中长期差价合同分解及相关问题的研究
|
摘要
鉴于日前市场价格的不确定性,为降低市场风险,国内多个市场引入差价合同进行风险控制,合同交易量往往占当年市场需求的80%以上。发电厂商与购电公司签订的差价合同一般为电量合同,如何将合同交易总量以一定的方式进行分解,兼顾各方利益并促进市场逐步健康发展,是一个具有重要意义且值得研究的问题。在此方面,本文做了如下主要工作:
电力市场的合同分解中应用确定性合同分解算法需要制定典型负荷曲线,历史负荷中的异常数据必然影响典型负荷曲线的有效性。提出了一种新的负荷形状畸变识别方法,并将其与传统的t检验法相结合,应用于负荷异常数据的识别和修正。最后选用浙江电网的历史数据,应用上述方法进行了异常负荷识别和修正,分析结果说明该方法简单、有效。
确定性合同分解算法是一种直观、简单的分解方法,但相关实证性研究较少。提出平均合同电量比例、平均合同电量比例和电价相关性两项公平性评估指标,采用期望、标准差和半标准差评估年度基数合同对电厂的收益、风险的影响。选用华东市场实际数据,分析结果表明在二次华东市场调电试运行中确定性合同分解算法公平地对待发购电双方,电厂签订年度基数合同合同可有效地降低其风险。
从合同博弈角度出发,考察单位电量的净收益计算单一购买者和发电厂商的效用,分析了单一购买者和发电厂商愿意参与确定性合同分解形成联盟的理论条件。当仅有一个发电厂商时,若双方均为风险厌恶型则联盟稳定,双方均为风险喜好型则联盟不稳定;当有一个购电公司、多个发电厂商时,需满足多个不等式条件时联盟稳定。选用国内某省级市场实际数据进行仿真分析,分析结果表明模型合理有效。
传统差价合同分解是以负荷预测值为基础进行,负荷的不确定性可能导致分解结果无法满足预期目标,因此在合同分解时有必要考虑负荷的不确定性。选用广义自回归条件异方差类模型建立随机负荷模型,以合同电量比例与预定比例之差的标准差最小化为目标,以实际标准差小于预期标准差的概率为机会约束,构造了最优差价合同分解的随机规划模型。采用基于蒙特卡洛模拟的遗传算法求解,算例结果表明所提出的模型和方法是有效的。
传统的中长期差价合同分解都是统一分解,发电商被动接受。提出了“发电商申报法”分解差价合同电量,在考虑检修、出力约束、系统可分解合同电量和系统必需合同电量等因素后,采用基于误差的差异度评估函数建立多目标优化模型,并分别采用“线性加权求和法”和“极小极大值法”将原模型转换为单目标优化。采用国内市场的实际数据构造算例,计算结果表明模型有效,两种转换均可获得原问题的有效解,采用“极小极大值法”时各发电商调整量的差异较小。
Considering the uncertainty of the day-ahead market price, in order to reduce the market risk, CfD (Contract for Difference) is introduced to hedge against market risk in some domestic markets. Usually, contract energy accounts for more than 80% of total energy. Generally, CfD that generators and buyers signed are energy contracts, how to decompose the total contract engergy while covering all participants' benefit, is an issue which is important and worth studying. The contributions are summarized as follows:
In order to apply the deterministic contract decomposition algorithm to decompose contract energy, typical load curve must be prepared. Abnormal historical load data affect the validity of typical load curve. The notion of D_(ht) is proposed, it is applied to identify the abnormal load data, and combined with t test to identify and correct the load outlier. The historical data of Zhejiang province is used to test the suggested approach, the result indicates that the algorithm is simple and effective.
An analysis on the deterministic contract decomposition algorithm of East China electricity market is presented. Average contract quantity proportion, correlation of contract quantity proportion and price have been adopted to evaluate the equity of the algorithm. Expectation, standard deviation and half standard deviation have been adopted to evaluate the income and risks of generators. An empirical analysis is done based on the historical data of East China electricity market. The result indicates that the algorithm treats buyer and generators fairly in the trial operation, CfD contracts help to reduce the generator's risk effectively.
From the viewpoint of cooperative game, per unit net revenue is adopted to evaluate single buyer and generators' utility, the conditions under which the generators and buyers are willing to participate in contract decomposition is derived. In a single-buyer-single-supplier market, if both parties are risk-averse, the coalition is always stable; if both parties are risk-adventurous, the coalition is always unstable. In a single-buyer-multi-supplier market, the coalition is conditionally stable. A numerical study based on the historical data of some China provincial electricity market is performed. The result indicates that the proposed model is reasonable and effective.
Traditional decomposition of CfD decomposition is based on the forecasted load, the load uncertainty may lead to that decomposition result can't satisfy the expected target, so considering load uncertain is necessary when decomposing contract. A stochastic load model based on the GARCH (Generalize Autoregressive Conditional Heteroscedasticity) model is suggested for contract decomposition. Taking the minimized standard deviation of the difference between contract ratio and appointed ratio as the objective, the probability that actual standard deviation is smaller than the expected standard deviation as a chance constraint, an optimal contract decomposition based on chance constrained programming is proposed. A genetic algorithm based on Monte Carlo simulation is used to solve the problem, the numerical result indicates that the model and method is effective.
Traditional contract decomposition method is based on such idea: "contracts are decomposed in a centralized way;; generation companies accept the result passively". A new contract decomposition method named "generation company declaration method" is presented. The maintenance, the power capacity of units, etc. are taken into account. A difference evaluation function based on errors is adopted to build a multiple objective optimization model, "linear weighted sum" and minimax method are respectively selected to convert the original problem into a single objective optimization. A case study of a domestic market is performed, the results indicate that the suggested model is effective, the solution of the original problem can be obtained by both conversion methods. When minimax method is selected, the difference in generation companies' adjustment quantity is smaller.
电力市场的合同分解中应用确定性合同分解算法需要制定典型负荷曲线,历史负荷中的异常数据必然影响典型负荷曲线的有效性。提出了一种新的负荷形状畸变识别方法,并将其与传统的t检验法相结合,应用于负荷异常数据的识别和修正。最后选用浙江电网的历史数据,应用上述方法进行了异常负荷识别和修正,分析结果说明该方法简单、有效。
确定性合同分解算法是一种直观、简单的分解方法,但相关实证性研究较少。提出平均合同电量比例、平均合同电量比例和电价相关性两项公平性评估指标,采用期望、标准差和半标准差评估年度基数合同对电厂的收益、风险的影响。选用华东市场实际数据,分析结果表明在二次华东市场调电试运行中确定性合同分解算法公平地对待发购电双方,电厂签订年度基数合同合同可有效地降低其风险。
从合同博弈角度出发,考察单位电量的净收益计算单一购买者和发电厂商的效用,分析了单一购买者和发电厂商愿意参与确定性合同分解形成联盟的理论条件。当仅有一个发电厂商时,若双方均为风险厌恶型则联盟稳定,双方均为风险喜好型则联盟不稳定;当有一个购电公司、多个发电厂商时,需满足多个不等式条件时联盟稳定。选用国内某省级市场实际数据进行仿真分析,分析结果表明模型合理有效。
传统差价合同分解是以负荷预测值为基础进行,负荷的不确定性可能导致分解结果无法满足预期目标,因此在合同分解时有必要考虑负荷的不确定性。选用广义自回归条件异方差类模型建立随机负荷模型,以合同电量比例与预定比例之差的标准差最小化为目标,以实际标准差小于预期标准差的概率为机会约束,构造了最优差价合同分解的随机规划模型。采用基于蒙特卡洛模拟的遗传算法求解,算例结果表明所提出的模型和方法是有效的。
传统的中长期差价合同分解都是统一分解,发电商被动接受。提出了“发电商申报法”分解差价合同电量,在考虑检修、出力约束、系统可分解合同电量和系统必需合同电量等因素后,采用基于误差的差异度评估函数建立多目标优化模型,并分别采用“线性加权求和法”和“极小极大值法”将原模型转换为单目标优化。采用国内市场的实际数据构造算例,计算结果表明模型有效,两种转换均可获得原问题的有效解,采用“极小极大值法”时各发电商调整量的差异较小。
Considering the uncertainty of the day-ahead market price, in order to reduce the market risk, CfD (Contract for Difference) is introduced to hedge against market risk in some domestic markets. Usually, contract energy accounts for more than 80% of total energy. Generally, CfD that generators and buyers signed are energy contracts, how to decompose the total contract engergy while covering all participants' benefit, is an issue which is important and worth studying. The contributions are summarized as follows:
In order to apply the deterministic contract decomposition algorithm to decompose contract energy, typical load curve must be prepared. Abnormal historical load data affect the validity of typical load curve. The notion of D_(ht) is proposed, it is applied to identify the abnormal load data, and combined with t test to identify and correct the load outlier. The historical data of Zhejiang province is used to test the suggested approach, the result indicates that the algorithm is simple and effective.
An analysis on the deterministic contract decomposition algorithm of East China electricity market is presented. Average contract quantity proportion, correlation of contract quantity proportion and price have been adopted to evaluate the equity of the algorithm. Expectation, standard deviation and half standard deviation have been adopted to evaluate the income and risks of generators. An empirical analysis is done based on the historical data of East China electricity market. The result indicates that the algorithm treats buyer and generators fairly in the trial operation, CfD contracts help to reduce the generator's risk effectively.
From the viewpoint of cooperative game, per unit net revenue is adopted to evaluate single buyer and generators' utility, the conditions under which the generators and buyers are willing to participate in contract decomposition is derived. In a single-buyer-single-supplier market, if both parties are risk-averse, the coalition is always stable; if both parties are risk-adventurous, the coalition is always unstable. In a single-buyer-multi-supplier market, the coalition is conditionally stable. A numerical study based on the historical data of some China provincial electricity market is performed. The result indicates that the proposed model is reasonable and effective.
Traditional decomposition of CfD decomposition is based on the forecasted load, the load uncertainty may lead to that decomposition result can't satisfy the expected target, so considering load uncertain is necessary when decomposing contract. A stochastic load model based on the GARCH (Generalize Autoregressive Conditional Heteroscedasticity) model is suggested for contract decomposition. Taking the minimized standard deviation of the difference between contract ratio and appointed ratio as the objective, the probability that actual standard deviation is smaller than the expected standard deviation as a chance constraint, an optimal contract decomposition based on chance constrained programming is proposed. A genetic algorithm based on Monte Carlo simulation is used to solve the problem, the numerical result indicates that the model and method is effective.
Traditional contract decomposition method is based on such idea: "contracts are decomposed in a centralized way;; generation companies accept the result passively". A new contract decomposition method named "generation company declaration method" is presented. The maintenance, the power capacity of units, etc. are taken into account. A difference evaluation function based on errors is adopted to build a multiple objective optimization model, "linear weighted sum" and minimax method are respectively selected to convert the original problem into a single objective optimization. A case study of a domestic market is performed, the results indicate that the suggested model is effective, the solution of the original problem can be obtained by both conversion methods. When minimax method is selected, the difference in generation companies' adjustment quantity is smaller.
引文
[1]王锡凡,王秀丽,陈皓勇.电力市场基础[M].西安:西安交通大学出版社,2007.
[2]杜松怀.电力市场[M].第3版.北京:中国电力出版社,2008.
[3]国家电力监管委员会.欧洲澳洲电力市场[MI.北京:中国电力出版社,2005.
[4]国家电力监管委员会.美国电力市场[M].北京:中国电力出版社,2005.
[5]Asano H.Regulatory reform of the electricity industry in Japan:What is the next step of deregulation?[J].Energy Policy.2006,34(16):2491-2497.
[6]栾凤奎,贾俊国,韩英豪,等.日本电力工业改革及其对我国电力市场建设的借鉴[J].华东电力.2006,34(9):6-9.
[7]阙光辉.日本电力市场化改革最新进展及启示[J].电力技术经济.2007,19(3):9-13.
[8]井志忠.韩国电力市场化改革探析[J].东北亚论坛.2006,15(3):88-92.
[9]傅胤荣,胡义华,潘永雄.韩国电力市场改革及其启示[J].华东电力.2007,35(1):66-68.
[10]Mukhopadhyay S,Dube S K.Status of power exchange in India:trading,scheduling,and real time operation of regional grids[C].Power Engineering Society General Meeting,2005.IEEE.
[11]马莉,魏玢,阙光辉,等.印度电力市场化改革情况及最新进展[J].中国电力.2007,40(6):71-73.
[12]李韩房,谭忠富.印度电力市场化改革概况[J].华东电力.2007,35(7):91-94.
[13]齐放,张粒子.印度电力市场化改革的新进展[J].电力需求侧管理.2008,10(2):63-65.
[14]李琼.阿根廷电力市场与监管实践[J].中国电力企业管理.2005(4):21-23.
[15]国家电力监管委员会.南美洲、亚洲、非洲各国电力市场化改革[M].北京:中国电力出版社,2005.
[16]Erdogan F H,Cetinkaya S,Dusmez T E.Market liberalization process and market arrangements in Turkey[C].European Electricity Market,2008.EEM 2008. 5th International Conference on.
[17]Musaba L.The development of the SAPP competitive electricity market[C].Power Engineering Society Inaugural Conference and Exposition in Africa,2005IEEE.
[18]王秀丽,宋永华,王锡凡.英国电力市场新模式一结构、成效及问题[J].中国电力.2003,36(6):1-5.
[19]王家伟.英国电力市场化改革之路[J].现代电力.2004,21(3):1-5.
[20]张滇生.北欧电力交易市场与电力市场运行[J].中国电业.2004(11):70-73.
[21]何永秀.北欧电力库的设计与实施[J].国际电力.2004,8(2):4-8.
[22]孙建平,戴铁潮.北欧电力市场发展概况[J].华东电力.2006,34(12):60-65.
[23]李博,朱元成.北欧电力市场辅助服务[J].中国电力.2008,41(4):88-92.
[24]Kelly S.Australian electricity market[J].IEEE Power Engineering Review.2002,22(5):4-7.
[25]李昕,涂光瑜.澳大利亚国家电力市场简介[J].电网技术.2000,24(8):73-76.
[26]曾次玲,张步涵,谢培元.澳大利亚国家电力市场的最新发展状况[J].电网技术.2004,28(24):74-80.
[27]胡朝阳,韩祯祥,童建中.美国东部PJM电力市场运营方式论述[J].国际电力.2002,6(4):18-23.
[28]魏玢.美国PJM电力市场及其对我国电力市场化改革的启示[J].电力系统自动化.2003,27(8):32-35.
[29]鲁顺,李灿,魏庆海,等.纽约电力市场探析[J].电网技术.2004,28(17):62-6671.
[30]葛炬,张粒子.美国得州ERCOT电力市场及其对我国电力改革的启示[J].中国电力.2004,37(12):13-17.
[31]程志欣.电力市场合同交易风险建模[D].华北电力大学(北京),2007.
[32]国家电力监管委员会.中国电力市场名词解释[M].北京:中国电力出版社,2005.
[33]国家电网公司营销部.电力市场体系建设资料选编[z].2006.
[34]张少华,李渝曾,王长军,等.电力市场中的远期合同交易[J].电力系统自 动化.2001,25(10):6-1050.
[35]张显,王锡凡,陈皓勇,等.电力市场中的双边合同[J].电力自动化设备.2003,23(11):77-86.
[36]刘广一,于尔铿,宋永华,等.合同的基本类型及其在电力市场中的作用[J].中国电力.1999,32(3):64-67.
[37]王长军,张少华,李渝曾,等.考虑差价合同交易的电力市场均衡模型[J].上海大学学报(自然科学版).2001,7(4):349-352.
[38]王建斌,谭忠富.远期合同对发电商市场力影响的博弈均衡模型[J].现代电力.2007,24(4):80-83.
[39]刘军虎,陈皓勇,张显.电力市场远期合同交易的实验分析[J].经济经纬.2006(6):26-29.
[40]袁智强,侯志俭,宋依群,等.考虑远期合约的不同投标策略供应函数均衡分析[J].上海交通大学学报.2004,38(8):1241-1245.
[41]任玉珑,唐松林,刘贞,等.基于Swarm的发电侧电力市场结构研究[J].统计与决策.2007(21):97-100.
[42]汤玉东,吴军基,邹云.Powerpool中发电公司竞价问题的微观经济学分析[J].电力系统自动化.2003,27(15):15-1924.
[43]刘晓东,戴士杰.浅析发电侧市场中的市场力[J].河北工业大学学报.2004,33(6):67-71.
[44]李晓刚,李玉平.统一出清电价下发电厂商的持留容量行为及相关抑制措施[J].电力自动化设备.2004,24(7):85-88.
[45]蔺利.基于博弈论分析合同在降低市场力方面的作用[J].商业研究.2005(6):83-86.
[46]吴宁,祖博,吴军基.演化博弈论在发电侧电力市场竟价中的应用[J].华东电力.2006,34(3):35-38.
[47]李明,李艳芳.考虑风险的发电厂竞价上网模型的研究[J].青海大学学报(自然科学版).2008,26(2):63-67.
[48]贾燕冰,严正.合同对动态电力市场稳定性的影响[J].电力系统自动化.2007,31(9):16-2071.
[49]戴铁潮,张丹.确定性合约电量分解算法在浙江发电市场的应用[J].华东电力.2000,28(10):7-959.
[50]赵学顺,戴铁潮,黄民翔.电力市场中风险规避问题的研究(二)--差价合约分析系统的实现[J].电力系统自动化.2001,25(8):16-19.
[51]余志文,郭志忠,于尔铿,等.发电侧电力市场中合同电量的一种实现方法[J].中国电力.2000,33(11):76-78.
[52]李其智,任震,余志文.基于合同电量滚动分解算法的合同管理系统[J].广东电力.2002,15(3):76-7881.
[53]汪峰,白晓民.面向电力市场交易计划算法研究[J].电网技术.2001,25(8):45-49.
[54]王漪,于继来,柳焯.基于月度竞价空间滚动均衡化的年中标电量分解[J].电力系统自动化.2006,30(17):24-2764.
[55]黎灿兵,胡亚杰,赵弘俊,等.合约电量分解通用模型与算法[J].电力系统自动化.2007,31(11):26-30.
[56]赵宇,全宏兴.电力市场过渡期水火电中长期合同计划[J].水电自动化与大坝监测.2007,31(3):6-9.
[57]张力,刘俊勇,刘继春,等.火电机组年合同电量的编制与分解算法研究[J].继电器.2007,35(4):64-69.
[58]温丽丽,刘俊勇,吴志云,等.基于月度滚动修正的合同电量分解算法与应用[J].现代电力.2008,25(1):82-87.
[59]姜生斌,纪昌明,王蕊,等.水电站年度合同电量的月度分解研究[J].人民黄河.2008(06).
[60]王雁凌,张粒子,舒隽,等.合同交易量和竞价交易量在日计划中的经济分配[J].电力系统自动化.2002,26(9):45-48.
[61]蒋东荣,刘学军,李群湛.电力市场环境下电网日发电计划的电量经济分配策略[J].中国电机工程学报.2004,24(7):90-94.
[62]吕泉,武亚光,王海霞,等.制定电力市场中机组合同交易电量日出力计划的一种经济策[J].电网技术.2006,30(14):71-76.
[63]封嘉爱.日前交易开放前日调度计划优化的研究[D].哈尔滨:哈尔滨工业 大学,2006.
[64]严亚男.水电参与南方区域电力市场交易的若干问题研究[D].昆明:昆明理工大学,2006.
[65]初壮.初期电力市场有功优化调度模型与算法研究[D].哈尔滨:哈尔滨工业大学,2007.
[66]陈亚红,穆钢,段方丽.短期电力负荷预报中几种异常数据的处理[J].东北电力学院学报.2002,22(2):1-5.
[67]田增尧,张明理,赵瑞.短期电力负荷预报中异常负荷数据的识别和修正[J].吉林电力.2004(6):21-23.
[68]张国江,邱家驹,李继红.基于人工神经网络的电力负荷坏数据辨识与调整[J].中国电机工程学报.2001,21(8):104-107113.
[69]张晓星,程其云,周湶,等.基于数据挖掘的电力负荷脏数据动态智能清洗[J].电力系统自动化.2005,29(8):60-64.
[70]叶锋,何桦,顾全,等.EMS中负荷预测不良数据的辨识与修正[J].电力系统自动化.2006,30(15):85-88.
[71]莫维仁,张伯明,孙宏斌,等.扩展短期负荷预测方法的应用[J].电网技术.2003,27(5):6-9.
[72]康重庆夏清,相年德.灰色系统参数估计与不良数据辨识[J].清华大学学报(自然科学版).1997(04).
[73]高山,单渊达.小波奇异性检测在负荷数据纠错和平滑处理中的应用[J].中国电机工程学报.2001,21(11):105-108113.
[74]任丽娜,芮执元,刘彦新,等.异常电力负荷数据的辨识方法研究[J].水力发电.2008,34(2):43-45.
[75]胡上序,陈德钊.观测数据的分析与处理[M].杭州:浙江大学出版社,1996.
[76]赵学顺,黄民翔,韩祯祥.电力市场中风险规避问题的研究(一)--不同电力市场阶段风险规避模型[J].电力系统自动化.2001,25(7):14-20.
[77]Alcock D,Pang C K.Risk management in a power pool[C].Proceeding of the 5th International Conference on Advances in Power System Control,Operation and Management.Hong Kong,.
[78]陈晓林,刘俊勇,宋永华,等.利用差价合同和金融输电权的组合规避电力市场风险[J].中国电机工程学报.2005,25(10):75-81.
[79]Qi L,Huang W.The impact of contract for difference on the market power in the restructured zhejiang electricity market[C].Power Systems Conference and Exposition,2004.IEEE PES.
[80]国家电力监管委员会华东监管局.华东电力市场运营规则2007/日期[2007-8-31].http://www.ecerb.gov.cn/upload/1145865702328.pdf.
[81]谢恒,李胜利,李晶,等.电力市场中合同管理系统的设计与应用[J].广东电力.2008,21(4):43-4660.
[82]Jerry S R.A Case Study in Risk Management[M].New Jersey:Prentice Hall,1972.
[83]Charles w.smithson.Managing Financial Risk:A Guide to Derivative Products,Financial Engineering,and Value Maximization[M].第3版.北京:中国人民大学出版社,2003.
[84]Markowitz H.Portfolio selection:efficient diversification of investments[M].Cambridge:Blackwell Publishing Limited,1991.
[85]吴喜之,王兆军.非参数统计方法[M].北京:高等教育出版社,1996.
[86]Owen G.Game theory[M].San Diego:Academic Press Limited,1995.
[87]谢政.对策论[M].长沙:国防科技大学出版社,2004.
[88]朱宝宪.投资学[M].北京:清华大学出版社,2002.
[89]张宁,杨莉,戴铁潮,等.区域市场中电力公司报价辅助决策系统设计[J]_电力系统自动化.2007,31(2):46-50.
[90]刘宝碇,赵瑞清,王刚.不确定规划及应用[M].北京:清华大学出版社,2003.
[91]Breipohl A M,Lee F N,Zhai D,et al.Gauss-Markov Load Model for Application in Risk Evaluation and Production Simulation[J].IEEE Power Engineering Review.1992,12(11):47.
[92]Zhai D,Breipohl A M,Lee F N,et al.The effect of load uncertainty on unit commitment risk[J].IEEE Transactions on Power Systems.1994,9(1):510-517.
[93]Jia-yo C,Breipohl A M,Lee F N,et al.Estimating the variance of production cost using a stochastic load model[J].IEEE Transactions on Power Systems.2000,15(4):1212-1217.
[94]Sisworahardjo N S,El-keib A A,Choi J,et al.A stochastic load model for an electricity market[J].Electric Power Systems Research.2006,76(6-7):500-508.
[95]Qiu Y,Zhao J,Chiang H D.Effects of the stochastic load model on power system voltage stability based on bifurcation theory[C].Transmission and Distribution Conference and Exposition,2008.T&D.IEEE/PES.Chicago,.
[96]朱东晓,曹树华,赵磊,等.电力负荷预测技术及其应用[M].北京:中国电力出版社,2004.
[97]康重庆,夏清,刘梅.电力系统负荷预测[M].北京:中国电力出版社,2007.
[98]Christian G.ARCH Models and Financial Applications[M].New York:Springer-Verlag,1997.
[99]彭作祥.金融时间序列建模分析[M].成都:西南财经大学出版社,2006.
[100]Corm A R,Gould N I,Toint P L.A Globally Convergent Augmented Langrangian Algorithm for Optimization with General Constraints and Simple Bounds[J].SIAM Journal on Numerical Analysis.1991,28(2):545- 572.
[101]Conn A R,Gould N I,Toint P L.A Globally Convergent Lagrangian Barrier Algorithm for Optimization with General Inequality Constraints and Simple Bounds[J].Mathematics of Computation.1997,66(217):261-288.
[102]Kantardzic M.Data mining:concepts,models,methods,and algorithms[M].New York:Wiley-IEEE,2003.
[103]孙文瑜,袁亚湘.最优化理论与方法[M].北京:科学出版社,2005.
[104]徐玖平,李军.多目标决策的理论和方法[M].北京:清华大学出版社,2005.
[105]高岳林,徐成贤.解带有二次约束二次规划的一个整体优化方法[J].运筹学学报.2002,6(2):53-60.
[106]Hoai A L.An efficient algorithm for globally minimizing a quadratic function under convex quadratic constraints[J].2000,87(3):401-426.
[2]杜松怀.电力市场[M].第3版.北京:中国电力出版社,2008.
[3]国家电力监管委员会.欧洲澳洲电力市场[MI.北京:中国电力出版社,2005.
[4]国家电力监管委员会.美国电力市场[M].北京:中国电力出版社,2005.
[5]Asano H.Regulatory reform of the electricity industry in Japan:What is the next step of deregulation?[J].Energy Policy.2006,34(16):2491-2497.
[6]栾凤奎,贾俊国,韩英豪,等.日本电力工业改革及其对我国电力市场建设的借鉴[J].华东电力.2006,34(9):6-9.
[7]阙光辉.日本电力市场化改革最新进展及启示[J].电力技术经济.2007,19(3):9-13.
[8]井志忠.韩国电力市场化改革探析[J].东北亚论坛.2006,15(3):88-92.
[9]傅胤荣,胡义华,潘永雄.韩国电力市场改革及其启示[J].华东电力.2007,35(1):66-68.
[10]Mukhopadhyay S,Dube S K.Status of power exchange in India:trading,scheduling,and real time operation of regional grids[C].Power Engineering Society General Meeting,2005.IEEE.
[11]马莉,魏玢,阙光辉,等.印度电力市场化改革情况及最新进展[J].中国电力.2007,40(6):71-73.
[12]李韩房,谭忠富.印度电力市场化改革概况[J].华东电力.2007,35(7):91-94.
[13]齐放,张粒子.印度电力市场化改革的新进展[J].电力需求侧管理.2008,10(2):63-65.
[14]李琼.阿根廷电力市场与监管实践[J].中国电力企业管理.2005(4):21-23.
[15]国家电力监管委员会.南美洲、亚洲、非洲各国电力市场化改革[M].北京:中国电力出版社,2005.
[16]Erdogan F H,Cetinkaya S,Dusmez T E.Market liberalization process and market arrangements in Turkey[C].European Electricity Market,2008.EEM 2008. 5th International Conference on.
[17]Musaba L.The development of the SAPP competitive electricity market[C].Power Engineering Society Inaugural Conference and Exposition in Africa,2005IEEE.
[18]王秀丽,宋永华,王锡凡.英国电力市场新模式一结构、成效及问题[J].中国电力.2003,36(6):1-5.
[19]王家伟.英国电力市场化改革之路[J].现代电力.2004,21(3):1-5.
[20]张滇生.北欧电力交易市场与电力市场运行[J].中国电业.2004(11):70-73.
[21]何永秀.北欧电力库的设计与实施[J].国际电力.2004,8(2):4-8.
[22]孙建平,戴铁潮.北欧电力市场发展概况[J].华东电力.2006,34(12):60-65.
[23]李博,朱元成.北欧电力市场辅助服务[J].中国电力.2008,41(4):88-92.
[24]Kelly S.Australian electricity market[J].IEEE Power Engineering Review.2002,22(5):4-7.
[25]李昕,涂光瑜.澳大利亚国家电力市场简介[J].电网技术.2000,24(8):73-76.
[26]曾次玲,张步涵,谢培元.澳大利亚国家电力市场的最新发展状况[J].电网技术.2004,28(24):74-80.
[27]胡朝阳,韩祯祥,童建中.美国东部PJM电力市场运营方式论述[J].国际电力.2002,6(4):18-23.
[28]魏玢.美国PJM电力市场及其对我国电力市场化改革的启示[J].电力系统自动化.2003,27(8):32-35.
[29]鲁顺,李灿,魏庆海,等.纽约电力市场探析[J].电网技术.2004,28(17):62-6671.
[30]葛炬,张粒子.美国得州ERCOT电力市场及其对我国电力改革的启示[J].中国电力.2004,37(12):13-17.
[31]程志欣.电力市场合同交易风险建模[D].华北电力大学(北京),2007.
[32]国家电力监管委员会.中国电力市场名词解释[M].北京:中国电力出版社,2005.
[33]国家电网公司营销部.电力市场体系建设资料选编[z].2006.
[34]张少华,李渝曾,王长军,等.电力市场中的远期合同交易[J].电力系统自 动化.2001,25(10):6-1050.
[35]张显,王锡凡,陈皓勇,等.电力市场中的双边合同[J].电力自动化设备.2003,23(11):77-86.
[36]刘广一,于尔铿,宋永华,等.合同的基本类型及其在电力市场中的作用[J].中国电力.1999,32(3):64-67.
[37]王长军,张少华,李渝曾,等.考虑差价合同交易的电力市场均衡模型[J].上海大学学报(自然科学版).2001,7(4):349-352.
[38]王建斌,谭忠富.远期合同对发电商市场力影响的博弈均衡模型[J].现代电力.2007,24(4):80-83.
[39]刘军虎,陈皓勇,张显.电力市场远期合同交易的实验分析[J].经济经纬.2006(6):26-29.
[40]袁智强,侯志俭,宋依群,等.考虑远期合约的不同投标策略供应函数均衡分析[J].上海交通大学学报.2004,38(8):1241-1245.
[41]任玉珑,唐松林,刘贞,等.基于Swarm的发电侧电力市场结构研究[J].统计与决策.2007(21):97-100.
[42]汤玉东,吴军基,邹云.Powerpool中发电公司竞价问题的微观经济学分析[J].电力系统自动化.2003,27(15):15-1924.
[43]刘晓东,戴士杰.浅析发电侧市场中的市场力[J].河北工业大学学报.2004,33(6):67-71.
[44]李晓刚,李玉平.统一出清电价下发电厂商的持留容量行为及相关抑制措施[J].电力自动化设备.2004,24(7):85-88.
[45]蔺利.基于博弈论分析合同在降低市场力方面的作用[J].商业研究.2005(6):83-86.
[46]吴宁,祖博,吴军基.演化博弈论在发电侧电力市场竟价中的应用[J].华东电力.2006,34(3):35-38.
[47]李明,李艳芳.考虑风险的发电厂竞价上网模型的研究[J].青海大学学报(自然科学版).2008,26(2):63-67.
[48]贾燕冰,严正.合同对动态电力市场稳定性的影响[J].电力系统自动化.2007,31(9):16-2071.
[49]戴铁潮,张丹.确定性合约电量分解算法在浙江发电市场的应用[J].华东电力.2000,28(10):7-959.
[50]赵学顺,戴铁潮,黄民翔.电力市场中风险规避问题的研究(二)--差价合约分析系统的实现[J].电力系统自动化.2001,25(8):16-19.
[51]余志文,郭志忠,于尔铿,等.发电侧电力市场中合同电量的一种实现方法[J].中国电力.2000,33(11):76-78.
[52]李其智,任震,余志文.基于合同电量滚动分解算法的合同管理系统[J].广东电力.2002,15(3):76-7881.
[53]汪峰,白晓民.面向电力市场交易计划算法研究[J].电网技术.2001,25(8):45-49.
[54]王漪,于继来,柳焯.基于月度竞价空间滚动均衡化的年中标电量分解[J].电力系统自动化.2006,30(17):24-2764.
[55]黎灿兵,胡亚杰,赵弘俊,等.合约电量分解通用模型与算法[J].电力系统自动化.2007,31(11):26-30.
[56]赵宇,全宏兴.电力市场过渡期水火电中长期合同计划[J].水电自动化与大坝监测.2007,31(3):6-9.
[57]张力,刘俊勇,刘继春,等.火电机组年合同电量的编制与分解算法研究[J].继电器.2007,35(4):64-69.
[58]温丽丽,刘俊勇,吴志云,等.基于月度滚动修正的合同电量分解算法与应用[J].现代电力.2008,25(1):82-87.
[59]姜生斌,纪昌明,王蕊,等.水电站年度合同电量的月度分解研究[J].人民黄河.2008(06).
[60]王雁凌,张粒子,舒隽,等.合同交易量和竞价交易量在日计划中的经济分配[J].电力系统自动化.2002,26(9):45-48.
[61]蒋东荣,刘学军,李群湛.电力市场环境下电网日发电计划的电量经济分配策略[J].中国电机工程学报.2004,24(7):90-94.
[62]吕泉,武亚光,王海霞,等.制定电力市场中机组合同交易电量日出力计划的一种经济策[J].电网技术.2006,30(14):71-76.
[63]封嘉爱.日前交易开放前日调度计划优化的研究[D].哈尔滨:哈尔滨工业 大学,2006.
[64]严亚男.水电参与南方区域电力市场交易的若干问题研究[D].昆明:昆明理工大学,2006.
[65]初壮.初期电力市场有功优化调度模型与算法研究[D].哈尔滨:哈尔滨工业大学,2007.
[66]陈亚红,穆钢,段方丽.短期电力负荷预报中几种异常数据的处理[J].东北电力学院学报.2002,22(2):1-5.
[67]田增尧,张明理,赵瑞.短期电力负荷预报中异常负荷数据的识别和修正[J].吉林电力.2004(6):21-23.
[68]张国江,邱家驹,李继红.基于人工神经网络的电力负荷坏数据辨识与调整[J].中国电机工程学报.2001,21(8):104-107113.
[69]张晓星,程其云,周湶,等.基于数据挖掘的电力负荷脏数据动态智能清洗[J].电力系统自动化.2005,29(8):60-64.
[70]叶锋,何桦,顾全,等.EMS中负荷预测不良数据的辨识与修正[J].电力系统自动化.2006,30(15):85-88.
[71]莫维仁,张伯明,孙宏斌,等.扩展短期负荷预测方法的应用[J].电网技术.2003,27(5):6-9.
[72]康重庆夏清,相年德.灰色系统参数估计与不良数据辨识[J].清华大学学报(自然科学版).1997(04).
[73]高山,单渊达.小波奇异性检测在负荷数据纠错和平滑处理中的应用[J].中国电机工程学报.2001,21(11):105-108113.
[74]任丽娜,芮执元,刘彦新,等.异常电力负荷数据的辨识方法研究[J].水力发电.2008,34(2):43-45.
[75]胡上序,陈德钊.观测数据的分析与处理[M].杭州:浙江大学出版社,1996.
[76]赵学顺,黄民翔,韩祯祥.电力市场中风险规避问题的研究(一)--不同电力市场阶段风险规避模型[J].电力系统自动化.2001,25(7):14-20.
[77]Alcock D,Pang C K.Risk management in a power pool[C].Proceeding of the 5th International Conference on Advances in Power System Control,Operation and Management.Hong Kong,.
[78]陈晓林,刘俊勇,宋永华,等.利用差价合同和金融输电权的组合规避电力市场风险[J].中国电机工程学报.2005,25(10):75-81.
[79]Qi L,Huang W.The impact of contract for difference on the market power in the restructured zhejiang electricity market[C].Power Systems Conference and Exposition,2004.IEEE PES.
[80]国家电力监管委员会华东监管局.华东电力市场运营规则2007/日期[2007-8-31].http://www.ecerb.gov.cn/upload/1145865702328.pdf.
[81]谢恒,李胜利,李晶,等.电力市场中合同管理系统的设计与应用[J].广东电力.2008,21(4):43-4660.
[82]Jerry S R.A Case Study in Risk Management[M].New Jersey:Prentice Hall,1972.
[83]Charles w.smithson.Managing Financial Risk:A Guide to Derivative Products,Financial Engineering,and Value Maximization[M].第3版.北京:中国人民大学出版社,2003.
[84]Markowitz H.Portfolio selection:efficient diversification of investments[M].Cambridge:Blackwell Publishing Limited,1991.
[85]吴喜之,王兆军.非参数统计方法[M].北京:高等教育出版社,1996.
[86]Owen G.Game theory[M].San Diego:Academic Press Limited,1995.
[87]谢政.对策论[M].长沙:国防科技大学出版社,2004.
[88]朱宝宪.投资学[M].北京:清华大学出版社,2002.
[89]张宁,杨莉,戴铁潮,等.区域市场中电力公司报价辅助决策系统设计[J]_电力系统自动化.2007,31(2):46-50.
[90]刘宝碇,赵瑞清,王刚.不确定规划及应用[M].北京:清华大学出版社,2003.
[91]Breipohl A M,Lee F N,Zhai D,et al.Gauss-Markov Load Model for Application in Risk Evaluation and Production Simulation[J].IEEE Power Engineering Review.1992,12(11):47.
[92]Zhai D,Breipohl A M,Lee F N,et al.The effect of load uncertainty on unit commitment risk[J].IEEE Transactions on Power Systems.1994,9(1):510-517.
[93]Jia-yo C,Breipohl A M,Lee F N,et al.Estimating the variance of production cost using a stochastic load model[J].IEEE Transactions on Power Systems.2000,15(4):1212-1217.
[94]Sisworahardjo N S,El-keib A A,Choi J,et al.A stochastic load model for an electricity market[J].Electric Power Systems Research.2006,76(6-7):500-508.
[95]Qiu Y,Zhao J,Chiang H D.Effects of the stochastic load model on power system voltage stability based on bifurcation theory[C].Transmission and Distribution Conference and Exposition,2008.T&D.IEEE/PES.Chicago,.
[96]朱东晓,曹树华,赵磊,等.电力负荷预测技术及其应用[M].北京:中国电力出版社,2004.
[97]康重庆,夏清,刘梅.电力系统负荷预测[M].北京:中国电力出版社,2007.
[98]Christian G.ARCH Models and Financial Applications[M].New York:Springer-Verlag,1997.
[99]彭作祥.金融时间序列建模分析[M].成都:西南财经大学出版社,2006.
[100]Corm A R,Gould N I,Toint P L.A Globally Convergent Augmented Langrangian Algorithm for Optimization with General Constraints and Simple Bounds[J].SIAM Journal on Numerical Analysis.1991,28(2):545- 572.
[101]Conn A R,Gould N I,Toint P L.A Globally Convergent Lagrangian Barrier Algorithm for Optimization with General Inequality Constraints and Simple Bounds[J].Mathematics of Computation.1997,66(217):261-288.
[102]Kantardzic M.Data mining:concepts,models,methods,and algorithms[M].New York:Wiley-IEEE,2003.
[103]孙文瑜,袁亚湘.最优化理论与方法[M].北京:科学出版社,2005.
[104]徐玖平,李军.多目标决策的理论和方法[M].北京:清华大学出版社,2005.
[105]高岳林,徐成贤.解带有二次约束二次规划的一个整体优化方法[J].运筹学学报.2002,6(2):53-60.
[106]Hoai A L.An efficient algorithm for globally minimizing a quadratic function under convex quadratic constraints[J].2000,87(3):401-426.