考虑调峰因素的风电规划研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
能源是人类社会赖以生存和发展的重要物质基础。能源的可持续发展是经济社会可持续发展的前提条件。随着煤炭、石油、天然气等化石能源的日趋枯竭以及环境问题日益突出,能源的可持续性面临巨大挑战。大力发展可再生能源成为人类的必然选择。风能因资源丰富,分布广泛,清洁无污染等优点成为大力开发利用的可再生能源之一。
     进入21世纪以来,将风能转化为电能的风力发电技术持续快速发展,风电装机容量逐年提高,我国以及欧美许多国家均提出了宏大的风电发展规划目标。但风电出力具有明显的波动性和不确定性,风电大规模并网给电网运行带来诸多问题,主要有调峰、调频、调压、稳定及电能质量等问题。我国电源结构以燃煤火电为主,调峰电源较为匮乏。风电大规模并网加剧了匮乏程度,使调峰问题凸显。风电调峰问题解决的好坏直接关系到风电发展规划目标的实现。
     本文主要研究与调峰密切相关的风电规划问题,分别对含大规模风电的系统调峰充裕性的概率性指标和容量性指标、考虑风电调峰要求的系统随机生产模拟,以及考虑风电调峰要求的机组检修计划进行了研究。主要研究内容如下:
     (1)分析了电源结构对风电并网后系统调峰的影响,全面概括总结了目前风电并网后的系统调峰问题研究现状以及含风电的电力系统规划研究现状,主要包括含风电的可靠性、随机生产模拟以及机组检修计划的研究现状。
     (2)分析了风速的变化特性,总结提出了风速时间序列建模原则和方法。采取基于各月同一钟点对风速进行统计的方法,有效计及风速的季节特性和日时间尺度特性;采用时间序列回归模型考虑风速波动的自相关特性,提出了基于概率测度变换的风速时间序列建模方法,避免基于回归模型的风速模拟过程中出现负风速问题;基于概率测度变换方法,采用自回归滑动平均模型(ARMA)及向量自回归模型(VAR)模型,对单一风电场及多风电场风速进行时间序列建模。
     (3)提出了含大规模风电的电力系统调峰充裕性评估方法。基于序贯蒙特卡罗方法以日为单位考察调峰问题,提出了调峰充裕性概率性指标——调峰不足概率,将调峰充裕性指标处理为发电充裕时的条件概率,分析了风电预测精度对调峰不足概率指标的影响,对比了发电不足概率与调峰不足概率,探讨了风电并网容量、长期基荷运行机组、风电-负荷相关性、风电场风速相关性以及地区电网外送功率规模对系统调峰不足概率指标的影响。
     (4)提出了系统调峰充裕性容量性指标评估方法,包括基于简化牛顿迭代法的储能调整需求容量评估方法及外送调整需求容量评估方法,有效弥补了调峰不足概率指标操作性的不足,分析了风电不同时间尺度的相关性等因素对调峰充裕性容量指标的影响。
     (5)基于序贯蒙特卡罗方法建立了考虑风电调峰要求的电力系统随机生产模拟方法,提出了弃风功率损失期望指标,分析了日前风电预测精度、风电并网容量水平等因素对系统发电成本、常规机组利用小时数和弃风功率损失期望的影响。
     (6)基于Benders分解法建立了考虑风电调峰要求的电力系统机组检修安排模型,将考虑带有序贯性特点调峰要求的检修问题分解为满足峰荷能力和系统最小出力容量在各个检修时段分布是否合理的问题;修正了基于负荷持续曲线的传统随机生产模拟模型,用虚拟弃风量来表征各个周基荷容量的大小;以虚拟弃风量和系统检修成本最小为目标函数,以各个检修时段发电可靠性为约束条件,通过Benders方法进行分解协调,实现了对电力系统常规机组检修安排。
     (7)通过IEEE-RTS79系统的仿真算例验证了所提方法的可行性与有效性,并以风电规划并网规模较大的通辽电网为背景,建立工程算例,进一步验证论文提出的模型和方法的实际应用价值。
Energy is important material basis for the survival and development of human society. Sustainable energy development is a prerequisite for sustainable economic and social development. With coal, oil, natural gas and other fossil energy resources depleting and environmental issues becoming increasingly prominent, the sustainability of energy is facing enormous challenges. It has become an inevitable choice of mankind to develop renewable energy vigorously. Wind energy becomes the focus of attention, for its advantages of abundance, widely distribution, and free pollution.
     Since the beginning of the21st century, wind power technology, which converts wind energy to electric power, has been developing rapidly. Installed capacity of wind power has increased annually, meanwhile, great wind power development and planing has been put forward in many countries including China. However, concerning its power fluctuation and uncertainty, large-scale wind power integration has great impacts upon the power system, such as peak-shaving, frequency regulating and voltage regulating issues, stability, power quality and so on. The peak-shaving power resources of China are originally scarce for the power generation structure is mainly based on coal-fired power. With large-scale wind power integration, the scarcity is becoming highlighted. The wind power peak-shaving issues are directly related to the realization of wind power development and planning objectives, especially in China.
     The research, in this dissertation, is mainly about wind power planning closely related with the peaking, including peak-shaving adequacy probability and capacity indices, probabilistic production simulation and generator maintenance scheduling which can consider the requirement of peak-shaving of wind power integrated systems. The main contents are as follows:
     (1) The impacts of the power source structure in China on peak-shaving capacity of wind power integrated system are analyzed. The research status of peak-shaving problems and planning problems of wind power integrated systems is summarized.
     (2) Based on the analysis of variation characteristics of wind, wind speed time series modeling principles are proposed. The actual sampling wind speed series and the time-series of regression analysis models are connected by probability measure transformation in order to avoid negative wind speed in regressive models. Based on the probability measure transformation methods, the models of autoregressive moving average (ARMA) and vector autoregressive (VAR) are used separately to build wind speed time-series models for single wind farm and multi-wind farms.
     (3) A peak-shaving adequacy evaluation method associated with large-scale wind power integrated systems is developed. It is based on sequential Monte-Carlo method, using reliability theory. Peak-shaving capacity insufficient probability index and its calculation method are proposed. Peak-shaving adequacy evaluation of IEEE-RTS system demonstrates that the proposed method is feasible and effective.
     (4) In order to increase the index operability, the peak-shaving capacity index evaluation method is developed. It is based on capacity credit evaluation theory. Two indexes of regulating requirement capacity of peak-valley difference and regulating requirement capacity for transmission are proposed by simplified Newton iteration method. Based on the IEEE-RTS system, the proposed method is verified to be effective.
     (5) A power system probabilistic product simulation method is built, which can consider the requirement of peak-shaving of wind power integrated systems. Based on the Monte-Carlo method, an expectation index of wind energy loss is proposed. In the probabilistic production method, day-ahead units arrangement is based on stealth enumeration method, and hydro power unit operation positions are determined by an iterative method of variable step size. Probabilistic product simulation of IEEE-RTS system demonstrates that the proposed method is feasible and effective, and the impacts of wind power prediction error and integration scale on system generation cost, unit utilization hours and loss of wind energy expectation are analyzed.
     (6) Based on Benders decomposition method, generator maintenance scheduling model is built, which can consider the requirement of peak-shaving of wind power integrated systems. The problem is decomposed to two parts:a deterministic multi-objective integer programming master problem and two sub-problems (loss of wind energy calculation and power generation reliability). The results on IEEE RTS system with wind farms demonstrate that the proposed method is feasible and effective.
     (7) An engineering example of Tongliao grid is built to verify the above methods further.
引文
[1]中华人民共和国国家发展和改革委员会.可再生能源发展“十二五”规划[R].北京:中华人民共和国国家发展和改革委员会,2011.
    [2]中华人民共和国国家发展和改革委员会能源研究所.中国风电发展路线图2050[R].北京:中华人民共和国国家发展和改革委员会,2011.
    [3]肖创英.欧美风电发展的经验与启示[M].北京:中国电力出版社,2010.
    [4]程路,白建华,贾德香,等.国外风电并网特点及对我国的启示[J].中外能源,2011,16(6):30-34.
    [5]U.S. Department of Energy.2010 Renewable Energy Data Book[R]. http://www.nrel.gov/analysis/.2011.
    [6]张宁,周天睿,段长刚,等.大规模风电接入对电力系统调峰的影响[J].电网技术,2010,34(1):152-158.
    [7]李秀芬,张建成,迟永宁.内蒙古风电发展存在的问题及解决方案分析[J].内蒙古电力技术.2010,28(5):1-4.
    [8]林章岁,罗利群.福建省风电出力特性及其对电网的影响分析[J].电力建设.2011,32(12):18-23.
    [9]王秀强.电网调峰能力不足约束风电发展[J].中国新能源.2011,4:28-28.
    [10]乔颖,鲁宗相.考虑电网约束的风电场自动有功控制[J].电力系统自动化.2009,33(22):88-93.
    [11]韩小琪,孙寿广,戚庆茹.从系统调峰角度评估电网接纳风电能力[J].中国电力.2010,43(6):16-19.
    [12]魏磊,张琳,姜宁,等.包含风电的电力系统调峰能力的计算方法探讨[J].电网与清洁能源.2010,26(8):59-63.
    [13]杨宏,刘建新,苑津莎.风电系统中常规机组负调峰能力研究[J].中国电机工程学报[J].2010,30(16):26-31.
    [14]吴功高,叶中雄,姚明,等.安徽电网接纳风电能力的分析研究[J].华东电力.2011,39(6):97-99.
    [15]衣立东,朱敏奕,魏磊,等.风电并网后西北电网调峰能力的计算方法[J].电网技术.2010,34(2):129-132.
    [16]侯佑华,房大中,白永祥,等.大规模风电运行的调度模式设计[J].中国电力.2010,43(8):67-72.
    [17]郑太一,冯利民,王绍然,等.一种计及电网安全约束的风电优化调度方法[J].电力系统自动化.2010,34(15):71-74.
    [18]李付强,王彬,涂少良,等.京津唐电网风力发电并网调峰特性分析[J].电网技术.2009,33(18):128-132.
    [19]杨建设,张佳丽.锡盟“风火打捆”风电运行方式研究[J].风能.2011,07:36-38.
    [20]Angarta J M, Usaola J G. Combining hydro-generation and wind energy:biding and operation on electricity spot markets[J]. Electric Power System Research.2011,77(5-6): 393-400.
    [21]Castronuovo E D, Lopes J A P. On the optimization of the daily operation of a wind-hydro power plant[J]. IEEE Trans on Power Systems.2004,19(3):1599-1606.
    [22]静铁岩,吕泉,郭琳,等.水电-风电系统日间联合调峰运行策略[J].电力系统自动化.2011,35(22):97-104.
    [23]陈启鑫,康重庆,夏清.碳捕集电厂的运行机制研究和调峰效益分析[J].中国电机工程学报.2010,30(7):22-28.
    [24]白建华,辛颂旭,贾德香,等.中国风电开发消纳及输送相关重大问题研究[J].电网与清洁能源.2010,26(1):14-17.
    [25]刘德伟,黄越辉,王伟胜.考虑调峰和电网输送约束的省级系统风电消纳能力分析[J].电力系统自动化.2011,35(22):77-81.
    [26]靳丹,丁坤,何世恩.丹麦风电调峰调频机制探讨及对中国的启示[J].电力科技与环保.2011,27(4):50-53.
    [27]静铁岩,吕泉,王海霞.风电参与的非常规调峰服务市场交易理论[J].中国电力.2011,44(10):29-32.
    [28]徐玮,杨玉林,李政光.甘肃酒泉大规模风电参与电力市场模式及其消纳方案[J].电网技术.2010,34(6):71-77.
    [29]胡剑琛,刘燕华,李献,等.风电并网后电网调峰措施的经济性分析[J].现代电力.2012,29(1):86-89.
    [30]葛炬,王飞,张粒子.含风电场电力系统旋转备用获取模型[J].电力系统自动化.2010,34(6):32-36.
    [31]袁铁江,晁勤,吐尔逊伊不拉音.面向电力市场的含风电电力系统的环境经济调度优化[J].电网技术.2009,33(20):13 1-135.
    [32]吴栋梁,王杨,郭创新,等.电力市场环境下考虑风电预测误差的经济调度模型[J].电力系统自动化.2012,36(6):23-28.
    [33]王锡凡.电力系统优化规划[M].北京:水利电力出版社,1990.
    [34]郭永基.电力系统可靠性分析[M].北京:清华大学出版社,2003.
    [35]杨莳百.发电系统可靠性分析原理和方法[M].北京:水利电力出版社,1985.
    [36]Maurice K, Alan S. The Advanced Theory of Statistic Volume 1, Macillan, USA,1977.
    [37]Chen R, Liu J S, Wang X. Convergence analyses and comparisons of Markov Chain Monte Carlo algorithm in digital communication. IEEE Trans. Signal Processing,2002,50:255-270.
    [38]Mckay M D, Beckman R J, Conover W J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code[J]. Technometrics,1979, 21(2):239-245.
    [39]吴义纯,丁明.风电可靠性评估[J].中国电力.2004,37(5):81-84.
    [40]徐玉琴,吴颖超.考虑风力发电影响的配电网可靠性评估[J].电网技术.2011,35(4):154-158.
    [41]Maliki Guindo, Wang Cheng-shan. Reliability analysis on the integration of wind/PV hybrid distributed generation in distribution system[J]. 电力系统自动化.2005,29(23):33-39.
    [42]吴义纯,丁明.基于蒙特卡罗仿真的风力发电系统可靠性评价[J].电力自动化设备.2004,24(12):70-73.
    [43]石文辉,陈静,王伟胜.含风电场的互联发电系统可靠性评估[J].电网技术备.2012,,36(2):224-230.
    [44]华文,徐政.包含风电场的发电系统可靠性研究[J].高压电器.2010,46(12):36-40.
    [45]李玉敦,谢开贵.含多个风电场的电力系统可靠性评估[J].电力科学与技术学报.2011,26(1):73-77.
    [46]Billinton R,Bai G.Generating capacity adequacy associated with wind energy[J].IEEETransactions on Energy Conversion,2004,19(3):641-646.
    [47]Kaigui Xie, Roy Billinton. Considering wind speed correlation of WECS in reliability evaluation using the time-shifting technique[J]. Electric Power System Research,2009,79(4): 687-693.
    [48]M. S. Miranda, R. W. Dunn. Spatially correlated wind speed modeling for generation adequacy studies in the UK[C]. Power Engineering Society General Meeting,2007.
    [49]R Billiton, H Chen, and R Ghajar. Time-series models for reliability evaluation of power systems including wind energy [J]. Microelectron Reliab,1996,36(9):1253-1261.
    [50]Rajesh Karki, Po Hu. Wind power simulation model for reliability evaluation[C]. Canadian Conference on Electrical and Computer Engineering, Sask,2005.
    [51]Kostas Philippopoulos, Despina Deligiorgi. Statistical simulation of wind speed in Athens Greece based on Weibull and ARMA models[J]. Intenational Journal of Energy and Evironment,2009,4(3); 151-158.
    [52]F Wang, D Y Liu, L H Zeng, et al. Study on the wind speed frequency distribution with AR-GARCH Model[C]. SUPERGEN'09 International Conference on Sustainable Power Generation and Supply, Nanjing,2009.
    [53]Alicja Lojowska, Dorota Kurowicka, Georgios Papaefthmiou. Advantages of ARMA-GARCh wind speed time series modeling[C]. The 11th International Conference on Probabilistic Methods to Power Systems (PMAPS). Singapore 2010.
    [54]Bo Martin Bibby, IB Michael Skovgaard, Michael Sqrensen. Diffusion-type models with given marginal distribution and autocorrelation fuction[J]. Bernoulli,2005,11(2):191-220
    [55]ZHANG Ning, KANG Chongqing, DUAN Changgang, et al. Simulation Methodology of Multiple Wind Farms Operation Considering Wind Speed Correlation [C]. The Third IASTED Asian Conference on Power and Energy Systems, Beijing,2009.
    [56]王海超,鲁宗相,周双喜.风电场发电可信容量度研究[J].中国电机工程学报.2005,22(10):103-106.
    [57]陈树勇,戴慧珠,白晓民,等.风电场的容量可信度和可避免费用计算[J].太阳能学报.1999,20(4):432-438.
    [58]张宁,康重庆,陈治坪,等.基于序列运算的风电可信容量计算方法[J].中国电机工 程学报.2011,31(25):1-9.
    [59]Michael Milligan, Brian Parsons. A comparison and case study of capacity credit algorithms for intermittent generators[J]. Solar 97, Washington DC,1997.
    [60]Castro R, Ferreira L A. Comparison between chronological and probabilistic methods to estimate wind power capacity credit[J]. IEEE Transactions on Power Systems,2001,16(4): 904-909.
    [61]Michael Milligan. Modeling utility-scale wind power plants part 2:capacity credit [R]. Golden:National Renewable Energy Laboratory(NREL),2002.
    [62]Claudine D, Annunzio, Surya Santoso. Noniterative method to approximate the effective load carrying capability of a wind plant power[J]. IEEE Transactions on Energy Conversion, 2008,23(2).-544-550.
    [63]Soder L, Amelin M. A review of different methodologies used for calculation of wind power capacity credit[J]. Power and Energy Society General Meeting, Pittsburgh, America,2008.
    [64]Mikael Amelin. Comparison of capacity credit calculation methods for conventional power plants and wind power[J]. IEEE Transactions on Power Systems,2009,24(2):685-691.
    [65]Booth R R. Power system simulation model based on probability analysis[J]. IEEE Transactions on Power Systems.1972.91 (1):62-69.
    [66]Schenk K F, Misra R B, Vassos S, et al. A new method for the evaluating of expected energy generation and loss of probability [J]. IEEE Transactions on Power Systems,1984,103(2): 294-303.
    [67]Wang X. Equivalent energy function approach to power system probabilistic modeling[J]. IEEE Transactions on Power Systems.1988,3(3),823-829.
    [68]王锡凡.电力系统随机生产模拟的等效电量函数法[J].西安交通大学学报.1984,18(6):13-26.
    [69]李林川,王锡凡,王秀丽.基于等效电量函数法的互联电力系统随机生产模拟[J].中国电机工程学报.1996,16(3):180-184.
    [70]Ran N S, Toy P, Schenk K F. Expected energy production costs by the method of moments[J]. IEEE Transactions on Power Apparatus and Systems,1980,99(5):1908-1917.
    [71]Stremel J P, Jenkins R T, Babb R A, et al. Production costing using the cumulant method of representing the equivalent load curve [J]. IEEE Transactions on Power Apparatus and Systems,1980,99(5):1947-1956.
    [72]陈刚,相年德,陈雪青.一种基于负荷分解的随机生产模拟新方法[J].中国电机工程学报.1992.12(3):47-52.
    [73]夏清,王少军,相年德.时序负荷曲线下电力系统概率性生产模拟[J].中国电机工程学报.1994,14(3):21-28.
    [74]夏清,王少军,相年德,等.时序负荷曲线下电力系统概率性水电生产模拟[J].中国电机工程学报.1998,18(6):429-433.
    [75]康重庆,白利超,夏清,等.基于序列运算理论的随机生产模拟算法的实施[J].中国 电机工程学报.2002,22(9):6-11.
    [76]陈树勇,戴慧珠,白晓民,等含风电场的电力系统随机生产模拟[J].中国电力,2000,33(3):30-31,69.
    [77]张节潭,程浩忠,胡泽春,等.含风电场的电力系统随机生产模拟[J].中国电机工程学报.2009,29(28):34-39.
    [78]冯长有,王锡凡.电力市场下的发电机组检修模型[J].电力系统自动化.2007,31(5):97-103.
    [79]丁明,冯永青.发电机组检修计划的可靠性和经济性研究[J].中国电力,2001,34(7):22-25.
    [80]吴龙,黄民翔.电力系统机组计划检修新模型[J].电力系统自动化,1998,22(2):26-28.
    [81]BURKE E K, MAHMOUI A J. Hybrid evolutionary techniques for the maintenance scheduling problem[J]. IEEE Trans on Power Systems,2000,15(1):122-128.
    [82]Wang Yaoyu, HANDSCHIN E. Unit maintenance scheduling in open systems using genetic algorithm[J]. Proceedings of 1999 IEEE Transaction and Distribution Conference:Vol 1, 1999, New Orleans, LA, USA.
    [83]KIM H, HAYASHI Y, NARA K. An algorithm for thermal unit maintenance scheduling through combined use of GA SA and TS[J]. IEEE Trans on Power Systems,1997, 12(1):329-335.
    [84]SILVA E L, SCHILING M T. Generation maintenance scheduling considering transmission constraints[J]. IEEE Trans on Power Systems,2000,15(2):838-843.
    [85]MARWALL M K C, SHAHIDEHPOUR S M. Integrated generation and transmission maintenance scheduling with network constraints. IEEE Trans on Power Systems, 1998,13(3):1063-1068
    [86]张节潭,王茂春,徐有蕊,等.采用最小积累风险度法的含风电场电力系统发电机组检修计划[J].电网技术,2011,35(5):97-102.
    [87]苏运,朱耀明,张节潭,等.考虑电力系统不确定性的机组检修计划安排[J].水电能源科学,2011,29(5),152-155.
    [88]方陈,夏清,孙欣.考虑大规模风电接入的发电机组检修计划[J].电力系统自动化,2010,34(19):20-24,74.
    [89]MICHEL D. Use of a stochastic process to sample wind curves in planning studies [C]. Proceeding of 2007 IEEE Power Tech Conference. Lausanne,2007:663-670.
    [90]P Chen, T Pederson, B Bak-Jensen and Z Chen. ARIMA-based time series model of stochastic wind power generation[J]. IEEE Trans on Power Systems,2010,25(2):667-676.
    [91]Giorsetto P, Utsurogi K F. Development of a New Procedure for Reliability Modeling of Wind Turbine Generators[J].IEEE Transactions on Power System,1983,102(1):134-143.
    [92]Pallabazzer R. Evaluation of Wind-generator potentiality [J].Solar Energy,1995,55 (11) 49-59.
    [93]Feijoo A E, Cidras J, Domelas J L G. Wind speed simulation in wind farms for steady-state security assessment of electrical power systems[J]. IEEE Transactions on Energy Conversion,1999,14(4):1582-1588.
    [94]SANDERHOFF P. PARK:user's guider [M]. Roskile, Denmark:Riso National Laboratory,1993.
    [95]李辉.风电场风速和输出功率的多尺度预测研究[D].兰州理工大学,2010.
    [96]雷亚洲,王伟胜,印永华,等.基于机会约束规划的风电穿透功率极限计算[J].中国电机工程学报,2002,22(5):32-35.
    [97]雷亚洲,王伟胜,印永华,等.含风电场电力系统的有功优化潮流[J].电网技术,2002,26(6):18-21.
    [98]Jangamshetti Suresh H, Rau V. Optimum siting of wind turbine generators[J]. IEEE trans on Energy Conversion,2001,16(1):8-13.
    [99]Jangamshetti S H, Rau V G. Site matching of wind turbine generators:a case study[J]. IEEE Trans on Energy Conversion,1999,14(4):1537-1543.
    [100]Garcia A, Torres J L, Prieto E, Francisco A. Fitting wind speed distributions:A case study. Solar Energy,62(2):139-144.
    [101]梁双,胡学浩,张东霞,等.考虑风速变化特性的风电容量可信度评估方法[J].中国电机工程学报,2012
    [102]M. S. Miranda, R. W. Dunn. Spatially correlated wind speed modeling for generation adequacy studies in the UK[C]. Power Engineering Society General Meeting,2007.
    [103]R Billiton, H Chen, and R Ghajar. Time-series models for reliability evaluation of power systems including wind energy [J]. MicroelectronReliab,1996,36(9):1253-1261.
    [104]Kostas Philippopoulos, Despina Deligiorgi. Statistical simulation of wind speed in Athens Greece based on Weibull and ARMA models[J]. Intenational Journal of Energy and Evironment,2009,4(3); 151-158.
    [105]F Wang, D Y Liu, L H Zeng, et al. Study on the wind speed frequency distribution with AR-GARCH Model[C]. SUPERGEN'09 International Conference on Sustainable Power Generation and Supply, Nanjing,2009.
    [106]Alicja Lojowska, Dorota Kurowicka, Georgios Papaefthmiou. Advantages of ARMA-GARCh wind speed time series modeling[C]. The 11th International Conference on Probabilistic Methods to Power Systems (PMAPS). Singapore 2010.
    [107]Rajesh Karki, Po Hu. Wind power simulation model for reliability evaluation[C]. Canadian Conference on Electrical and Computer Engineering, Sask,2005.
    [108]Bo Martin Bibby, IB Michael Skovgaard, Michael Sqrensen. Diffusion-type models with given marginal distribution and autocorrelation fuction[J]. Bernoulli,2005,11(2):191-220
    [109]ZHANG Ning, KANG Chongqing, DUAN Changgang, et al. Simulation Methodology of Multiple Wind Farms Operation Considering Wind Speed Correlation [C]. The Third IASTED Asian Conference on Power and Energy Systems, Beijing,2009.
    [110]现代数学手册编纂委员会.现代数学手册-随机数学卷[M],武汉:华中科技大学出版社,2000.
    [111]高铁梅.计量经济分析方法与建模[M],北京:清华大学出版社,2009.
    [112]W. Wangdee, R. Billinton. Considering load-carrying capability and wind speed correlation of WECS in generation adequacy assessment[J].IEEE Transactions on Energy Conversion. 2006,21(3):734-741.
    [113]Kaigui Xie, Roy Billinton. Considering wind speed correlation of WECS in reliability evaluation using the time-shifting technique[J]. Electric Power System Research,2009,79(4): 687-693.
    [114]曹坊,张粒子.结合系统调峰容量比确定抽水蓄能机组装机容量的方法[J].电力自动化设备,2007,27(6):47-50.
    [115]The Reliability Test System Task Force of the Application of Probability Methods Subcommittee. IEEE RELIABILITY TEST SYSTEM [J]. IEEE Transactions on Power Apparatus and Systems,1979,98(6):2047-2054.
    [116]李文沅.电力系统风险评估模型、方法和应用[M].北京:科学出版社,2006.
    [117]丁明,罗初田.电力系统可靠性指标的灵敏度分析[J].合肥工业大学学报:自然科学版,1990,13(3):72-81.
    [118]冯永青,张伯明,吴文传,等.电力市场发电机检修计划的快速算法[J].电力系统自动化,2004,28(16):41-44.
    [119]国家电力监管委员会电力可靠性管理中心.2009年全国电力可靠性指标[R].http://www.chinaer.org/
    [120]国家电力监管委员会电力可靠性管理中心.2010年全国电力可靠性指标[R].http://www.chinaer.org/
    [121]国家电力监管委员会电力可靠性管理中心.2011年全国电力可靠性指标[R].http://www.chinaer.org/
    [122]姚金雄,张世强.基于调峰裕度分析的榆林电网风电接纳能力[J].电力建设.2011,32(2):24-28

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700