电价引导下电力产业链综合节能优化模型研究
|
摘要
电力产业作为能源工业的重要组成部分,其对能源供应安全具有重要影响,推动电力产业节能是缓解能源供应短缺压力保障能源供应安全的重要途径。我国通过推行发电节能调度、发电权交易、峰谷分时电价、居民阶梯电价等一系列政策,提高清洁能源发电比例,优化电力结构,促进节电降耗,在电力产业发、输、配、供、用各环节取得了一定的节能减排成果。但由于各环节利益的割裂性,已有节能措施仅从各环节自身的效益出发,难以达成电力产业链整体的协调合作,实现节能效益最大化。如何从电力产业链的总体角度出发,协调各环节节能工作的开展,实现各部分节能效益的协同与放大,成为电力产业链节能工作研究与实施的重点。以此为背景,论文基于我国当前的电力机制,针对电力产业链发、输、配、供、用各环节电价引导下促进发电侧、电网侧和用户侧联合节能的优化模型与方法进行研究,旨在为我国电力产业链节能工作的综合开展提供理论参考。
关于发电侧节能优化问题,重点研究了发电节能与电煤运输节能的优化模型。在发电节能优化上,分别针对可再生能源发电全额收购、限制部分风电出力、水火联合备用等三种调度方案构建了包括风电、光伏发电、水电、火电等多类型机组的联合调度节能优化模型,研究了既定负荷需求下调度方案对各类型机组发电出力及发电煤耗的影响。优化结果表明基于可调节水电与火电联合备用的多类型机组发电节能调度,将有利于兼顾电力系统的节能效果与经济运行。随后构建了辅助节能优化调度的发电权交易节能降耗优化模型,借助Shapley值法将发电权交易的节能效益在各参与主体之间进行分配,并依据节能贡献率确定各交易对中参与机组所得利润增量与置换价格。在电煤运输节能优化上,以电煤采购成本最小化为目标,分别构建了单一发电企业电煤供应与运输路径动态优化模型和区域电煤运输网络联合优化模型,对发电煤炭需求变动带来的电煤运输能耗差异进行分析。
关于电网侧节能优化问题,重点研究了电网侧输电阻塞管理带来的能耗增量及相应的分摊机制。在输电阻塞管理节能优化上,基于直流最优潮流理论,引入发电机输出功率转移分布因子,构建了以机组发电出力煤耗增量最小为目标不考虑负荷削减的输电阻塞管理节能优化模型,并采用基于配对综合煤耗当量的反向等量配对法进行求解,算例结果表明该模型与方法较其他阻塞管理方法可有效降低输电阻塞的煤耗增量成本。随后基于Aumann-Shapley值法对阻塞煤耗增量在阻塞路径上进行分摊,并构建了基于煤炭能源边际价值的输电引导价格设计模型,为输电线路的扩建与发展提供参考,引导用户用电结构及电源投资方向。
在用户侧节能优化方面,首先以天然气联合循环分布式发电系统为例构建了可调节出力的分布式发电系统的运行策略优化模型,并分析了调峰调度与上网电价对分布式发电运行策略的影响。随后基于分布式发电的出力能耗分析,引入可中断负荷的停电阈值价格概念,构建了考虑用户侧分布式发电与可中断负荷参与的阻塞管理联合节能优化模型,研究了发、输、配、供、用各环节的联合节能优化。此外,针对居民阶梯电价,论文构建了以发电节煤最大化为目标的电价优化设计模型,分析了不同分档比例和不同发电结构对发电节能优化效果的影响。
在产业链综合节能内在运作机制方面,论文以用户销售电价变动为例,构建了基于电价联动机制的电力产业链综合节能系统动力学模型,通过因果关系图与流积图描绘系统中各环节的发电环节、输配电环节、供用电环节的响应运作机理,借助Vensim软件对电力产业链中发电侧与用户侧的综合节能响应传递机理和电网输电阻塞节能的响应机理进行了仿真模拟,并以电网收益分享比例、煤炭需求价格弹性及火电上网电价调整时间等因素为敏感因子对电力产业链的综合节能效果进行敏感性分析。研究表明,提高销售电价带来的需求侧响应效果将引导电力产业链实现综合节能,然而在电力产业链的综合作用下该需求响应效果将随时间递减,因此需构建基于销售电价的需求侧管理长期调整机制。
Power is one of the main constitutions of energy and has great effect on energy supply security. Energy saving of power industry has been one important way for relieving the energy supply shortage pressure. Through the implementation of energy-saving dispatching, generation right exchange, TOU(time-of-use) power price, and resident multi-step power price, which improve the proportion of clean energy power generation and power generation structre, and reduce the consumption and losses of power, positive results have been achieved in energy saving in each part of power generation, power transmission and distribution, and power demand side. However, as each part of the power industry chain pays more attention on their own benefits when carring out energy-saving policies, it is difficult to realize overall coordination and cooperation of the whole power industry and achieve the optimal energy-saving benefits. How to coordinate the energy conservation measures of all participants of the power industry chain to achieve the synergy and amplification of their energy saving benefits have been a key point in the research and implemetaion of power energy-saving policies. Based on above background and current electricity market mechanism, this dissertation studied the comprehensive energy saving methods and optimal models of power industry chain that are electricity price-oriented, and providing theoretical reference for improving the coordination in each part. Based on the power industry chain, this dissertation studied the optimization models and methodology of combined energy savings in power generation, power grid and demand side under the prices in each part of power generation, power transmission and distribution, and power demand side, and designed to provide theoretical reference for the implementation of energy saving policies in power industry chain of China.
For the optimization of energy saving in power generation side, this dissertation focused on the optimization models of energy saving in power generation and electricity-coal transportation. Three energy saving optimization models for combined dispatching of multi-type generation units, including wind power generation untis, PV power generation units, hydro-power generation units, and thermal power generation units, were built based on different dispatching polices:the purchasing of electricity generated by renewable energy in full amount, restriction of wind power output, and the joint operation of hydropower and thermal power generation. The combined model includes wind power, photovoltaic power, hydropower, and thermal power. This dissertation studied the effect of dispatching methods on power output and coal consumption in each type of unit with a given load demand. The results showed that energy-saving generation dispatch of multi-type units based on an adjustable standby power combined hydropower with thermal power will achieve a better balance between energy-saving effect and economic operation of power system. An optimization model of energy-saving and emission-reduction for generation rights trade is built. Energy-saving benefits from power rights trade can be allocated among the participants with Shapley-value method. Incremental profit and generation exchange price can be fixed based on the contribution rate of energy-saving. For the optimization of coal transportation, this dissertation established a dynamic optimization model of coal supply and transportation route with a single generation enterprise, and a combined optimization model of coal transportation network. An analysis of energy consumption differences in coal transportation brought by the change of coal demand has been taken in the dissertation.
For the optimization of energy saving in power grid side, this dissertation focused on the coal consumption increment and share policy from the transmission congestion management. For the optimization of transmission congestion management, this dissertation introduced GSDF (generation shifted distribution factor) based on DCOPF theory. With the target of the minimum coal consumption increment, an optimization model for the energy-saving in transmission congestion management was built without considering load curtailment. Based on equal and opposite quantity in pairs of comprehensive coal consumption equivalent to solve a numerical example. The result showed that the cost of coal consumption increment in transmission congestion is decreased. The coal consumption increment was allocated in the congestion route based on Aumann-Shapley value method. This dissertation built a price design model of power transmission guiding price based on the energy marginal value. Providing theoretical reference for the development and extension of power transmission line, and guiding the power consumption structure and generation investment.
For the optimization of energy saving in demand side, this dissertation built a strategy optimization model of distributed power generation system with an adjusted output by taking the distributed combined cycle gas plant as an example. Analyzing the effect of peak load dispatching and electricity price on distributed power generation. Based on the analysis of distributed power generation output, this dissertation built an optimization model of combined energy-saving in congestion management by introducing the outage threshold price of interruptible load. This model takes the distributed generation in the demand side and interruptible load into consideration and then studies the combined energy-saving optimization of power generation, power transmission and power demand. Moreover, for the tiered pricing, this dissertation built an optimization model of electricity price, which aimed at a maximum coal savings. An analysis of the effect of different gradation and different generation structures on the energy-saving optimization of power generation has been taken in the dissertation.
For the inner operating mechanism of comprehensive energy saving in the power industry chain, this dissertation establishes a price-oriented system dynamics model of comprehensive energy saving in the power industry chain. Describing the response operation mechanism between each part through causal loop and flow-stock diagram, and simulate the variation trend and coal-saving effect under the comprehensive function of power industry chain by Vensim. The result shows that, the effect of demand side response of increasing sale price will lead comprehensive energy savings in power industry chain. By increasing sale price and rolling the proceeds into transmission capacity construction, and the compensation of clean energy, it will decrease the power transmission congestion cost and coal consumption. However, the effect of demand response will decrease progressively over time. A long-term adjustment mechanism of sale-price-oriented demand side management is needed.
关于发电侧节能优化问题,重点研究了发电节能与电煤运输节能的优化模型。在发电节能优化上,分别针对可再生能源发电全额收购、限制部分风电出力、水火联合备用等三种调度方案构建了包括风电、光伏发电、水电、火电等多类型机组的联合调度节能优化模型,研究了既定负荷需求下调度方案对各类型机组发电出力及发电煤耗的影响。优化结果表明基于可调节水电与火电联合备用的多类型机组发电节能调度,将有利于兼顾电力系统的节能效果与经济运行。随后构建了辅助节能优化调度的发电权交易节能降耗优化模型,借助Shapley值法将发电权交易的节能效益在各参与主体之间进行分配,并依据节能贡献率确定各交易对中参与机组所得利润增量与置换价格。在电煤运输节能优化上,以电煤采购成本最小化为目标,分别构建了单一发电企业电煤供应与运输路径动态优化模型和区域电煤运输网络联合优化模型,对发电煤炭需求变动带来的电煤运输能耗差异进行分析。
关于电网侧节能优化问题,重点研究了电网侧输电阻塞管理带来的能耗增量及相应的分摊机制。在输电阻塞管理节能优化上,基于直流最优潮流理论,引入发电机输出功率转移分布因子,构建了以机组发电出力煤耗增量最小为目标不考虑负荷削减的输电阻塞管理节能优化模型,并采用基于配对综合煤耗当量的反向等量配对法进行求解,算例结果表明该模型与方法较其他阻塞管理方法可有效降低输电阻塞的煤耗增量成本。随后基于Aumann-Shapley值法对阻塞煤耗增量在阻塞路径上进行分摊,并构建了基于煤炭能源边际价值的输电引导价格设计模型,为输电线路的扩建与发展提供参考,引导用户用电结构及电源投资方向。
在用户侧节能优化方面,首先以天然气联合循环分布式发电系统为例构建了可调节出力的分布式发电系统的运行策略优化模型,并分析了调峰调度与上网电价对分布式发电运行策略的影响。随后基于分布式发电的出力能耗分析,引入可中断负荷的停电阈值价格概念,构建了考虑用户侧分布式发电与可中断负荷参与的阻塞管理联合节能优化模型,研究了发、输、配、供、用各环节的联合节能优化。此外,针对居民阶梯电价,论文构建了以发电节煤最大化为目标的电价优化设计模型,分析了不同分档比例和不同发电结构对发电节能优化效果的影响。
在产业链综合节能内在运作机制方面,论文以用户销售电价变动为例,构建了基于电价联动机制的电力产业链综合节能系统动力学模型,通过因果关系图与流积图描绘系统中各环节的发电环节、输配电环节、供用电环节的响应运作机理,借助Vensim软件对电力产业链中发电侧与用户侧的综合节能响应传递机理和电网输电阻塞节能的响应机理进行了仿真模拟,并以电网收益分享比例、煤炭需求价格弹性及火电上网电价调整时间等因素为敏感因子对电力产业链的综合节能效果进行敏感性分析。研究表明,提高销售电价带来的需求侧响应效果将引导电力产业链实现综合节能,然而在电力产业链的综合作用下该需求响应效果将随时间递减,因此需构建基于销售电价的需求侧管理长期调整机制。
Power is one of the main constitutions of energy and has great effect on energy supply security. Energy saving of power industry has been one important way for relieving the energy supply shortage pressure. Through the implementation of energy-saving dispatching, generation right exchange, TOU(time-of-use) power price, and resident multi-step power price, which improve the proportion of clean energy power generation and power generation structre, and reduce the consumption and losses of power, positive results have been achieved in energy saving in each part of power generation, power transmission and distribution, and power demand side. However, as each part of the power industry chain pays more attention on their own benefits when carring out energy-saving policies, it is difficult to realize overall coordination and cooperation of the whole power industry and achieve the optimal energy-saving benefits. How to coordinate the energy conservation measures of all participants of the power industry chain to achieve the synergy and amplification of their energy saving benefits have been a key point in the research and implemetaion of power energy-saving policies. Based on above background and current electricity market mechanism, this dissertation studied the comprehensive energy saving methods and optimal models of power industry chain that are electricity price-oriented, and providing theoretical reference for improving the coordination in each part. Based on the power industry chain, this dissertation studied the optimization models and methodology of combined energy savings in power generation, power grid and demand side under the prices in each part of power generation, power transmission and distribution, and power demand side, and designed to provide theoretical reference for the implementation of energy saving policies in power industry chain of China.
For the optimization of energy saving in power generation side, this dissertation focused on the optimization models of energy saving in power generation and electricity-coal transportation. Three energy saving optimization models for combined dispatching of multi-type generation units, including wind power generation untis, PV power generation units, hydro-power generation units, and thermal power generation units, were built based on different dispatching polices:the purchasing of electricity generated by renewable energy in full amount, restriction of wind power output, and the joint operation of hydropower and thermal power generation. The combined model includes wind power, photovoltaic power, hydropower, and thermal power. This dissertation studied the effect of dispatching methods on power output and coal consumption in each type of unit with a given load demand. The results showed that energy-saving generation dispatch of multi-type units based on an adjustable standby power combined hydropower with thermal power will achieve a better balance between energy-saving effect and economic operation of power system. An optimization model of energy-saving and emission-reduction for generation rights trade is built. Energy-saving benefits from power rights trade can be allocated among the participants with Shapley-value method. Incremental profit and generation exchange price can be fixed based on the contribution rate of energy-saving. For the optimization of coal transportation, this dissertation established a dynamic optimization model of coal supply and transportation route with a single generation enterprise, and a combined optimization model of coal transportation network. An analysis of energy consumption differences in coal transportation brought by the change of coal demand has been taken in the dissertation.
For the optimization of energy saving in power grid side, this dissertation focused on the coal consumption increment and share policy from the transmission congestion management. For the optimization of transmission congestion management, this dissertation introduced GSDF (generation shifted distribution factor) based on DCOPF theory. With the target of the minimum coal consumption increment, an optimization model for the energy-saving in transmission congestion management was built without considering load curtailment. Based on equal and opposite quantity in pairs of comprehensive coal consumption equivalent to solve a numerical example. The result showed that the cost of coal consumption increment in transmission congestion is decreased. The coal consumption increment was allocated in the congestion route based on Aumann-Shapley value method. This dissertation built a price design model of power transmission guiding price based on the energy marginal value. Providing theoretical reference for the development and extension of power transmission line, and guiding the power consumption structure and generation investment.
For the optimization of energy saving in demand side, this dissertation built a strategy optimization model of distributed power generation system with an adjusted output by taking the distributed combined cycle gas plant as an example. Analyzing the effect of peak load dispatching and electricity price on distributed power generation. Based on the analysis of distributed power generation output, this dissertation built an optimization model of combined energy-saving in congestion management by introducing the outage threshold price of interruptible load. This model takes the distributed generation in the demand side and interruptible load into consideration and then studies the combined energy-saving optimization of power generation, power transmission and power demand. Moreover, for the tiered pricing, this dissertation built an optimization model of electricity price, which aimed at a maximum coal savings. An analysis of the effect of different gradation and different generation structures on the energy-saving optimization of power generation has been taken in the dissertation.
For the inner operating mechanism of comprehensive energy saving in the power industry chain, this dissertation establishes a price-oriented system dynamics model of comprehensive energy saving in the power industry chain. Describing the response operation mechanism between each part through causal loop and flow-stock diagram, and simulate the variation trend and coal-saving effect under the comprehensive function of power industry chain by Vensim. The result shows that, the effect of demand side response of increasing sale price will lead comprehensive energy savings in power industry chain. By increasing sale price and rolling the proceeds into transmission capacity construction, and the compensation of clean energy, it will decrease the power transmission congestion cost and coal consumption. However, the effect of demand response will decrease progressively over time. A long-term adjustment mechanism of sale-price-oriented demand side management is needed.
引文
[1]Ming Yang. China's energy efficiency target 2010 [J]. Energy Policy,2008, (36): 561-570
[2]P.A. Steenhof. Decomposition of electricity demand in China's industrial sector [J]. Energry Economics,2006(28):370-384
[3]魏一鸣.中国可持续发展战略报告[M].北京：科学出版社,2006
[4]马驹.全国主要煤炭基地煤炭运输铁路水路分工研究[J].铁道工程学报,2008,6(06)：20-23.
[5]刘敬青.我国大陆煤炭中长距离运输经济性分析[J].综合运输,2010,10：66-74.
[6]高卫东,姜巍.中国煤炭资源供应格局演变及流动路径分析[J].地域研究与开发,2012,31(02)：9-14.
[7]姜广君.我国能源运输通道体系综合评价及优化研究[D].中国矿业大学(北京,2011.
[8]赵聪颖.煤炭物流运输网络效率分析[D].上海交通大学,2013.
[9]姜庆国.电煤供应链碳排放过程及测度研究[D].北京交通大学,2013.
[10]邱黎明.煤炭物流网络服务节点选择研究[D].西南交通大学,2013.
[11]于良,金凤君,张兵.中国煤炭运输的现状、发展趋势与对策研究[J].铁道经济研究,2006,05：38-41.
[12]李天威.火电行业低碳减排对策建议[J].环境保护,2013,24：43-45.
[13]杜欣慧,孙红玲.节能发电调度机组组合算法的研究[J].现代电力,2010,27(03)：21-24.
[14]谢国辉,张粒子,舒隽,王家兴.火电机组日前节能发电调度机组组合[J].电力系统及其自动化学报,2011,23(02)：122-126.
[15]赖菲,王智微,郭翔.火电机组节能发电调度系统研究(一)系统概述[J].发电技术论坛,2009,38(9)：92-94
[16]赖菲,王庭飞,郭翔.火电机组节能发电调度系统研究(二)关键技术[J].发电技术论坛,2010,39(4)：83-86
[17]赖菲,王智微,郭翔.火电机组节能发电调度系统研究(三)系统实现[J].发电技术论坛,2010,39(5)：98-100
[18]吴至复,曾鸣,刘宝华,等.电力市场中的水火电优化调度模型及其应用[J].电网技术,2006,30(15)：45-49
[19]韩冬,蔡兴国.综合环境保护及峰谷电价的水火电短期优化调度[J].电网技术.2009,33(14)：78-83
[20]张炜,何光宇,王稹,等.水火联合电力系统中期经济调度系统的研究[J].电网技术,2002,26(10)：6-9.
[21]Sasikala J, Ramaswamy M. Optimal Gamma Based Fixed Head Hydro Thermal Scheduling Using Granitic Algorithmic [J]. Expert Systems with Applications, 2010,37(4):3352-3357
[22]陈刚,相年德,陈雪青.水火联合电力系统长期优化调度模型与算法[J].水电能源科学.1992,10(1):48-57
[23]AmjadyN, Farrokhzad D, Modarres M. ptimal Reliable Operation of Hydro Thermal Power Systems with Random Unit Outages [J]. IEEE Transactions on Power Systems,2003,18(1):279-289
[24]谢春瑰,高长征,李莹莹,房芳.风火电联合运营优化分析[J].煤炭技术,2011,30(12)：275-277.
[25]汪宁渤,丁坤,陟晶,马彦宏,王建东.风电火电打捆联合外送是解决风电市场瓶颈的有效途径[J].电力技术,2010,19(13-14)：1-4+19.
[26]申杨硕,陈致宏,鞠立伟,谭忠富,赵宝柱.节能调度环境下风、火电联合运行优化模型及其应用[J].水电能源科学,2013,31(05)：235-238.
[27]龙军,莫群芳,曾建.基于随机规划的含风电场的电力系统节能优化调度策略[J].电网技术,2011,35(9)：133-138.
[28]孙元章,吴俊,李国杰,何剑.基于风速预测和随机规划的含风电场电力系统动态经济调度[J].中国电机工程学报,29(2009),G4：41-47.
[29]吕清洁.含风/水/火电的电力系统动态经济调度和节能调度[D].重庆大学,2012.
[30]秦珍.多电源电力系统多目标优化调度研究[D].广西大学,2012.
[31]尚金成.基于节能减排的发电权交易理论及应用(一)发电权交易理论[J].电力系统自动化,2009,33(12)：46-52.
[32]郑欣,蒋传文,李磊,赵岩.基于能耗和效益最优的发电权节能降耗分析[J].电力系统自动化,2008,32(24)：39-42.
[33]陈赞,严正.考虑节能减排与网络约束的发电权交易模型[J].电力系统保护与控制,2009,37(12)：52-57.
[34]许荣,赵岩,李磊,蒋传文,黄加一.基于节能降耗的发电权交易效益分析[J].水电能源科学,2007,25(06)：150-153.
[35]王雁凌,邱小燕,许传龙.以节能降耗为目标的发电权交易阻塞管理模型 [J].电网技术,2012,36(06)：272-276.
[36]Christie R D, Wollenberg B F, Wangensteen I. Transmission management in the deregulated environment[J]. IEEE Proceedings,2000,88(2):170-195.
[37]Shirmohammadi D, Wollenberg B, Vojdani A et al. Transmission dispatch and congestion management in the emerging energy market structures[J]. IEEE Transactions on Power Systems,1998,13(4):1466-1474.
[38]Christie R D, Wangensteen I. The energy market in Norway and Sweden:congestion management[J]. IEEE Trans on Power Engineering Review,1998,18(5):61-63.
[39]de Vries L J. Capacity allocation in a restructured electricity market:technical and economic evaluation of congestion management methods on interconnectors[DB]. Power Tech Proceedings,2001 IEEE Porto,vol.1,6 pp.
[40]Oren S,Spiller P T,Varaiya P et al. Nodal prices and transmission rights:a critical appraisal[A]. In:Working Paper of Program on Workable Energy Regulation[C]. Berkeley:1996.
[41]Oren S,Varaiya P,Wu F. Folk theorems on transmission open access:proofs and counterexamples[R]. Power Report PWP-23, University of California Energy Institute,1994.
[42]Rau N S. Transmission loss and congestion cost allocation-an approach based on responsibility[J]. IEEE Transactions on Power Systems,2000,15(4):579-585.
[43]李帆,朱敏.英国电力市场及输电系统简介[J]. 电力系统自动化.1999,23(2)：33-40.
[44]Singh H, Hao S, Papalexopoulos A. Transmission congestion management in competitive electricity markets[J]. IEEE Transactions on Power Systems,1998,13(2):672-680.
[45]Baran M E,Banunarayanan V,Garren K E. Equitable allocation of congestion relief cost to trans mission[J]-IEEE Transactions on Power Systems,2000,15(2):579-585.
[46]张永平,焦连伟,陈寿孙,严正,文福拴,倪以信,吴复立.电力市场阻塞管理综述[J].电网技术,2003,08：1-9+32.
[47]Schweppe F C,Caramanis M C,Tabors R D et al. Spot price of electricity[M]. Kluwer Academic Publishers,1988.
[48]WilliamH. Contract networks for electric power transmission[J]. Journal of Regulatory Economics,1992,4(9):211-242.
[49]David A K. Dispatch methodologies for open access transmission systems[J]. IEEE Trans on Power Systems,1998,13(l):46-53.
[50]Lei J,Deng Y,Zhang R. Congestion management for generation scheduling in a deregulated Chinese power system[A]. Power Engineering Society Winter Meeting,IEEE[C],2001,3:1262-1265.
[51]王秀丽,甘志,雷兵,等(Wang Xiuli,Gan Zhi,Lei Bing et al).输电阻塞管理的灵敏度分析模型及算法(Sensitivity analysis approach of transmission congestion management) [J].电力系统自动化(Automation of Electric Power Systems),2002,26(4):10-13.
[52]Fang R S, David A K. Optimal dispatch under transmission contracts[J]. IEEE Trans on Power Systems,1999,14(2):732-737.
[53]Losi A. Trade curtailment schemes for the security control of the transmission network in a deregulated environment[J]. International Journal of Electrical Power & Energy Systems,2002,24(1):9-17.
[54]Galiana F D,Ilic M. A mathematical framework for the analysis and management of power transactions under open access[J]. IEEE Transactions on Power Systems,1998,13(2):681-687.
[55]Galiana F D,Kockar I, Franco P C. Combined pool/bilateral dispatch-Part Ⅰ: performance of trading strategies[J]. IEEE Transactions on Power Systems,2002,17(11):92-99.
[56]Wang X,Song Y H,Lu Q. Primal-dual interior point linear programming optimal power flow for real-time congestion management A]. Power Engineering Society Winter Meeting,IEEE,2000,3:1643-1649.
[57]Wang Xing,Song Yonghua,Lu Qiang. A coordinated real-time optimal dispatch method for unbundled electricity markets[J]. IEEE Transactions on Power Systems,2002,17(2):482-490.
[58]GehLringKL. Canyesteulay demand-side management lessons become tomorrow's market solutions? The Electricity Journal,2002;15(5):63-69
[59]Murat Fahrioglu, FemandoL. Alvarado. Designing incentive compatible contracts for effective demand management. IEEE Trans. on Power Systems,2000; 15(4):1255-1260
[60]Walid EI-Khattam, Kankar Bhattacharya, Hegazy Y, Salama M. M. A. Optimal investment Planning for distribute degeneration in a competitive electricity market. IEEE Trans, on power Systems,2004;19(3):1674-1684
[61]Mamisa Pipattanasompom, Miehael Willingham, Saifur Rahman. Implieationofon site distribute degeneration for commercial/industrial facilities. IEEE Transactions on Power Systems,2005:20(1):206-212
[62]Walid EI-Khattam, Hegazy Y. Q Salama M. M. A. Investigating distributed generation systems Performance using Monte Carlo simulation. IEEE Trans, n power Systems,2006:21(2):524-532
[63]Sehweppe F C, Caramanis MC, Tabors R D, Bohn RE. Spot Price of electricity [M]. Kluwer Academic Publishers,1988
[64]汤振七,于尔铿,唐国庆.电力市场输电阻塞管理[J].电力系统自动化,2001,25(23)：13-16
[65]贺辉,徐政,黄莹.负荷在阻塞管理中的作用[J].电力系统自动化,2003,27(17)：11-14
[66]凌永伟,辛洁晴,言茂松.基于输电当量电价的直接阻塞管理[J].电网技术,.2003,27(7)：1 9-24
[67]赵晋泉,侯志俭,吴际舜.电力市场中的交易模式和传输阻塞管理J[].电力系统自动化.1999,23(20)：5-8
[68]Hogan W. Contraet networks for electric Power transmission[J]. Journal of Regulatory Economics,1992,4(9):211-242
[69]Baran ME,Banunaryranan V, Garren KE. Equitable allocation of congestion relief cost to transactions [J]. IEEE Transactions on Power Systems.2000, 15(2):579-585
[7C]Fradi A, Brignone S, Wollenberg BE. Calculation of energy transaction allocation factors [J]. IEEE Transactions on Power Systems,2001,16(2):266-272
[71]Baran ME,Banunarryanan V, GarrenKE, A transaction assessment method of allocation of transmission services [J]. IEEE Transactions on Power Systems, 1999,14(3):920-928
[72]P. J. Mago,A. K. Hueffed, Evaluation of a turbine driven CCHP system for large office buildings under different operating strategies[J]. Energy and Buildings 42 (2010) 1628-1636.
[73]P. J. Mago, L. M. Chamra, Analysis and optimization of CCHP systems based on energy, economical, and environmental considerations[J]. Energy and Buildings 41 (2009) 1099-1106.
[74]J. Wang, Y. Jing, C. Zhang, Z. Zhai, Performance comparison of combined cooling heating and power system in different operation modes[J]. Applied Energy 88 (2011) 4621-4631.
[75]N. Fumo, L.M. Chamra. Analysis of combined cooling, heating, and power systems based on source primary energy consumption[J]. Applied Energy 87 (2011)2023-2030.
[76]D. Maraver, A. Sin, F. Sebastian, J. Royo, Environmental assessment of CCHP (combined cooling heating and power) systems based on biomass combustion in comparison to conventional generation[J]. Energy 57 (2013) 17-23.
[77]X. Q. Kong, R. Z. Wang, X. H. Huang, Energy optimization model for a CCHP system with available gas turbines[J]. Applied Thermal Engineering 25 (2005)377-391.
[78]B.Zhang, W. Long, An optimal sizing method for cogeneration plants[J]. Energy and Buildings 38 (2006) 189-195.
[79]C.Z. Li, Y.M. Shi, X.H. Huang, Sensitivity analysis of energy demands on performance of CCHP system[J]. Energy Conversion and Management 49 (2008) 3491-3497.
[80]H.Li, R.Nalim, P.A. Haldi. Thermal-economic optimization of a distributed multi-generation energy system-A case study of Beijing[J]. Applied Thermal Engineering 26 (2006) 709-719.
[81]Y. Jing, H. Bai, J. Wang. Multi-objective optimization design and operation strategy analysis of BCHP system based on life cycle assessment[J]. Energy 37 (2012) 405-416.
[82]J. Wang, Y. Jing, C. Zhang, J. Zhao, Review on multi-criteria decision analysis aid in sustainable energy decision-making[J]. Renewable and Sustainable Energy Reviews 13 (2009) 2263-2278.
[83]H. Ren, W. Zhou, K. Nakagami, W. Gao, Q. Wu, Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects[J]. Applied Energy 87 (2010) 3642-3651.
[84]B. Jabbari, N.Tahouni, A. Ataei, M.H. Panjeshahi, Design and optimization of CCHP system incorporated into kraft process, using pinch analysis with pressure drop consideration[J]. Applied Thermal Engineering in press (2013)1-10.
[85]Gh.Abdollahia, M. Meratizamanb. Multi-objective approach in thermoenvironomic optimization of a small-scale distributed CCHP system with risk analysis[J]. Energy and Buildings 43 (2011) 3144-3153.
[86]J. Wang, Y. Jing, C. Zhang, Optimization of capacity and operation for CCHP system by genetic algorithm[J]. Applied Energy 87 (2010) 1325-1335.
[87]J. Wu, J. Wang, S. Li, Multi-objective optimal operation strategy study of micro-CCHP system[J]. Energy 48 (2012) 472-483.
[88]E. Bernier, F. Mare'chal, R. Samson, Multi-objective design optimization of a natural gas-combined cycle with carbon dioxide capture in a life cycle perspective[J]. Energy 35 (2010) 1121-1128.
[89]H. Sayyaadi G. Abdollahi, Application of the multi-objective optimization and risk analysis for the sizing of a residential small-scale CCHP system[J]. Energy and Buildings 60 (2013) 330-344.
[90]K.C. Kavvadias, Z.B. Maroulis, Theproblem is solved using a multi-objective evolutionary algorithm[J]. Energy Policy38(2010)945-954.
[91]Subcommittee, L.. Spot-Price Marketing of Surplus Energy By the Sharing of Interruptible Loads In A Regional Power Pool[J]. IEEE Transaetions on Power Apparatus and Systems,1985, PAS-104(2):328-336.
[92]Peng Wang,Yong Liu,Goel L.,Quiwei Wu,Yi Ding,WenPing Qin. A reliability-based reserve criterion for market settlement within terruptible loads [C]. Proeeedings-2009 IEEE Power & Energy Society General Meeting,2009,1:514-517.
[93]Bai,J.,Gooi, H.B., Xia, L.M., Strbae, G., Venkatesh,B.. A Probabilistic Reserve Market Incorporating Interruptible Load[C]. Proceedings-2007 IEEE Power Engineering Soeiety General Meeting,2007:1-1
[94]Chowdhury,N., Billinton,R.. Interruptible load considerations in spinning reserve assessment of isolated and interconnected generating systems[J]. IEE Proceedings of Generation,Transmission and Distribution,1990,137(2):159-167.
[95]Kun-Yuan Huang,Yann-Chang Huang. Integrating direct load control with interruptible load management to Provide instantaneous reserves for aneillary services[J]. IEEE Transactions on Power Systems,2004,19(3):1626-1634.
[96]Le Anh Tuan,Kankar Bhattacharya,Jaap Daalder. Transmission congestion management in bilateral markets:An interruptible load auction solution[J]. Electric Power systems Researeh,2005,74(3):379-389.
[97]Raikar, S.,Ilic,M.. Interruptible Physcal transmission contracts for congestion management[C]. IEEE 2001 Power Engineering Society Summer Meeting,2001,3:1639-164.
[98]国家电网公司电力需求侧管理指导中心.美国节能服务行业回顾[M].2005
[99]Gehring Kay L. Can yesterday's demand-side management lessons become tomorrow's market solutions[J]. The Electricity Journal,2002,15(5)
[100]Raymond F. Ghajar, Roy Billinton. Economic costs of power interruptions:a consistent model and methodology[J]. Electrical Power and Energy Systems, 2006,28.
[101]Le Anh Tuan, Kankar Bhattacharya. Competitive Framework for Procurement of Interruptible Load Services[J]. IEEE Transactions on Power Systems,Vol.18, No.2, May 2003.
[102]C. N. Kurucz D. Brandt S. Sim. A Linear Programming Model for Reducing System Peak Through Customer Load Control Programs[J]. IEEE Transactions on Power Systems, Vol.11, No.4,1996.
[103]Kun-Yuan Huang and Yann-Chang Huang. Integrating Direct Load Control With Interruptible Load Management to Provide Instantaneous Reserves for Ancillary Services[J], IEEE TRANSACTIONS ON POWER SYSTEMS, VOL.19, NO.3,2004.
[104]黄海涛,张粒子,乔慧婷,杜宁.基于变密度聚类的居民阶梯分段电量制定方法[J].电网技术,2010,34(11)：111-116.
[105]黄海涛.居民阶梯电价结构与水平设计的模型研究[J].华东电力,2012,40(5)：721-727.
[106]朱柯丁,宋艺航,谭忠富,吴海林.居民生活阶梯电价设计优化模型[J].华东电力,2011,39(6)：862-867.
[107]李媛,罗琴,宋依群,徐剑,蔡磊,顾俊.基于模糊响应的阶梯电价机制研究[J].华东电力,2012,40(3)：367-372.
[108]李媛,罗琴,宋依群,等.基于需求响应的居民分时阶梯电价档位制定方法研究[J].电力系统保护与控制,2012,40(18)：65-68.
[109]黄海涛.居民分时阶梯电价联合优化模型研究[J].电网技术,2012,36(10)：253-258.
[110]曾明,李娜,刘超.基于效用函数的居民阶梯电价方案的节电效果评估[J].华东电力,2011,39(8)：1215-1219.
[111]吴建宏,曾明,刘宏志,刘超,周飞.基于灰色关联度和理想解法的居民阶梯电价实施效果评价[J].水电能源科学,2012,30(12)：187-190.
[112]张勃.北京地区光伏系统发电功率预测的研究[D].燕山大学,2013.
[113]吴宏宇,管晓宏,翟桥柱,高峰.水火电联合短期调度的混合整数规划方法[J].中国电机工程学报,2009,28：82-88.
[114]陈启鑫,康重庆,程旭东,綦海昌,马志波.考虑阻塞管理的发电权交易模型及其网络流算法[J].中国电机工程学报,2008,34：106-111.
[115]赵晋泉,叶君玲,邓勇.直流潮流与交流潮流的对比分析[J].电网技术,2012,10：147-152.
[116]张伯明,陈寿孙,严正.高等电力网络分析[M].北京：清华大学出版社,2007：191-193.
[117]高志华,任震,黄福全,潘锡芒.输电阻塞中的多机组联合竞标策略[J].电网技术,2004,05：55-58+76.
[118]杨朋朋,韩学山,查浩.一种计及静态安全约束机组组合的有效算法[J].电力系统自动化,2009,33(8)：39-43.
[119]江健健,夏清,沈瑜,康重庆,许洪强,张元鹏.基于边际电价的短期机组组合求解新方法[J].清华大学学报(自然科学版),2001,Z1：151-154.
[120]Li Fangxing,Bo Rui. DCOPF-based LMP simulation:algorithm,comparison with ACOPF,and sensitivity[J]. IEEE Transactions onPower Systems,007,22(4): 1475-1485.
[121]朱民茂.电力市场下基于灵敏度因子的超短期阻塞管理[D].湖南大学,2007.
[122]吴政球,朱民茂,王良缘,方日升,叶世顺.超短期在线输电阻塞管理模型及算法[J].电力系统及其自动化学报,2007,06：109-113.
[123]王秀丽,甘志,雷兵,王锡凡.输电阻塞管理的灵敏度分析模型及算法[J].电力系统自动化,2002,04：10-13+22.
[124]杨洪明,段献忠.双边交易模式下基于Aumann-Shapley值的阻塞费用分摊方法研究[J].中国电机工程学报,2002,11：60-64.
[125]刘岩,于渤,洪富艳.可再生能源价值构成与定价模型研究[J].预测,2011,01：61-65.
[126]高彦辉.电力市场阻塞管理中的电价[J].电网技术,2008,S2：223-225.
[127]毛伟明,周明,李庚银.多时段下计及可中断负荷的电网输电阻塞管理[J].电网技术,2008,04：72-77.
[128]申晓军.城镇居民生活用电需求弹性分析[D].浙江大学,2011.
[129]Faisal Jamil, Eatzaz Ahmad. Income and price elasticities of electricity demand: Aggregate and Sector-wise analyses[J]. Energy policy,2011,39:5519-5527.
[130]林伯强.争议阶梯电价[J].中国电力企业管理,2010,(27)：20-22.
[131]成邦文.基于对数正态分布的洛伦兹曲线与基尼系数[J].数量经济技术经济研究,2005,(2)：127-135.
[132]丁世龙,张金隆.采用差别定价方法制定居民电价的研究[J].价格理论与实践,2006,(7)：23-24.
[2]P.A. Steenhof. Decomposition of electricity demand in China's industrial sector [J]. Energry Economics,2006(28):370-384
[3]魏一鸣.中国可持续发展战略报告[M].北京：科学出版社,2006
[4]马驹.全国主要煤炭基地煤炭运输铁路水路分工研究[J].铁道工程学报,2008,6(06)：20-23.
[5]刘敬青.我国大陆煤炭中长距离运输经济性分析[J].综合运输,2010,10：66-74.
[6]高卫东,姜巍.中国煤炭资源供应格局演变及流动路径分析[J].地域研究与开发,2012,31(02)：9-14.
[7]姜广君.我国能源运输通道体系综合评价及优化研究[D].中国矿业大学(北京,2011.
[8]赵聪颖.煤炭物流运输网络效率分析[D].上海交通大学,2013.
[9]姜庆国.电煤供应链碳排放过程及测度研究[D].北京交通大学,2013.
[10]邱黎明.煤炭物流网络服务节点选择研究[D].西南交通大学,2013.
[11]于良,金凤君,张兵.中国煤炭运输的现状、发展趋势与对策研究[J].铁道经济研究,2006,05：38-41.
[12]李天威.火电行业低碳减排对策建议[J].环境保护,2013,24：43-45.
[13]杜欣慧,孙红玲.节能发电调度机组组合算法的研究[J].现代电力,2010,27(03)：21-24.
[14]谢国辉,张粒子,舒隽,王家兴.火电机组日前节能发电调度机组组合[J].电力系统及其自动化学报,2011,23(02)：122-126.
[15]赖菲,王智微,郭翔.火电机组节能发电调度系统研究(一)系统概述[J].发电技术论坛,2009,38(9)：92-94
[16]赖菲,王庭飞,郭翔.火电机组节能发电调度系统研究(二)关键技术[J].发电技术论坛,2010,39(4)：83-86
[17]赖菲,王智微,郭翔.火电机组节能发电调度系统研究(三)系统实现[J].发电技术论坛,2010,39(5)：98-100
[18]吴至复,曾鸣,刘宝华,等.电力市场中的水火电优化调度模型及其应用[J].电网技术,2006,30(15)：45-49
[19]韩冬,蔡兴国.综合环境保护及峰谷电价的水火电短期优化调度[J].电网技术.2009,33(14)：78-83
[20]张炜,何光宇,王稹,等.水火联合电力系统中期经济调度系统的研究[J].电网技术,2002,26(10)：6-9.
[21]Sasikala J, Ramaswamy M. Optimal Gamma Based Fixed Head Hydro Thermal Scheduling Using Granitic Algorithmic [J]. Expert Systems with Applications, 2010,37(4):3352-3357
[22]陈刚,相年德,陈雪青.水火联合电力系统长期优化调度模型与算法[J].水电能源科学.1992,10(1):48-57
[23]AmjadyN, Farrokhzad D, Modarres M. ptimal Reliable Operation of Hydro Thermal Power Systems with Random Unit Outages [J]. IEEE Transactions on Power Systems,2003,18(1):279-289
[24]谢春瑰,高长征,李莹莹,房芳.风火电联合运营优化分析[J].煤炭技术,2011,30(12)：275-277.
[25]汪宁渤,丁坤,陟晶,马彦宏,王建东.风电火电打捆联合外送是解决风电市场瓶颈的有效途径[J].电力技术,2010,19(13-14)：1-4+19.
[26]申杨硕,陈致宏,鞠立伟,谭忠富,赵宝柱.节能调度环境下风、火电联合运行优化模型及其应用[J].水电能源科学,2013,31(05)：235-238.
[27]龙军,莫群芳,曾建.基于随机规划的含风电场的电力系统节能优化调度策略[J].电网技术,2011,35(9)：133-138.
[28]孙元章,吴俊,李国杰,何剑.基于风速预测和随机规划的含风电场电力系统动态经济调度[J].中国电机工程学报,29(2009),G4：41-47.
[29]吕清洁.含风/水/火电的电力系统动态经济调度和节能调度[D].重庆大学,2012.
[30]秦珍.多电源电力系统多目标优化调度研究[D].广西大学,2012.
[31]尚金成.基于节能减排的发电权交易理论及应用(一)发电权交易理论[J].电力系统自动化,2009,33(12)：46-52.
[32]郑欣,蒋传文,李磊,赵岩.基于能耗和效益最优的发电权节能降耗分析[J].电力系统自动化,2008,32(24)：39-42.
[33]陈赞,严正.考虑节能减排与网络约束的发电权交易模型[J].电力系统保护与控制,2009,37(12)：52-57.
[34]许荣,赵岩,李磊,蒋传文,黄加一.基于节能降耗的发电权交易效益分析[J].水电能源科学,2007,25(06)：150-153.
[35]王雁凌,邱小燕,许传龙.以节能降耗为目标的发电权交易阻塞管理模型 [J].电网技术,2012,36(06)：272-276.
[36]Christie R D, Wollenberg B F, Wangensteen I. Transmission management in the deregulated environment[J]. IEEE Proceedings,2000,88(2):170-195.
[37]Shirmohammadi D, Wollenberg B, Vojdani A et al. Transmission dispatch and congestion management in the emerging energy market structures[J]. IEEE Transactions on Power Systems,1998,13(4):1466-1474.
[38]Christie R D, Wangensteen I. The energy market in Norway and Sweden:congestion management[J]. IEEE Trans on Power Engineering Review,1998,18(5):61-63.
[39]de Vries L J. Capacity allocation in a restructured electricity market:technical and economic evaluation of congestion management methods on interconnectors[DB]. Power Tech Proceedings,2001 IEEE Porto,vol.1,6 pp.
[40]Oren S,Spiller P T,Varaiya P et al. Nodal prices and transmission rights:a critical appraisal[A]. In:Working Paper of Program on Workable Energy Regulation[C]. Berkeley:1996.
[41]Oren S,Varaiya P,Wu F. Folk theorems on transmission open access:proofs and counterexamples[R]. Power Report PWP-23, University of California Energy Institute,1994.
[42]Rau N S. Transmission loss and congestion cost allocation-an approach based on responsibility[J]. IEEE Transactions on Power Systems,2000,15(4):579-585.
[43]李帆,朱敏.英国电力市场及输电系统简介[J]. 电力系统自动化.1999,23(2)：33-40.
[44]Singh H, Hao S, Papalexopoulos A. Transmission congestion management in competitive electricity markets[J]. IEEE Transactions on Power Systems,1998,13(2):672-680.
[45]Baran M E,Banunarayanan V,Garren K E. Equitable allocation of congestion relief cost to trans mission[J]-IEEE Transactions on Power Systems,2000,15(2):579-585.
[46]张永平,焦连伟,陈寿孙,严正,文福拴,倪以信,吴复立.电力市场阻塞管理综述[J].电网技术,2003,08：1-9+32.
[47]Schweppe F C,Caramanis M C,Tabors R D et al. Spot price of electricity[M]. Kluwer Academic Publishers,1988.
[48]WilliamH. Contract networks for electric power transmission[J]. Journal of Regulatory Economics,1992,4(9):211-242.
[49]David A K. Dispatch methodologies for open access transmission systems[J]. IEEE Trans on Power Systems,1998,13(l):46-53.
[50]Lei J,Deng Y,Zhang R. Congestion management for generation scheduling in a deregulated Chinese power system[A]. Power Engineering Society Winter Meeting,IEEE[C],2001,3:1262-1265.
[51]王秀丽,甘志,雷兵,等(Wang Xiuli,Gan Zhi,Lei Bing et al).输电阻塞管理的灵敏度分析模型及算法(Sensitivity analysis approach of transmission congestion management) [J].电力系统自动化(Automation of Electric Power Systems),2002,26(4):10-13.
[52]Fang R S, David A K. Optimal dispatch under transmission contracts[J]. IEEE Trans on Power Systems,1999,14(2):732-737.
[53]Losi A. Trade curtailment schemes for the security control of the transmission network in a deregulated environment[J]. International Journal of Electrical Power & Energy Systems,2002,24(1):9-17.
[54]Galiana F D,Ilic M. A mathematical framework for the analysis and management of power transactions under open access[J]. IEEE Transactions on Power Systems,1998,13(2):681-687.
[55]Galiana F D,Kockar I, Franco P C. Combined pool/bilateral dispatch-Part Ⅰ: performance of trading strategies[J]. IEEE Transactions on Power Systems,2002,17(11):92-99.
[56]Wang X,Song Y H,Lu Q. Primal-dual interior point linear programming optimal power flow for real-time congestion management A]. Power Engineering Society Winter Meeting,IEEE,2000,3:1643-1649.
[57]Wang Xing,Song Yonghua,Lu Qiang. A coordinated real-time optimal dispatch method for unbundled electricity markets[J]. IEEE Transactions on Power Systems,2002,17(2):482-490.
[58]GehLringKL. Canyesteulay demand-side management lessons become tomorrow's market solutions? The Electricity Journal,2002;15(5):63-69
[59]Murat Fahrioglu, FemandoL. Alvarado. Designing incentive compatible contracts for effective demand management. IEEE Trans. on Power Systems,2000; 15(4):1255-1260
[60]Walid EI-Khattam, Kankar Bhattacharya, Hegazy Y, Salama M. M. A. Optimal investment Planning for distribute degeneration in a competitive electricity market. IEEE Trans, on power Systems,2004;19(3):1674-1684
[61]Mamisa Pipattanasompom, Miehael Willingham, Saifur Rahman. Implieationofon site distribute degeneration for commercial/industrial facilities. IEEE Transactions on Power Systems,2005:20(1):206-212
[62]Walid EI-Khattam, Hegazy Y. Q Salama M. M. A. Investigating distributed generation systems Performance using Monte Carlo simulation. IEEE Trans, n power Systems,2006:21(2):524-532
[63]Sehweppe F C, Caramanis MC, Tabors R D, Bohn RE. Spot Price of electricity [M]. Kluwer Academic Publishers,1988
[64]汤振七,于尔铿,唐国庆.电力市场输电阻塞管理[J].电力系统自动化,2001,25(23)：13-16
[65]贺辉,徐政,黄莹.负荷在阻塞管理中的作用[J].电力系统自动化,2003,27(17)：11-14
[66]凌永伟,辛洁晴,言茂松.基于输电当量电价的直接阻塞管理[J].电网技术,.2003,27(7)：1 9-24
[67]赵晋泉,侯志俭,吴际舜.电力市场中的交易模式和传输阻塞管理J[].电力系统自动化.1999,23(20)：5-8
[68]Hogan W. Contraet networks for electric Power transmission[J]. Journal of Regulatory Economics,1992,4(9):211-242
[69]Baran ME,Banunaryranan V, Garren KE. Equitable allocation of congestion relief cost to transactions [J]. IEEE Transactions on Power Systems.2000, 15(2):579-585
[7C]Fradi A, Brignone S, Wollenberg BE. Calculation of energy transaction allocation factors [J]. IEEE Transactions on Power Systems,2001,16(2):266-272
[71]Baran ME,Banunarryanan V, GarrenKE, A transaction assessment method of allocation of transmission services [J]. IEEE Transactions on Power Systems, 1999,14(3):920-928
[72]P. J. Mago,A. K. Hueffed, Evaluation of a turbine driven CCHP system for large office buildings under different operating strategies[J]. Energy and Buildings 42 (2010) 1628-1636.
[73]P. J. Mago, L. M. Chamra, Analysis and optimization of CCHP systems based on energy, economical, and environmental considerations[J]. Energy and Buildings 41 (2009) 1099-1106.
[74]J. Wang, Y. Jing, C. Zhang, Z. Zhai, Performance comparison of combined cooling heating and power system in different operation modes[J]. Applied Energy 88 (2011) 4621-4631.
[75]N. Fumo, L.M. Chamra. Analysis of combined cooling, heating, and power systems based on source primary energy consumption[J]. Applied Energy 87 (2011)2023-2030.
[76]D. Maraver, A. Sin, F. Sebastian, J. Royo, Environmental assessment of CCHP (combined cooling heating and power) systems based on biomass combustion in comparison to conventional generation[J]. Energy 57 (2013) 17-23.
[77]X. Q. Kong, R. Z. Wang, X. H. Huang, Energy optimization model for a CCHP system with available gas turbines[J]. Applied Thermal Engineering 25 (2005)377-391.
[78]B.Zhang, W. Long, An optimal sizing method for cogeneration plants[J]. Energy and Buildings 38 (2006) 189-195.
[79]C.Z. Li, Y.M. Shi, X.H. Huang, Sensitivity analysis of energy demands on performance of CCHP system[J]. Energy Conversion and Management 49 (2008) 3491-3497.
[80]H.Li, R.Nalim, P.A. Haldi. Thermal-economic optimization of a distributed multi-generation energy system-A case study of Beijing[J]. Applied Thermal Engineering 26 (2006) 709-719.
[81]Y. Jing, H. Bai, J. Wang. Multi-objective optimization design and operation strategy analysis of BCHP system based on life cycle assessment[J]. Energy 37 (2012) 405-416.
[82]J. Wang, Y. Jing, C. Zhang, J. Zhao, Review on multi-criteria decision analysis aid in sustainable energy decision-making[J]. Renewable and Sustainable Energy Reviews 13 (2009) 2263-2278.
[83]H. Ren, W. Zhou, K. Nakagami, W. Gao, Q. Wu, Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects[J]. Applied Energy 87 (2010) 3642-3651.
[84]B. Jabbari, N.Tahouni, A. Ataei, M.H. Panjeshahi, Design and optimization of CCHP system incorporated into kraft process, using pinch analysis with pressure drop consideration[J]. Applied Thermal Engineering in press (2013)1-10.
[85]Gh.Abdollahia, M. Meratizamanb. Multi-objective approach in thermoenvironomic optimization of a small-scale distributed CCHP system with risk analysis[J]. Energy and Buildings 43 (2011) 3144-3153.
[86]J. Wang, Y. Jing, C. Zhang, Optimization of capacity and operation for CCHP system by genetic algorithm[J]. Applied Energy 87 (2010) 1325-1335.
[87]J. Wu, J. Wang, S. Li, Multi-objective optimal operation strategy study of micro-CCHP system[J]. Energy 48 (2012) 472-483.
[88]E. Bernier, F. Mare'chal, R. Samson, Multi-objective design optimization of a natural gas-combined cycle with carbon dioxide capture in a life cycle perspective[J]. Energy 35 (2010) 1121-1128.
[89]H. Sayyaadi G. Abdollahi, Application of the multi-objective optimization and risk analysis for the sizing of a residential small-scale CCHP system[J]. Energy and Buildings 60 (2013) 330-344.
[90]K.C. Kavvadias, Z.B. Maroulis, Theproblem is solved using a multi-objective evolutionary algorithm[J]. Energy Policy38(2010)945-954.
[91]Subcommittee, L.. Spot-Price Marketing of Surplus Energy By the Sharing of Interruptible Loads In A Regional Power Pool[J]. IEEE Transaetions on Power Apparatus and Systems,1985, PAS-104(2):328-336.
[92]Peng Wang,Yong Liu,Goel L.,Quiwei Wu,Yi Ding,WenPing Qin. A reliability-based reserve criterion for market settlement within terruptible loads [C]. Proeeedings-2009 IEEE Power & Energy Society General Meeting,2009,1:514-517.
[93]Bai,J.,Gooi, H.B., Xia, L.M., Strbae, G., Venkatesh,B.. A Probabilistic Reserve Market Incorporating Interruptible Load[C]. Proceedings-2007 IEEE Power Engineering Soeiety General Meeting,2007:1-1
[94]Chowdhury,N., Billinton,R.. Interruptible load considerations in spinning reserve assessment of isolated and interconnected generating systems[J]. IEE Proceedings of Generation,Transmission and Distribution,1990,137(2):159-167.
[95]Kun-Yuan Huang,Yann-Chang Huang. Integrating direct load control with interruptible load management to Provide instantaneous reserves for aneillary services[J]. IEEE Transactions on Power Systems,2004,19(3):1626-1634.
[96]Le Anh Tuan,Kankar Bhattacharya,Jaap Daalder. Transmission congestion management in bilateral markets:An interruptible load auction solution[J]. Electric Power systems Researeh,2005,74(3):379-389.
[97]Raikar, S.,Ilic,M.. Interruptible Physcal transmission contracts for congestion management[C]. IEEE 2001 Power Engineering Society Summer Meeting,2001,3:1639-164.
[98]国家电网公司电力需求侧管理指导中心.美国节能服务行业回顾[M].2005
[99]Gehring Kay L. Can yesterday's demand-side management lessons become tomorrow's market solutions[J]. The Electricity Journal,2002,15(5)
[100]Raymond F. Ghajar, Roy Billinton. Economic costs of power interruptions:a consistent model and methodology[J]. Electrical Power and Energy Systems, 2006,28.
[101]Le Anh Tuan, Kankar Bhattacharya. Competitive Framework for Procurement of Interruptible Load Services[J]. IEEE Transactions on Power Systems,Vol.18, No.2, May 2003.
[102]C. N. Kurucz D. Brandt S. Sim. A Linear Programming Model for Reducing System Peak Through Customer Load Control Programs[J]. IEEE Transactions on Power Systems, Vol.11, No.4,1996.
[103]Kun-Yuan Huang and Yann-Chang Huang. Integrating Direct Load Control With Interruptible Load Management to Provide Instantaneous Reserves for Ancillary Services[J], IEEE TRANSACTIONS ON POWER SYSTEMS, VOL.19, NO.3,2004.
[104]黄海涛,张粒子,乔慧婷,杜宁.基于变密度聚类的居民阶梯分段电量制定方法[J].电网技术,2010,34(11)：111-116.
[105]黄海涛.居民阶梯电价结构与水平设计的模型研究[J].华东电力,2012,40(5)：721-727.
[106]朱柯丁,宋艺航,谭忠富,吴海林.居民生活阶梯电价设计优化模型[J].华东电力,2011,39(6)：862-867.
[107]李媛,罗琴,宋依群,徐剑,蔡磊,顾俊.基于模糊响应的阶梯电价机制研究[J].华东电力,2012,40(3)：367-372.
[108]李媛,罗琴,宋依群,等.基于需求响应的居民分时阶梯电价档位制定方法研究[J].电力系统保护与控制,2012,40(18)：65-68.
[109]黄海涛.居民分时阶梯电价联合优化模型研究[J].电网技术,2012,36(10)：253-258.
[110]曾明,李娜,刘超.基于效用函数的居民阶梯电价方案的节电效果评估[J].华东电力,2011,39(8)：1215-1219.
[111]吴建宏,曾明,刘宏志,刘超,周飞.基于灰色关联度和理想解法的居民阶梯电价实施效果评价[J].水电能源科学,2012,30(12)：187-190.
[112]张勃.北京地区光伏系统发电功率预测的研究[D].燕山大学,2013.
[113]吴宏宇,管晓宏,翟桥柱,高峰.水火电联合短期调度的混合整数规划方法[J].中国电机工程学报,2009,28：82-88.
[114]陈启鑫,康重庆,程旭东,綦海昌,马志波.考虑阻塞管理的发电权交易模型及其网络流算法[J].中国电机工程学报,2008,34：106-111.
[115]赵晋泉,叶君玲,邓勇.直流潮流与交流潮流的对比分析[J].电网技术,2012,10：147-152.
[116]张伯明,陈寿孙,严正.高等电力网络分析[M].北京：清华大学出版社,2007：191-193.
[117]高志华,任震,黄福全,潘锡芒.输电阻塞中的多机组联合竞标策略[J].电网技术,2004,05：55-58+76.
[118]杨朋朋,韩学山,查浩.一种计及静态安全约束机组组合的有效算法[J].电力系统自动化,2009,33(8)：39-43.
[119]江健健,夏清,沈瑜,康重庆,许洪强,张元鹏.基于边际电价的短期机组组合求解新方法[J].清华大学学报(自然科学版),2001,Z1：151-154.
[120]Li Fangxing,Bo Rui. DCOPF-based LMP simulation:algorithm,comparison with ACOPF,and sensitivity[J]. IEEE Transactions onPower Systems,007,22(4): 1475-1485.
[121]朱民茂.电力市场下基于灵敏度因子的超短期阻塞管理[D].湖南大学,2007.
[122]吴政球,朱民茂,王良缘,方日升,叶世顺.超短期在线输电阻塞管理模型及算法[J].电力系统及其自动化学报,2007,06：109-113.
[123]王秀丽,甘志,雷兵,王锡凡.输电阻塞管理的灵敏度分析模型及算法[J].电力系统自动化,2002,04：10-13+22.
[124]杨洪明,段献忠.双边交易模式下基于Aumann-Shapley值的阻塞费用分摊方法研究[J].中国电机工程学报,2002,11：60-64.
[125]刘岩,于渤,洪富艳.可再生能源价值构成与定价模型研究[J].预测,2011,01：61-65.
[126]高彦辉.电力市场阻塞管理中的电价[J].电网技术,2008,S2：223-225.
[127]毛伟明,周明,李庚银.多时段下计及可中断负荷的电网输电阻塞管理[J].电网技术,2008,04：72-77.
[128]申晓军.城镇居民生活用电需求弹性分析[D].浙江大学,2011.
[129]Faisal Jamil, Eatzaz Ahmad. Income and price elasticities of electricity demand: Aggregate and Sector-wise analyses[J]. Energy policy,2011,39:5519-5527.
[130]林伯强.争议阶梯电价[J].中国电力企业管理,2010,(27)：20-22.
[131]成邦文.基于对数正态分布的洛伦兹曲线与基尼系数[J].数量经济技术经济研究,2005,(2)：127-135.
[132]丁世龙,张金隆.采用差别定价方法制定居民电价的研究[J].价格理论与实践,2006,(7)：23-24.