. W., Patry G. G. An interval linear programming approach for municipal solid waste management planning under uncertainty [J]. Civil Engi-neering Systems,1992,9:319-335
[137]尚金成,张立庆.电力节能减排与资源优化配置技术的研究与应用[J].电网技术,2007,31(22)：58-63
[138]杨燕.全国电力利用效率比较及其影响因素分析[D].北京市：华北电力大学,2008
[139]Loulou R., Kanudia A. Minimax regret strategies for greenhouse gas abatement: methodology and application [J]. Operations Research Letters,1999,25:219-230
[140]Muela E., Schweickardt G., Garces F. Fuzzy possibilistic model for medium-term power generation planning with environmental criteria [J]. Energy Policy,2007,35: 5643-5655
[141]Rachmatullah C, Aye L, Fuller RJ. Scenario planning for the electricity generation in Indonesia [J]. Energy Policy,2007,35:2352-2359
[142]Tummala R. V. M. Decision analysis with business applications [M]. New York and London:Intext Educational Publishers,1973
[143]Quaddusa M. A., Goh T. N. Electric power generation expansion:planning with multiple objectives [J]. Applied Energy,1985,19(4):301-319
[144]Li Y. P., Huang G. H. Minimax regret analysis for municipal solid wastemanage-ment:an interval-stochastic programming approach [J]. Journal of the Air & Waste Management Association,2006,56:931-944
[145]Li Y. P., Huang G. H., Nie S. L. A robust interval-based minimax-regret analysis approach for the identification of optimal water-resources-allocation strategies under uncertainty[J]. Resources, Conservationand Recycling,2009,54:86-96
[146]Huang G. H., Baetz B. W., Patry G. G. A grey fuzzy linear programming approach for municipal solid waste management planning under uncertainty [J]. Civil Engi-neering Systems,1993,10:123-146
[147]Huang G. H., Baetz B. W., Patry G. G. Grey fuzzy integer programming:an appli-cation to regional waste management planning under uncertainty. Socio-Economic Planning Sciences,1995,29:17-38
[148]Ben-Tai A., Nemirovski A. Robust solutions of linear programming problems con-taminated with uncertain data [J]. Mathematical Programming,2000,88:411-424
[149]Bertsimas D., Sim M. Robust discrete optimization and network flows [J]. Mathe-matical Programming,2003,98:49-71
[150]Bertsimas D., Sim M. The price of robustness [J]. Operations Research,2004,52: 35-53
[151]Bertsimas D., Thiele A. A robust optimization approach to inventory theory [J]. Operations Research,2006,54 (1):150-168
[152]Tan Q., Huang G. H., Cai Y. P. Radial-interval linear programming for environ-mental management under varied protection levels [J]. Journal of the Air & Waste Management Association,2010,60:1078-1093
[153]Tan Q., Huang G. H., Cai Y. P. Waste management with recourse:an inexact dy-namic programming model containing fuzzy boundary intervals in objectives and constraints [J]. Journal of Environmental Management,2010,91:1898-1913
[154]Tan Q., Huang G. H., Cai Y. P. Radial interval chance-constrained programming for agricultural non-point source water pollution control under uncertainty [J]. Agricultural Water Management,2011,98:1595-1606
[155]Labriet M., Kanudia A., Loulou R. Climate mitigation under an uncertain techno-logy future:a TIAM-World analysis [J]. Energy Economics,2012,34(S3): S366-S377
[156]Esmaili M., Amjady N., Shayanfar H. A. Stochastic congestion management in power markets using efficient scenario approaches [J]. Energy Conversation and Management,2010,51(11):2285-2293
[157]马卫华,胡彦霞.北京市能源发展研究的战略思考[J].华北电力大学学报(社会科学版),2007,(1)：43-47
[158]程伟良,王凯,李柏杰.北京市能源分布状况及转化利用分析研究[J].中国电力教育,2012,(z1)：1-2
[159]王朝华.节能优先应该成为“十二五”时期北京市能源发展的基础[J].中国集体经济,2011,(33)：36-37
[160]国家发展与改革委员会.北京市“十一五”规划
[161]国家发展与改革委员会.北京市“十二五”规划
[162]北京市统计年鉴.北京市统计局(1978-2012)
[163]国务院常务会议.城市总体规划(2004-2020)
[164]国家发展与改革委员会.北京市轨道交通建设规划(2004-2015)
[165]Zhu S. L., Jiang K. J. Analyses of technical options for mitigating CO2 emissions from an urban transport system:a case study of Beijing City [J]. International Journal of Environment and Pollution,2003,19(5):483-497
[166]Cai Y. P., Huang G. H., Yang Z. F., et al. Identification of optimal strategies for energy management systems planning under multiple uncertainties [J]. Applied Energy,2009,86:480-495
[167]Burn D. H., Lence B. J. Comparison of optimization formulations for waste-load allocations. Journalof Environmental Engineering,1992,118:597-612
[168]Ellis J. H. Acid rain control strategies:options exist despite scientific uncertainty. Environmental Science & Technology,1994,22:1248-1255
[169]Ellis J. H., Bowman M. L. Critical loads and development of acid rain control options. Journal of Environmental Engineering,1994,120:273-289
[170]Huang Y. F., Baetz B. W., Huang G. H., et al. Violation analysis for solid waste management systems:an interval fuzzy programming approach. Journal of Envi-ronmental Management,2002,65:431-446
[171]Tan, Q., Huang, G. H., Wu, C. Z., et al. Development of an inexact fuzzy robust programming model for integrated evacuation management under uncertainty. Journal of Urban Planning and Development,2009,135(1):39-49
[172]Tan, Q., Huang, G. H., Wu, C. Z., et al. IF-EM:an interval-parameter fuzzy linear programming model for environment-oriented evacuation planning under uncertainty. Journal of Advanced Transportation,2011,45(4):286-303
[173]Uri N. D. Energy demand and interfuel substitution in India. European Economic Review,1979,12(2):181-190
[174]Kemfert C, Welsch H. Energy-Capital-Labor Substitution and the Economic Ef-fects of CO2 Abatement:Evidence for Germany. Journal of Policy Modeling,2000, 22(6):641-660
[175]Ahn H., Gumus M., Kaminsky P. Pricing and manufacturing decisions when de-mand is a function of prices in multiple periods. Operations Research,2007,55(6): 1039-1057
[176]Ma H., Oxley L., Gibson J. Substitution possibilities and determinants of energy intensity for China. Energy Policy,2009,37(5):1793-1804
[177]Lin X, Janak S. L, Floudas C.A. A New robust optimization approach for schedu-ling under uncertainty:I. Bounded uncertainty. Computers & Chemical Engineering, 2004,28:1069-1085
[178]Janak S. L, Lin X. X, Floudas C. A. A new robust optimization approach for sche-duling under uncertainty II. Uncertainty with known probability distribution. Com-puters & Chemical Engineering,2007,31:171-195
[179]Pearl J. Probabilistic reasoning in intelligent systems:networks of plausible infe-rence. San Francisco:Morgan Kaufmann Publishers Inc.,1988.
[180]网慧敏.基于先验信息的统计预测方法及其应用研究[D].北京市：华北电力大学,2011
[181]许昌林,魏立力.一元线性回归模型的贝叶斯分析[J].重庆工学院学报(自然科学),2009,23(3)：98-100
[182]齐齐哈尔市统计局.2011年齐齐哈尔市国民经济和社会发展统计公报
[183]黑龙江省统计局.黑龙江统计年鉴(1990-2012)
[184]齐齐哈尔市统计局.齐齐哈尔经济统计年鉴(1989-2012)
[185]“十二五”时期齐齐哈尔结构调整和转型的思考[EB/OL].www.chinaneast.gov.cn
[186]我市“十二五”节能降耗取得重大进展[EB/OL].http://www.qqhrstats.gov.cn
[187]黑龙江省统计局齐齐哈尔市节能降耗成绩显著,存在问题须高度重视[EB/OL].http://www.hlj.stats.gov.cn/jjfx/dsfx/14604.htm
[188]齐齐哈尔市国民经济和社会发展第十二个五年规划纲要[EB/OL].http://www.qqhr.gov.cn
[189]董丽娟.齐齐哈尔市发展CNG汽车综合效益分析及推广[J].黑龙江科技信息,2009,22：41-41
[190]齐齐哈尔市人民政府.齐齐哈尔市城市总体规划(2010-2020)
[191]齐齐哈尔市2012年政府工作报告[EB/OL].http://www.qqhr.gov.cn
[192]黑龙江省人民政府.《黑龙江省新能源和可再生能源产业发展规划(2010-2020年)》
[193]田国光,甄微.黑龙江省齐齐哈尔市地热资源浅析[J].黑龙江科技信息,2011,25：32-32
[194]齐齐哈尔市生物发电：一个项目多方受益[EB/OL]. http://qiqihar.dbw.cn
[195]齐齐哈尔市2011年国民经济和社会发展计划执行情况与2012年国民经济和社会发展计划(草案)[EB/OL].http://www.qqhr.gov.cn