电力市场环境下火电厂竞价决策研究
|
摘要
电力工业的市场化改革已经成为我国电力工业发展的必然趋势。我国电力市场的发展还处于初级阶段,仅在发电侧引入竞争,火电厂作为电力市场的竞争主体,通过参与市场竞争来获得经济效益。如何建立符合电力市场交易情况的竞价决策模型,制定优化的竞价策略来实现电厂竞价利润的最大化,已成为火电厂急须解决的问题。本文从指导火电厂参与电力市场竞价交易、提高电厂经济效益的角度出发,对火电厂竞价决策问题进行了研究。
目前我国电力市场的竞价交易以电能交易为主。火电厂的竞价交易主要在中长期交易市场和日前交易市场中进行。本文结合火电厂的生产特点和我国电力市场发展的实际情况,针对火电厂竞价交易中所面临的主要问题,构建了火电厂竞价决策的整体框架。该框架主要对三个层面的问题进行分析:中长期交易中的发电量优化分配问题,日前交易市场中的多时段竞价问题,峰荷交易时段输电网络约束对火电厂竞价决策的影响。
我国电力市场的电能交易需要通过年度、月度以及日前交易多个交易市场来完成。由于受发电成本、各交易市场电价以及供需关系等因素的影响,按不同比例将有限的发电量分配到各交易市场,所获得的风险和收益会有很大差异。为了协调火电厂的竞价利润和风险,本文提出了基于满意度的发电量优化分配决策模型。该模型借鉴模糊投资组合理论,结合决策者经验和对待风险的态度,将反映利润和风险的各项指标进行模糊化处理,通过模糊优化确定符合决策者满意度的电量优化分配策略。该模型较适合我国电力市场历史数据不足的实际情况,可以为电厂制定月度竞价策略提供理论依据。
在日前交易市场中,发电厂主要解决的问题是,如何合理安排次日各交易时段的竞价电量,使发电机组在满足合同电量的前提下,获得更多利润。本文通过对历史交易数据的分析处理,给出反映市场电价与电厂中标容量之间量化关系的电价模糊回归模型,在此基础上建立了基于模糊机会约束规划的多时段竞价决策模型。该模型考虑了市场电价不确定所引起的风险问题,通过选择置信水平和悲观利润与乐观利润的折中系数,体现决策者对待风险的态度。根据优化模型的特点,提出了改进的离散二进制粒子群优化算法和遗传算法相结合的求解方法。分别针对目标函数和约束条件的特点,给出适应求解该模型的启发式规则和粒子生成策略,保证新生粒子为该优化问题的可行解,提高了离散二进制粒子群算法的性能。
在峰荷时段,输电元件上的传输功率往往会达到其输电容量的限值,输电容量约束的存在将直接影响电力市场的出清结果,进而影响到发电厂的竞价利润。本文采用考虑合同电力的线性供给函数均衡模型,分析在峰荷时段输电网络约束对火电厂竞价决策的影响。对无约束和计及网络约束情况下,电厂的Nash均衡策略进行了比较研究,并针对峰荷时段发电厂竞价所特有的现实条件,提出了一种改进的循环迭代方法,用来求解Nash均衡。该方法可以在一定程度上避免由于初始值选择不当导致的迭代不收敛,而且迭代过程可以保证网络阻塞状况不发生变化,有利于得到符合现实意义的Nash均衡。
为了根据自身特点和外部市场环境制定合理的竞价策略,发电厂需要对自身以及竞争对手的在电力市场中的竞争地位进行分析和评估。本文建立了面向发电厂的市场力评估指标体系。该评估指标体系分为预评估指标和后评估指标。重点分析了预评估指标的构成。在分析发电厂的必发容量的基础上,运用潮流追踪方法从微观层面分析了发电厂对节点和支路的影响,提出了发电厂供电控制区域的概念,由此构成了反映发电厂对节点、支路、区域乃至系统影响的预评估指标。预评估指标用于比较评价各电厂的位置优势,后评估指标则用于评价电厂自身市场力水平和竞价策略的优劣。
本文研究工作得到了国家自然科学基金项目(项目编号:50377021)的资助,部分研究成果在烟台发电厂运营决策支持系统中得到应用,该项目获得山东省科技进步二等奖。
Power market is the trend of Chinese power industry. Chinese power market is in the original period which there is competition only in the supply side. Power plant gets profit through competition as the main part in the market competition. How to built bidding decision model according with the market trade condition, establish optimal bidding strategy to maximize bidding profit of the plant is in urgent need for the power plant. In this thesis the power plant bidding strategy problem is studied from point of power plant taking part in power market bidding trade and increasing the profit of power plant.
At present the main trade is for energy in Chinese power market. The bidding trade of the power plants is going on mainly in the medium and long trade and day-head market. In this thesis aiming at the main problem that will be met in the power plant bidding trade combined the power plant’s producing characteristics and real condition in Chinese power plant developing, the power plant bidding decision framework is built. In this framework three aspects of problems are analyzed which are optimal generation allocation in medium and long trade market, multi-periods bidding in day-ahead trade market and the influence of transmission constraints on power plant bidding decision in peak load trade period.
The energy trade in Chinese power market should be accomplished in year market, month market and day-ahead market. Influenced by the generation cost, different prices in market and relationship between supply and demand the limited generation allocated in each market according to different proportion will lead to different risk and profit. To harmonize the profit and risk of the power plant in this thesis an optimal allocation decision model based on satisfaction is proposed. In this model the fuzzy investment portfolio theory is used for reference. Combined with the experience of the decision-maker and his attitude to risk, the indexes reflecting risks and profits are expressed by fuzzy sets. The optimal generation allocation strategy accord with decision-maker satisfaction is decided by fuzzy optimization.
In day-ahead trade market the main problem should be solved is arrange next day bidding generation in each trade period reasonably to get more profit in premise of satisfy the contract generation. In this thesis the historic trade data is analyzed, the fuzzy regression model which reflect the relationship between the market price and generator allocated generation is given. On this basis the multi-period bidding decision model based on fuzzy chance-constrained program model is built. In this model the risk problem caused by uncertainty of market power price is considered, the decision-maker’s attitude to risk is reflected by selecting confidence level and tradeoff coefficient of pessimism profit and optimism profit. Considering the characteristic of this model the BPSO and GA are united to solve this problem. Aiming at the characteristics of the object and constraint condition, the heuristic rules and particle generation strategy to solve this model is given to assure the new particle is the feasible solve for the optimization problem and the performance of BPSO is improved.
In the peak load period, the transmission power in the transmission equipment will reach the transmission capacity limit which can affect the clearing result of the power market and the bidding profit of the power plant. In this thesis the linear supply function equilibrium model of contracted power is considered, the power bidding decision with transmission constraints during peak load period is studied. The Nash equilibrium decisions with and without network constraints are compared. For the special real condition of power plant bidding in peak load period an advanced circulation iteration method is proposed to solve Nash equilibrium. This method prohibits iteration disconvergence by improper selection of original data. The iteration process guarantees that the congestion condition of the network will not change. And the solved Nash equilibrium has real sense.
To make reasonable bidding strategy based on its characteristics and market environment outside, the power plant need to analyze and evaluate the position of itself and competing rival. In this thesis the market power evaluation index system for power plant is built. The evaluation index system is separated into pre-evaluation and post-evaluation index. The composition of the pre-evaluation is analyzed for emphasis. On the basis of analyzing must-run capacity the power flow tracing method is used to analyze influence of plant on node and branch from the point of microcosmic. The power plant serving control area is proposed. And on this basis the pre-evaluation index reflecting influences of power plant on node, branch, area and the system is formed. The pre-evaluation index is to compare the advantage of power plant position and the post-evaluation index is to evaluate self-market power and the bidding strategy.
This research work is supported by National Nature Science Foundation (50377021). Part of the research results has been applied in Operation Decision Supporting System of Yantai Power Plant. This project achieved the Shandong Province Science and Technology Advancement Award.
目前我国电力市场的竞价交易以电能交易为主。火电厂的竞价交易主要在中长期交易市场和日前交易市场中进行。本文结合火电厂的生产特点和我国电力市场发展的实际情况,针对火电厂竞价交易中所面临的主要问题,构建了火电厂竞价决策的整体框架。该框架主要对三个层面的问题进行分析:中长期交易中的发电量优化分配问题,日前交易市场中的多时段竞价问题,峰荷交易时段输电网络约束对火电厂竞价决策的影响。
我国电力市场的电能交易需要通过年度、月度以及日前交易多个交易市场来完成。由于受发电成本、各交易市场电价以及供需关系等因素的影响,按不同比例将有限的发电量分配到各交易市场,所获得的风险和收益会有很大差异。为了协调火电厂的竞价利润和风险,本文提出了基于满意度的发电量优化分配决策模型。该模型借鉴模糊投资组合理论,结合决策者经验和对待风险的态度,将反映利润和风险的各项指标进行模糊化处理,通过模糊优化确定符合决策者满意度的电量优化分配策略。该模型较适合我国电力市场历史数据不足的实际情况,可以为电厂制定月度竞价策略提供理论依据。
在日前交易市场中,发电厂主要解决的问题是,如何合理安排次日各交易时段的竞价电量,使发电机组在满足合同电量的前提下,获得更多利润。本文通过对历史交易数据的分析处理,给出反映市场电价与电厂中标容量之间量化关系的电价模糊回归模型,在此基础上建立了基于模糊机会约束规划的多时段竞价决策模型。该模型考虑了市场电价不确定所引起的风险问题,通过选择置信水平和悲观利润与乐观利润的折中系数,体现决策者对待风险的态度。根据优化模型的特点,提出了改进的离散二进制粒子群优化算法和遗传算法相结合的求解方法。分别针对目标函数和约束条件的特点,给出适应求解该模型的启发式规则和粒子生成策略,保证新生粒子为该优化问题的可行解,提高了离散二进制粒子群算法的性能。
在峰荷时段,输电元件上的传输功率往往会达到其输电容量的限值,输电容量约束的存在将直接影响电力市场的出清结果,进而影响到发电厂的竞价利润。本文采用考虑合同电力的线性供给函数均衡模型,分析在峰荷时段输电网络约束对火电厂竞价决策的影响。对无约束和计及网络约束情况下,电厂的Nash均衡策略进行了比较研究,并针对峰荷时段发电厂竞价所特有的现实条件,提出了一种改进的循环迭代方法,用来求解Nash均衡。该方法可以在一定程度上避免由于初始值选择不当导致的迭代不收敛,而且迭代过程可以保证网络阻塞状况不发生变化,有利于得到符合现实意义的Nash均衡。
为了根据自身特点和外部市场环境制定合理的竞价策略,发电厂需要对自身以及竞争对手的在电力市场中的竞争地位进行分析和评估。本文建立了面向发电厂的市场力评估指标体系。该评估指标体系分为预评估指标和后评估指标。重点分析了预评估指标的构成。在分析发电厂的必发容量的基础上,运用潮流追踪方法从微观层面分析了发电厂对节点和支路的影响,提出了发电厂供电控制区域的概念,由此构成了反映发电厂对节点、支路、区域乃至系统影响的预评估指标。预评估指标用于比较评价各电厂的位置优势,后评估指标则用于评价电厂自身市场力水平和竞价策略的优劣。
本文研究工作得到了国家自然科学基金项目(项目编号:50377021)的资助,部分研究成果在烟台发电厂运营决策支持系统中得到应用,该项目获得山东省科技进步二等奖。
Power market is the trend of Chinese power industry. Chinese power market is in the original period which there is competition only in the supply side. Power plant gets profit through competition as the main part in the market competition. How to built bidding decision model according with the market trade condition, establish optimal bidding strategy to maximize bidding profit of the plant is in urgent need for the power plant. In this thesis the power plant bidding strategy problem is studied from point of power plant taking part in power market bidding trade and increasing the profit of power plant.
At present the main trade is for energy in Chinese power market. The bidding trade of the power plants is going on mainly in the medium and long trade and day-head market. In this thesis aiming at the main problem that will be met in the power plant bidding trade combined the power plant’s producing characteristics and real condition in Chinese power plant developing, the power plant bidding decision framework is built. In this framework three aspects of problems are analyzed which are optimal generation allocation in medium and long trade market, multi-periods bidding in day-ahead trade market and the influence of transmission constraints on power plant bidding decision in peak load trade period.
The energy trade in Chinese power market should be accomplished in year market, month market and day-ahead market. Influenced by the generation cost, different prices in market and relationship between supply and demand the limited generation allocated in each market according to different proportion will lead to different risk and profit. To harmonize the profit and risk of the power plant in this thesis an optimal allocation decision model based on satisfaction is proposed. In this model the fuzzy investment portfolio theory is used for reference. Combined with the experience of the decision-maker and his attitude to risk, the indexes reflecting risks and profits are expressed by fuzzy sets. The optimal generation allocation strategy accord with decision-maker satisfaction is decided by fuzzy optimization.
In day-ahead trade market the main problem should be solved is arrange next day bidding generation in each trade period reasonably to get more profit in premise of satisfy the contract generation. In this thesis the historic trade data is analyzed, the fuzzy regression model which reflect the relationship between the market price and generator allocated generation is given. On this basis the multi-period bidding decision model based on fuzzy chance-constrained program model is built. In this model the risk problem caused by uncertainty of market power price is considered, the decision-maker’s attitude to risk is reflected by selecting confidence level and tradeoff coefficient of pessimism profit and optimism profit. Considering the characteristic of this model the BPSO and GA are united to solve this problem. Aiming at the characteristics of the object and constraint condition, the heuristic rules and particle generation strategy to solve this model is given to assure the new particle is the feasible solve for the optimization problem and the performance of BPSO is improved.
In the peak load period, the transmission power in the transmission equipment will reach the transmission capacity limit which can affect the clearing result of the power market and the bidding profit of the power plant. In this thesis the linear supply function equilibrium model of contracted power is considered, the power bidding decision with transmission constraints during peak load period is studied. The Nash equilibrium decisions with and without network constraints are compared. For the special real condition of power plant bidding in peak load period an advanced circulation iteration method is proposed to solve Nash equilibrium. This method prohibits iteration disconvergence by improper selection of original data. The iteration process guarantees that the congestion condition of the network will not change. And the solved Nash equilibrium has real sense.
To make reasonable bidding strategy based on its characteristics and market environment outside, the power plant need to analyze and evaluate the position of itself and competing rival. In this thesis the market power evaluation index system for power plant is built. The evaluation index system is separated into pre-evaluation and post-evaluation index. The composition of the pre-evaluation is analyzed for emphasis. On the basis of analyzing must-run capacity the power flow tracing method is used to analyze influence of plant on node and branch from the point of microcosmic. The power plant serving control area is proposed. And on this basis the pre-evaluation index reflecting influences of power plant on node, branch, area and the system is formed. The pre-evaluation index is to compare the advantage of power plant position and the post-evaluation index is to evaluate self-market power and the bidding strategy.
This research work is supported by National Nature Science Foundation (50377021). Part of the research results has been applied in Operation Decision Supporting System of Yantai Power Plant. This project achieved the Shandong Province Science and Technology Advancement Award.
引文
1于尔铿,韩放,谢开等.电力市场.中国电力出版社, 1998: 23~35
2尚金成,黄永皓,夏清.电力市场理论研究与应用.中国电力出版社, 2002: 67~79
3国家电监会.欧洲、澳洲电力市场.中国电力出版社, 2005: 5~10
4国家电监会.美国电力市场.中国电力出版社, 2005: 7~16
5 A. L. Ott. Experience with PJM market operation, system design, and implementation. IEEE Transaction on Power Systems. 2003, 18(2): 528~534
6 T. Kristiansen. Congestion management, transmission pricing and area price hedging in the Nordic region. Electrical Power and Energy Systems. 2004, 26(9): 685~695
7李英.北欧与华东电力市场环境之比较.浙江电力. 2003, 22(4): 62~65
8 G. B. Sheble. Computational auction mechanisms for restructured power industry operation. Kluwer Academic Publishers, 1999: 35~41
9 C. A. Li, A. J. Svoboda, X. H. Guan et al. Revenue adequate bidding strategies in competitive electricity markets. IEEE Transactions on Power Systems. 1999, 14(2): 492~497
10 E. Sakk, R. J. Thomas, R. Zimmerman. Power system bidding tournament for a deregulated environment. Proceedings of the 30th Hwaii International Conference on System Sciences, Hawaii(USA) 1997: 681~686
11曾次玲,张步涵,谢培元.澳大利亚国家电力市场最新发展状况.电网技术. 2004, 28(24): 74~80
12 S. Kelly. Australian electricity market. IEEE Power Engineering Review. 2002, 22(5): 4~7, 15
13 R. D. Christie, I. Wangensteen. The energy market in Norway and Sweden: Introduction. IEEE Power Engineering Review. 1998, 18(2): 44~45
14王永干,刘宝华.国外电力工业体制与改革.中国电力出版社, 2001: 10~15
15史连军,韩放.中国电力市场的现状与展望.电力系统自动化. 2000,24(3): 1~4
16李灿,朱峰,杨立兵等.华东电力市场月度双向竞价模式.电力系统自动化. 2004, 28(17): 27~31
17国家电监会.关于印发《东北区域电力市场初期运营规则(暂行)》的通知.电监供电[2003]54号令, 2003
18国家电监会.华东电力市场运行规则.电监市场[2004]13号令, 2004
19 A. K. David, F. S. Wen. Strategic bidding in competitive electricity markets: a literature survey. IEEE Power Engineering Soiety Summer Meeting, Seattle(USA), 2000: 2168~2173
20 A. K. David. Competitive Bidding in Electricity Supply. IEE Proceedings: Generation, Transmission and Distribution. 1993, 140(3): 421~426
21雷兵,王秀丽,王锡凡.独立发电商在日前市场的竞价策略分析.电力系统自动化. 2002, 26(24): 8~14
22李凡生,徐丽杰,王玮.试论火力发电厂动态成本分析.电网技术. 2001, 25(7): 44~47
23李涛,蒋传文,侯志俭.发电侧电力市场的成本报价模型.水电能源科学. 2001, 19(3): 62~62
24周建平,周浩.电力市场竞价策略探讨.中国电力. 2001, 34(3): 8~10
25刘继春,刘俊勇.发电公司回收成本型报价决策.电力系统及其自动化学报. 2003, 15(4): 79~82
26 E. Gallestey, A. Stothert, M. Antoine, S. Morton. Model Predictive Control and the Optimization of Power Plant Load While Considering Lifetime Consumption. IEEE Transactions on Power Systems. 2002, 17(1): 186~192
27 A. Wang, B. Ramsay. Prediction of System Marginal Price in the UK Power Pool Using Neural Networks. International Conference on Neural Networks, Houston (USA), 1997: 2116~2120
28杨莉,邱家驹,江道灼.基于BP网络的下一交易日无约束市场清算电价预测模型.电力系统自动化. 2001, 25(19): 11~14
29李彩华,郭志忠,王志伟.混合式短期边际电价预测模型.电力系统自动化. 2002, 26(10): 29~33
30 F. J. Nogales, J. Contreras, A. J. Conejo et al. Forecasting Next-Day Electricity Prices by Time Series Models. IEEE Transactions on Power Systems. 2002, 17(2): 342~348
31 G. Li, C. C. Liu, C. Mattson, J. Lawarree. Day-ahead electricity price forecasting in a Grid Environment. IEEE Transactions on Power Systems. 2007, 22(1): 266~274
32 R. C. Garcia, J. Contreras, M. V. Akkeren, J. B. C. Garcia. A GARCH forecasting model to predict day-ahead electricity prices. IEEE Transactions on Power Systems. 2005, 20(2): 867~874
33 A. J. Conejo, F. J. Nogales. Price-taker Bidding Strategy Under Price Uncertainty. IEEE Transactions on Power Systems. 2002, 17(4): 1081~1088
34 Y. Y. Hatim, S. M. Shahidehpour. Risk and Profit in Self-Scheduling for GenCos. IEEE Transactions on power Systems. 2004, 19(4): 2104~2106
35 A. J. Conejo, F. J. Nogales, J. M. Arroyo et al. Risk-Constrained Self-Scheduling of a Thermal Power Producer. IEEE Transactions on Power Systems. 2004, 19(3): 1569~1574
36 D. Das, B. F. Wollenberg. Risk Assessment of Generators Bidding in Day-Ahead Market. IEEE Transactions on Power Systems. 2005, 20(1): 416~424
37张喜铭,姚建刚.基于效用分析方法的发电企业最优报价策略.电力系统自动化. 2005, 29(7): 12~16
38 H. Y. Yamin. Fuzzy Self-Scheduling for GenCos. IEEE Transactions on Power Systems. 2005, 20(1): 503~505
39 H. Y. Yamin. Spinning reserve uncertainty in day-ahead competitive electricity markets for GENCOs. IEEE Transactions on Power Systems. 2005,
20(1): 521~523
40 F. S. Wen, A. K. David. Optimal Bidding Strategies and Modeling of Imperfect Information Among Competitive Generators. IEEE Trans on Power Systems. 2001, 16(1): 15~19
41马新顺,文福拴,倪以信.计及风险的发电公司最优报价策略研究.电力系统自动化. 2003, 27(20): 16~20
42马莉,文福拴, A. K. David.采用分段报价规则的竞价策略初探.电力系统自动化. 2002, 26(9): 16~19
43高春凤,江辉,彭建春.基于机会约束规划的发电商报价策略.电网技术. 2005, 29(19): 75~78
44马新顺,文福拴,刘建新.构造发电公司最优报价策略的机会约束规划方法.电网技术. 2005, 29(10): 35~39
45宋依群,焦连伟,倪以信等.应用动态学习改进对竞争对手微增响应猜测的发电公司投标策略.中国电机工程学报. 2003, 23(12): 23~27
46张晓东,高波,宋之平.发电企业竞标策略的研究.中国电机工程学报. 2004, 24(9): 153~157
47 J. D. WEBER, T. J. OVERBYE. A Two-level Optimization Problem for Analysis of Market Bidding Strategies. Proceedings of IEEE Power Engineering Society Summer Meeting, Edmonton (Canada), 1999: 682~687
48 V. P. Gountis, A. G. Bakirtzis. Bidding Strategies for Electricity Producers in a Competitive Electricity Marketplace. IEEE Transactions on Power Systems. 2004, 19(1): 356~365
49 A. G. Bakirtzis, N. P. Ziogos, A. C. Tellidou, G. A. Bakirtzis. Electricity producer offering strategies in day-ahead energy market with step-wise offers. IEEE Transactions on Power Systems. 2007, 22(4): 1804~1818
50 P. Bajpai, S. N. Singh. Fuzzy adaptive particle swarm optimization for bidding strategy in uniform price spot market. IEEE Transactions on Power Systems. 2007, 22(4): 1804~1818
51马新顺,文福栓,刘建新.计及输电容量约束的发电公司最优报价策略.电力系统自动化. 2005, 29(10): 6~10
52陶芬,张步涵,杨超.考虑输电阻塞影响的发电商最优报价策略.电网技术. 2007, 31(16): 12~16
53杨莉,文福拴,吴复立.基于可能性理论的发电公司最优报价策略.电力系统自动化. 2002, 26(23): 12~17
54张维迎.博弈论与信息经济学.上海人民出版社, 1996: 145~167
55刁勤华,林济铿,倪以信等.博弈论及其在电力市场中的应用.电力系统自动化. 2001, 25(1): 19~23
56尚金城,黄光皓,张维存等.一种基于博弈论的发电商竞价策略模型与算法.电力系统自动化. 2002, 26(9): 11~14
57 H. Song, C. C. Liu, J. Lawarree. Nash equilibrium bidding strategies in a bilateral. IEEE Transactions on Power Systems. 2002, 17(1): 73~79
58 B. F. Hobbs. Linear Complementarity Model of Nash-Cournot Competition in Bilateral and POOLCO Power Market. IEEE Transactions on Power Systems. 2001, 16(2): 194~202
59 J. Contreras et al. Numerical solutions to Nash-Cournot equilibria in coupled constraint electricity markets. IEEE Transactions on Power Systems. 2004, 19(1): 195~206
60 D. Chattopadhyay. Multicommodity Spatial Cournot Model for Generator Bidding Analysis. IEEE Transactions on Power Systems. 2004, 19(1): 267~275
61 A. R. Kian, J. B. Cruz, R. J. Thomas. Bidding strategies in oligopolistic dynamic electricity double-sided auctions. IEEE Transactions on Power Systems. 2005, 20(1): 50~58
62武智勇,康重庆,夏清等.基于博弈论的发电商报价策略.电力系统自动化. 2002, 26(9): 7~11
63郭家春,何光宇,王稹等.发电公司报价决策行为的分析.电力系统自动化. 2002, 26(5): 5~8
64任玉珑,邹小燕,张新华.发电公司的不完全信息竞价博弈模型.电力系统自动化. 2003, 27(9): 11~14
65 R. Baldick. Electricity market equilibrium models: the effect of parameterization. IEEE Transactions on Power Systems. 2002, 17(4): 1170~1176
66 C. J. Day et al. Oligopolistic competition in power networks: a conjectured supply function approach. IEEE Transactions Power Systems. 2002, 17(3): 597~607
67 H. Niu, R. Baldick, G. D. Zhu. Supply function equilibrium bidding strategies with fixed forward contracts. IEEE Transactions on Power Systems. 2005, 20(4): 1859~1867
68 T. Li, M. Shahidehpour. Strategic bidding of transmission-constrained GENCOs with incomplete information. IEEE Transactions on Power Systems. 2005, 20(1): 437~447
69 P. Couchman, B. Kouvaritakis, M. Cannon, F. Prashad. Gaming strategy for electric power with random demand. IEEE Transactions on Power Systems. 2005, 20(3): 1283~1292
70 R. Green, D. M. Newbery. Competition in Electricity Spot Market. Journal of Political Economy. 1992, 100(5): 929~953
71 R. Wang, J. C. Shang, X. Y. Zhou, Y. C. Zhang. Risk assessment andmanagement of portfolio optimization for power plants. IEEE/PES Transmission and Distribution Conference & Exhibition: Asia and Pacific, Dalian(China), 2005: 1~6
72 R. Bjorgan, C. C. Liu, J. Lawarree. Financial Risk Management in a Competitive Electricity Market. IEEE Transactions on Power Systems. 1999, 14(4): 1285~1291
73 T. Bjorkvoll. Power generation on planning and risk management in a liberalized market. Proceedings of IEEE Porto Power Tech Conference, Porto(Portugal), 2001: 6~10
74 D. J. Wu. Optimal bidding and contracting strategies in the deregulated electric power market: Part I. Proceedings of the 33rd Annual Havaii International Conference on System Sciences, Havaii(USA), 2000: 1235~1240
75 M. A. Plazas, A. J. Conejo, F. J. Prieto. Multimarket optimal bidding for a power producer. IEEE Transactions on Power Systems. 2005, 20(4): 2041~2050
76任震,黄福权,黄雯莹.电力市场中的发电厂投标组合策略.电力系统自动化. 2002, 26(2): 14~17
77刘亚安,薛禹胜,管晓宏. Price-taker在两个电力市场中的交易决策(二)发电商的策略.电力系统自动化. 2004, 28(17): 12~15
78曾次玲,张步涵,谢培元.基于风险管理在开放的能量市场和备用市场间优化分配发电容量.电网技术. 2004, 28(13): 70~74
79吕泉,武亚光,李卫东.考虑预期利润目标的发电商多交易市场发电量分配策略.电力系统自动化. 2006, 30(9): 29~33
80张显,王锡凡,王建学等.发电商长期电能分配策略研究.中国电机工程学报. 2005, 25(1): 6~12
81王壬,尚金成,冯旸等.基于CVaR风险计量指标的发电商投标组合策略及模型.电力系统自动化. 2005, 29(14): 5~9
82洪雁,邵全,吴祈宗.模糊机会约束规划下的投资组合模型研究.数量经济技术经济研究. 2005, (9): 112~118
83 K. K. Lai, S. Y. Wang, J. P. Xu et al. A Class of Linear Interval Programming Problems and Its Application to Portfolio Selection. IEEE Transactions on Fuzzy Systems. 2002, 10(6): 698~706
84 Y. Y. Hong, S. W. Tsai, M. T. Weng. Bidding strategy based on artificial intelligence for a competitive electric market. IEE Proceedings: Generation, Transmission and Distribution. 2001, 148(2): 159~164
85 W. Mielezarski, G. Michalik, M. Widjaja. Bidding strategies in electricity market. Proceedings of the 21st 1999 IEEE International Conference, Santa Clara(Canada), 1999: 71~76
86刘梅招,杨莉,甘德强.基于Agent的电力市场仿真研究综述.电网技术. 2005, 29(4): 76~80
87 D. W. Bunn, F. S. Oliveira. Agent-based simulation-An application to the new electricity trading arrangements of England and Wales. IEEE Transactions on Evolutionary Computation. 2001, 5(5): 493~503
88康重庆,江健健,夏清.基于智能个体信念学习的电力市场模拟的理论框架.电网技术. 2005, 29(12): 10~15
89 M. Ventosa, A. Baillo, A. Ramos et al. Electricity market modeling rends. Energy Policy. 2005, 33(7): 897~913
90 G. Xiong, T. Hashiyama, S. Okuma. An electricity supplier bidding strategy through Q-learning. Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, Chicago(USA), 2002: 1516~1521
91 I. Watanabe, K. Okada, K. Tokoro et al. Adaptive multiagent model of electric power market with congestion management. Proceedings of 2002 World Congress on Computational Intelligence-WCCI'02, Honolulu(USA), 2002: 12~17
92 A. K. David, F. S. Wen. Market Power in Electricity Supply. IEEE Transactions on Power Systems. 2001, 16(4): 352~360
93 A. F. Rahimi, A. Y. Shefrin. Effective Market Monitoring in Deregulated Electricity Markets. IEEE Transactions on Power Systems. 2003, 18(2): 486~493
94张集,张粒子,于亮.电力市场中的市场力分层评价指标体系.中国电机工程学报. 2006, 26(6): 123~128
95 D. Gan, D. V. Bourcier. A Simple Method for Locational Market Power Screening. IEEE Power Engineering Society Winter Meeting, New York(USA), 2002: 434~439
96 P. Wang, Y. Xiao, Y. Ding. Nodal Market Power Assessment in ElectricityMarkets. IEEE Transactions on Power Systems. 2004, 19(3): 1373~1379
97马歆,侯志俭,蒋传文.电力市场中市场势力的影响分析.电力自动化设备. 2004, 24(2): 38~41
98赵建国,韩学山,程时杰.电力市场中考虑发电机组功率速度约束的市场力分析.电网技术. 2003, 27(11): 43~48
99杨淼锋,刘俊勇,刘继春.市场力指标组态作用下的发电企业报价带研究.现代电力. 2005, 22(2): 71~76
100陈云华.基于综合指标法的发电企业市场竞争力分析.水力发电学报. 2006, 25(5): 14~17
101 J. Kennedy, R. Eberhart. A discrete binary version of the particle swarm algorithm. IEEE International Conference on computational Cybernetics and Simulation, Orlando(USA), 1997: 4104~4108
102刘涌,侯志俭,蒋传文.求解机组组合问题的改进离散粒子群算法.电力系统自动化. 2006, 30(4): 35~39
103 S. A. Kazarlis, A. G. Bakirtzis, V. Petridis. A genetic algorithm solution to the unit commitment problem. IEEE Transactions on Power Systems. 1996, 11(1): 83~92
104 R. Baldick, R. Grant, E. Kahn. Theory and application of linear supply function equilibrium in electricity markets. Journal of Regulatory Economics. 2004, 25(2): 143~167
105 B. F. Hobbs, B. Carolyn, J. S. Pang. Strategic gaming analysis for electric power systems: an MPEC approach. IEEE Transactions on Power Systems. 2000, 15(2): 638~645
106 J. Bialek. Tracing the flow of electricity. IEE Proceedings: Generation, Transmission and Distribution. 1996, 143(4): 313~320.
2尚金成,黄永皓,夏清.电力市场理论研究与应用.中国电力出版社, 2002: 67~79
3国家电监会.欧洲、澳洲电力市场.中国电力出版社, 2005: 5~10
4国家电监会.美国电力市场.中国电力出版社, 2005: 7~16
5 A. L. Ott. Experience with PJM market operation, system design, and implementation. IEEE Transaction on Power Systems. 2003, 18(2): 528~534
6 T. Kristiansen. Congestion management, transmission pricing and area price hedging in the Nordic region. Electrical Power and Energy Systems. 2004, 26(9): 685~695
7李英.北欧与华东电力市场环境之比较.浙江电力. 2003, 22(4): 62~65
8 G. B. Sheble. Computational auction mechanisms for restructured power industry operation. Kluwer Academic Publishers, 1999: 35~41
9 C. A. Li, A. J. Svoboda, X. H. Guan et al. Revenue adequate bidding strategies in competitive electricity markets. IEEE Transactions on Power Systems. 1999, 14(2): 492~497
10 E. Sakk, R. J. Thomas, R. Zimmerman. Power system bidding tournament for a deregulated environment. Proceedings of the 30th Hwaii International Conference on System Sciences, Hawaii(USA) 1997: 681~686
11曾次玲,张步涵,谢培元.澳大利亚国家电力市场最新发展状况.电网技术. 2004, 28(24): 74~80
12 S. Kelly. Australian electricity market. IEEE Power Engineering Review. 2002, 22(5): 4~7, 15
13 R. D. Christie, I. Wangensteen. The energy market in Norway and Sweden: Introduction. IEEE Power Engineering Review. 1998, 18(2): 44~45
14王永干,刘宝华.国外电力工业体制与改革.中国电力出版社, 2001: 10~15
15史连军,韩放.中国电力市场的现状与展望.电力系统自动化. 2000,24(3): 1~4
16李灿,朱峰,杨立兵等.华东电力市场月度双向竞价模式.电力系统自动化. 2004, 28(17): 27~31
17国家电监会.关于印发《东北区域电力市场初期运营规则(暂行)》的通知.电监供电[2003]54号令, 2003
18国家电监会.华东电力市场运行规则.电监市场[2004]13号令, 2004
19 A. K. David, F. S. Wen. Strategic bidding in competitive electricity markets: a literature survey. IEEE Power Engineering Soiety Summer Meeting, Seattle(USA), 2000: 2168~2173
20 A. K. David. Competitive Bidding in Electricity Supply. IEE Proceedings: Generation, Transmission and Distribution. 1993, 140(3): 421~426
21雷兵,王秀丽,王锡凡.独立发电商在日前市场的竞价策略分析.电力系统自动化. 2002, 26(24): 8~14
22李凡生,徐丽杰,王玮.试论火力发电厂动态成本分析.电网技术. 2001, 25(7): 44~47
23李涛,蒋传文,侯志俭.发电侧电力市场的成本报价模型.水电能源科学. 2001, 19(3): 62~62
24周建平,周浩.电力市场竞价策略探讨.中国电力. 2001, 34(3): 8~10
25刘继春,刘俊勇.发电公司回收成本型报价决策.电力系统及其自动化学报. 2003, 15(4): 79~82
26 E. Gallestey, A. Stothert, M. Antoine, S. Morton. Model Predictive Control and the Optimization of Power Plant Load While Considering Lifetime Consumption. IEEE Transactions on Power Systems. 2002, 17(1): 186~192
27 A. Wang, B. Ramsay. Prediction of System Marginal Price in the UK Power Pool Using Neural Networks. International Conference on Neural Networks, Houston (USA), 1997: 2116~2120
28杨莉,邱家驹,江道灼.基于BP网络的下一交易日无约束市场清算电价预测模型.电力系统自动化. 2001, 25(19): 11~14
29李彩华,郭志忠,王志伟.混合式短期边际电价预测模型.电力系统自动化. 2002, 26(10): 29~33
30 F. J. Nogales, J. Contreras, A. J. Conejo et al. Forecasting Next-Day Electricity Prices by Time Series Models. IEEE Transactions on Power Systems. 2002, 17(2): 342~348
31 G. Li, C. C. Liu, C. Mattson, J. Lawarree. Day-ahead electricity price forecasting in a Grid Environment. IEEE Transactions on Power Systems. 2007, 22(1): 266~274
32 R. C. Garcia, J. Contreras, M. V. Akkeren, J. B. C. Garcia. A GARCH forecasting model to predict day-ahead electricity prices. IEEE Transactions on Power Systems. 2005, 20(2): 867~874
33 A. J. Conejo, F. J. Nogales. Price-taker Bidding Strategy Under Price Uncertainty. IEEE Transactions on Power Systems. 2002, 17(4): 1081~1088
34 Y. Y. Hatim, S. M. Shahidehpour. Risk and Profit in Self-Scheduling for GenCos. IEEE Transactions on power Systems. 2004, 19(4): 2104~2106
35 A. J. Conejo, F. J. Nogales, J. M. Arroyo et al. Risk-Constrained Self-Scheduling of a Thermal Power Producer. IEEE Transactions on Power Systems. 2004, 19(3): 1569~1574
36 D. Das, B. F. Wollenberg. Risk Assessment of Generators Bidding in Day-Ahead Market. IEEE Transactions on Power Systems. 2005, 20(1): 416~424
37张喜铭,姚建刚.基于效用分析方法的发电企业最优报价策略.电力系统自动化. 2005, 29(7): 12~16
38 H. Y. Yamin. Fuzzy Self-Scheduling for GenCos. IEEE Transactions on Power Systems. 2005, 20(1): 503~505
39 H. Y. Yamin. Spinning reserve uncertainty in day-ahead competitive electricity markets for GENCOs. IEEE Transactions on Power Systems. 2005,
20(1): 521~523
40 F. S. Wen, A. K. David. Optimal Bidding Strategies and Modeling of Imperfect Information Among Competitive Generators. IEEE Trans on Power Systems. 2001, 16(1): 15~19
41马新顺,文福拴,倪以信.计及风险的发电公司最优报价策略研究.电力系统自动化. 2003, 27(20): 16~20
42马莉,文福拴, A. K. David.采用分段报价规则的竞价策略初探.电力系统自动化. 2002, 26(9): 16~19
43高春凤,江辉,彭建春.基于机会约束规划的发电商报价策略.电网技术. 2005, 29(19): 75~78
44马新顺,文福拴,刘建新.构造发电公司最优报价策略的机会约束规划方法.电网技术. 2005, 29(10): 35~39
45宋依群,焦连伟,倪以信等.应用动态学习改进对竞争对手微增响应猜测的发电公司投标策略.中国电机工程学报. 2003, 23(12): 23~27
46张晓东,高波,宋之平.发电企业竞标策略的研究.中国电机工程学报. 2004, 24(9): 153~157
47 J. D. WEBER, T. J. OVERBYE. A Two-level Optimization Problem for Analysis of Market Bidding Strategies. Proceedings of IEEE Power Engineering Society Summer Meeting, Edmonton (Canada), 1999: 682~687
48 V. P. Gountis, A. G. Bakirtzis. Bidding Strategies for Electricity Producers in a Competitive Electricity Marketplace. IEEE Transactions on Power Systems. 2004, 19(1): 356~365
49 A. G. Bakirtzis, N. P. Ziogos, A. C. Tellidou, G. A. Bakirtzis. Electricity producer offering strategies in day-ahead energy market with step-wise offers. IEEE Transactions on Power Systems. 2007, 22(4): 1804~1818
50 P. Bajpai, S. N. Singh. Fuzzy adaptive particle swarm optimization for bidding strategy in uniform price spot market. IEEE Transactions on Power Systems. 2007, 22(4): 1804~1818
51马新顺,文福栓,刘建新.计及输电容量约束的发电公司最优报价策略.电力系统自动化. 2005, 29(10): 6~10
52陶芬,张步涵,杨超.考虑输电阻塞影响的发电商最优报价策略.电网技术. 2007, 31(16): 12~16
53杨莉,文福拴,吴复立.基于可能性理论的发电公司最优报价策略.电力系统自动化. 2002, 26(23): 12~17
54张维迎.博弈论与信息经济学.上海人民出版社, 1996: 145~167
55刁勤华,林济铿,倪以信等.博弈论及其在电力市场中的应用.电力系统自动化. 2001, 25(1): 19~23
56尚金城,黄光皓,张维存等.一种基于博弈论的发电商竞价策略模型与算法.电力系统自动化. 2002, 26(9): 11~14
57 H. Song, C. C. Liu, J. Lawarree. Nash equilibrium bidding strategies in a bilateral. IEEE Transactions on Power Systems. 2002, 17(1): 73~79
58 B. F. Hobbs. Linear Complementarity Model of Nash-Cournot Competition in Bilateral and POOLCO Power Market. IEEE Transactions on Power Systems. 2001, 16(2): 194~202
59 J. Contreras et al. Numerical solutions to Nash-Cournot equilibria in coupled constraint electricity markets. IEEE Transactions on Power Systems. 2004, 19(1): 195~206
60 D. Chattopadhyay. Multicommodity Spatial Cournot Model for Generator Bidding Analysis. IEEE Transactions on Power Systems. 2004, 19(1): 267~275
61 A. R. Kian, J. B. Cruz, R. J. Thomas. Bidding strategies in oligopolistic dynamic electricity double-sided auctions. IEEE Transactions on Power Systems. 2005, 20(1): 50~58
62武智勇,康重庆,夏清等.基于博弈论的发电商报价策略.电力系统自动化. 2002, 26(9): 7~11
63郭家春,何光宇,王稹等.发电公司报价决策行为的分析.电力系统自动化. 2002, 26(5): 5~8
64任玉珑,邹小燕,张新华.发电公司的不完全信息竞价博弈模型.电力系统自动化. 2003, 27(9): 11~14
65 R. Baldick. Electricity market equilibrium models: the effect of parameterization. IEEE Transactions on Power Systems. 2002, 17(4): 1170~1176
66 C. J. Day et al. Oligopolistic competition in power networks: a conjectured supply function approach. IEEE Transactions Power Systems. 2002, 17(3): 597~607
67 H. Niu, R. Baldick, G. D. Zhu. Supply function equilibrium bidding strategies with fixed forward contracts. IEEE Transactions on Power Systems. 2005, 20(4): 1859~1867
68 T. Li, M. Shahidehpour. Strategic bidding of transmission-constrained GENCOs with incomplete information. IEEE Transactions on Power Systems. 2005, 20(1): 437~447
69 P. Couchman, B. Kouvaritakis, M. Cannon, F. Prashad. Gaming strategy for electric power with random demand. IEEE Transactions on Power Systems. 2005, 20(3): 1283~1292
70 R. Green, D. M. Newbery. Competition in Electricity Spot Market. Journal of Political Economy. 1992, 100(5): 929~953
71 R. Wang, J. C. Shang, X. Y. Zhou, Y. C. Zhang. Risk assessment andmanagement of portfolio optimization for power plants. IEEE/PES Transmission and Distribution Conference & Exhibition: Asia and Pacific, Dalian(China), 2005: 1~6
72 R. Bjorgan, C. C. Liu, J. Lawarree. Financial Risk Management in a Competitive Electricity Market. IEEE Transactions on Power Systems. 1999, 14(4): 1285~1291
73 T. Bjorkvoll. Power generation on planning and risk management in a liberalized market. Proceedings of IEEE Porto Power Tech Conference, Porto(Portugal), 2001: 6~10
74 D. J. Wu. Optimal bidding and contracting strategies in the deregulated electric power market: Part I. Proceedings of the 33rd Annual Havaii International Conference on System Sciences, Havaii(USA), 2000: 1235~1240
75 M. A. Plazas, A. J. Conejo, F. J. Prieto. Multimarket optimal bidding for a power producer. IEEE Transactions on Power Systems. 2005, 20(4): 2041~2050
76任震,黄福权,黄雯莹.电力市场中的发电厂投标组合策略.电力系统自动化. 2002, 26(2): 14~17
77刘亚安,薛禹胜,管晓宏. Price-taker在两个电力市场中的交易决策(二)发电商的策略.电力系统自动化. 2004, 28(17): 12~15
78曾次玲,张步涵,谢培元.基于风险管理在开放的能量市场和备用市场间优化分配发电容量.电网技术. 2004, 28(13): 70~74
79吕泉,武亚光,李卫东.考虑预期利润目标的发电商多交易市场发电量分配策略.电力系统自动化. 2006, 30(9): 29~33
80张显,王锡凡,王建学等.发电商长期电能分配策略研究.中国电机工程学报. 2005, 25(1): 6~12
81王壬,尚金成,冯旸等.基于CVaR风险计量指标的发电商投标组合策略及模型.电力系统自动化. 2005, 29(14): 5~9
82洪雁,邵全,吴祈宗.模糊机会约束规划下的投资组合模型研究.数量经济技术经济研究. 2005, (9): 112~118
83 K. K. Lai, S. Y. Wang, J. P. Xu et al. A Class of Linear Interval Programming Problems and Its Application to Portfolio Selection. IEEE Transactions on Fuzzy Systems. 2002, 10(6): 698~706
84 Y. Y. Hong, S. W. Tsai, M. T. Weng. Bidding strategy based on artificial intelligence for a competitive electric market. IEE Proceedings: Generation, Transmission and Distribution. 2001, 148(2): 159~164
85 W. Mielezarski, G. Michalik, M. Widjaja. Bidding strategies in electricity market. Proceedings of the 21st 1999 IEEE International Conference, Santa Clara(Canada), 1999: 71~76
86刘梅招,杨莉,甘德强.基于Agent的电力市场仿真研究综述.电网技术. 2005, 29(4): 76~80
87 D. W. Bunn, F. S. Oliveira. Agent-based simulation-An application to the new electricity trading arrangements of England and Wales. IEEE Transactions on Evolutionary Computation. 2001, 5(5): 493~503
88康重庆,江健健,夏清.基于智能个体信念学习的电力市场模拟的理论框架.电网技术. 2005, 29(12): 10~15
89 M. Ventosa, A. Baillo, A. Ramos et al. Electricity market modeling rends. Energy Policy. 2005, 33(7): 897~913
90 G. Xiong, T. Hashiyama, S. Okuma. An electricity supplier bidding strategy through Q-learning. Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, Chicago(USA), 2002: 1516~1521
91 I. Watanabe, K. Okada, K. Tokoro et al. Adaptive multiagent model of electric power market with congestion management. Proceedings of 2002 World Congress on Computational Intelligence-WCCI'02, Honolulu(USA), 2002: 12~17
92 A. K. David, F. S. Wen. Market Power in Electricity Supply. IEEE Transactions on Power Systems. 2001, 16(4): 352~360
93 A. F. Rahimi, A. Y. Shefrin. Effective Market Monitoring in Deregulated Electricity Markets. IEEE Transactions on Power Systems. 2003, 18(2): 486~493
94张集,张粒子,于亮.电力市场中的市场力分层评价指标体系.中国电机工程学报. 2006, 26(6): 123~128
95 D. Gan, D. V. Bourcier. A Simple Method for Locational Market Power Screening. IEEE Power Engineering Society Winter Meeting, New York(USA), 2002: 434~439
96 P. Wang, Y. Xiao, Y. Ding. Nodal Market Power Assessment in ElectricityMarkets. IEEE Transactions on Power Systems. 2004, 19(3): 1373~1379
97马歆,侯志俭,蒋传文.电力市场中市场势力的影响分析.电力自动化设备. 2004, 24(2): 38~41
98赵建国,韩学山,程时杰.电力市场中考虑发电机组功率速度约束的市场力分析.电网技术. 2003, 27(11): 43~48
99杨淼锋,刘俊勇,刘继春.市场力指标组态作用下的发电企业报价带研究.现代电力. 2005, 22(2): 71~76
100陈云华.基于综合指标法的发电企业市场竞争力分析.水力发电学报. 2006, 25(5): 14~17
101 J. Kennedy, R. Eberhart. A discrete binary version of the particle swarm algorithm. IEEE International Conference on computational Cybernetics and Simulation, Orlando(USA), 1997: 4104~4108
102刘涌,侯志俭,蒋传文.求解机组组合问题的改进离散粒子群算法.电力系统自动化. 2006, 30(4): 35~39
103 S. A. Kazarlis, A. G. Bakirtzis, V. Petridis. A genetic algorithm solution to the unit commitment problem. IEEE Transactions on Power Systems. 1996, 11(1): 83~92
104 R. Baldick, R. Grant, E. Kahn. Theory and application of linear supply function equilibrium in electricity markets. Journal of Regulatory Economics. 2004, 25(2): 143~167
105 B. F. Hobbs, B. Carolyn, J. S. Pang. Strategic gaming analysis for electric power systems: an MPEC approach. IEEE Transactions on Power Systems. 2000, 15(2): 638~645
106 J. Bialek. Tracing the flow of electricity. IEE Proceedings: Generation, Transmission and Distribution. 1996, 143(4): 313~320.