智能电网中多种发电模式联合调度模型及效益评价研究
|
摘要
随着智能电网的发展和节能减排要求的提高,清洁能源发电比重正不断提高。风力发电和太阳能发电具有发电随机性强、可调节能力弱,地域性强、集中度高等特点,提高我国电网对清洁能源接入的适应性,寻求技术经济效益最优的解决策略,加强智能电网建设被认为是解决风电和光电上网问题的关键。
本文提出的智能电网中多种发电模式联合调度模型及效益评价研究,通过分析清洁能源发电的必要性、智能电网的要求、大规模清洁能源发电并网的优势和问题,提出清洁能源和传统能源等多种发电模式联合调度的方式,对于我国节能减排目标的实现、电网安全稳定运行、电网和发电企业经济效益的提高具有积极的作用。
在大量数据资料分析整理的基础上,系统分析我国电源结构与电网的发展现状及面临的问题,研究智能电网的提出对电源和电网的影响,同时对本文主要研究的几种发电模式,如火力发电、风力发电、太阳能光伏发电等,进行技术经济对比分析,为电力预测、联合调度及效益评价做铺垫；针对风力发电和太阳能光伏发电不稳定性的特点,分别提出了服务于联合调度的基于GABP神经网络的预测模型进行风电输出功率的预测,服务于联合调度基于拟境知识挖掘的自适应神经网络预测模型进行光伏发电输出功率的预测,最后提出了服务于联合调度基于FHNN相似日聚类的组合预测模型进行智能电网电力负荷预测研究；根据我国节能减排的要求,结合风火电联合运营的特点,建立了智能电网中风火电联合运营节能减排调度模型,提出了基于KKT和量子遗传算法的多目标决策智能算法进行模型的求解；根据风力发电和太阳能光伏发电的特点,探讨风光互补混合供电系统模式,建立了风光互补智能调度模型,提出了改进的混沌免疫遗传算法进行模型的求解；综合分析火力发电、风力发电和太阳能光伏发电的特点,建立了一套火、风、光联合运营的电力调度模型,提出了改进的量子粒子群优化算法进行调度模型的求解；综合考虑智能电网中混合电力系统联合调度的特点,从社会效益、环境效益、经济效益、安全性等几个方面进行综合分析,建立了效益评价指标体系,提出了基于Vague集和D-S证据理论的效益评价模型,进行智能电网中多种发电模式联合调度效益评价；针对以上提出的预测模型、调度模型和效益评价模型,分别进行了实证分析和验证。多种发电模式联合调度符合我国智能电网的发展方向,有利于达到节能减排的效果,通过系统深入的分析,针对目前我国多种发电模式联合调度管理中存在的问题,提出了相应的管理方案和建议。
本文的电力预测模型、调度模型和效益评价模型,对我国智能电网的发展、多种发电模式联合调度管理具有理论与实践指导借鉴意义。
With the development of smart grid and improvement of energy conservation and emissions reduction, clean energy proportion is increasing. Wind power and solar power are both stochastic and intermittent with strong regional. About how to improve access adaptability, operation flexibility and stability of clean energy and to seek optimal economic benefit strategy, strengthen construction of smart grid is considered to be one of the keys.
Through analysis of necessity of clean energy generation, requirement of smart grid, advantages and problems of large-scale grid-connected clean energy, the paper puts forward joint dispatch mode of diversified power generation. The proposed theory and application of joint dispatch of diversified power generation in smart grid has a positive role to achieve the goal of energy conservation and emissions reduction, the safe and stable operation of power grid, to improve economic benefits of electric power enterprise.
Through sort huge data resources, the paper analyzes development situation and problems of China's power structure and power grid, research influence on power supply and power grid of smart grid. At the same time, based on technical and economic comparison and analysis of mainly studied power modes, such as thermal power, wind power, solar photovoltaic power generation and so on, the paper establishes groundwork for power forecast, dispatch, and benefit evaluation. Aiming at characteristics of wind power, the paper proposes wind power output forecast model based on GABP neural network. Aiming at characteristics of photovoltaic power, the paper proposes photovoltaic power output forecast model based on adaptive neural network combined with scenario simulation knowledge mining. Finally the combination forecast model based on FNN similar day clustering for smart grid power is proposed. According to characteristics of joint wind and thermal power system, the paper establishes energy saving and emission reduction dispatch model of joint wind-thermal power operation in smart grid. Multi-objective decision-making intelligence algorithm based on KKT and quantum genetic algorithm is established to solve the model. According to characteristics of the wind power and photovoltaic power generation, hybrid wind-photovoltaic power supply system is researched and intelligent wind-photovoltaic power dispatch model is established. The improved chaos immune genetic algorithm is used to solve the model. Comprehensive analysis of thermal power, wind power and solar photovoltaic power generation, the dispatching model of joint thermal-wind-photovoltaic power operation is set up, the improved quantum particle swarm optimization algorithm is put forward for dispatch model. Considering characteristics of hybrid power system in smart grid, comprehensive evaluation index system including social benefit, environmental benefit, economic benefit and security is established. The comprehensive evaluation model based on vague set and of D-S evidence theory is proposed to evaluate comprehensive benefit of joint operations in smart grid. According to above presented forecast model, dispatch model and comprehensive evaluation model, respectively, the empirical analysis are verified. Joint dispatch of diversified power generation conforms to the developing direction of smart grid in China, and is conducive to achieve the result of energy conservation and emissions reduction. By system analysis, management solutions and suggestions about joint dispatch of diversified power generation are put forward.
The proposed power forecast model, dispatch model and benefit evaluation model have guiding significance in theory and practice aspect for development of smart grid in China, management of joint operation of diversified power generation.
本文提出的智能电网中多种发电模式联合调度模型及效益评价研究,通过分析清洁能源发电的必要性、智能电网的要求、大规模清洁能源发电并网的优势和问题,提出清洁能源和传统能源等多种发电模式联合调度的方式,对于我国节能减排目标的实现、电网安全稳定运行、电网和发电企业经济效益的提高具有积极的作用。
在大量数据资料分析整理的基础上,系统分析我国电源结构与电网的发展现状及面临的问题,研究智能电网的提出对电源和电网的影响,同时对本文主要研究的几种发电模式,如火力发电、风力发电、太阳能光伏发电等,进行技术经济对比分析,为电力预测、联合调度及效益评价做铺垫；针对风力发电和太阳能光伏发电不稳定性的特点,分别提出了服务于联合调度的基于GABP神经网络的预测模型进行风电输出功率的预测,服务于联合调度基于拟境知识挖掘的自适应神经网络预测模型进行光伏发电输出功率的预测,最后提出了服务于联合调度基于FHNN相似日聚类的组合预测模型进行智能电网电力负荷预测研究；根据我国节能减排的要求,结合风火电联合运营的特点,建立了智能电网中风火电联合运营节能减排调度模型,提出了基于KKT和量子遗传算法的多目标决策智能算法进行模型的求解；根据风力发电和太阳能光伏发电的特点,探讨风光互补混合供电系统模式,建立了风光互补智能调度模型,提出了改进的混沌免疫遗传算法进行模型的求解；综合分析火力发电、风力发电和太阳能光伏发电的特点,建立了一套火、风、光联合运营的电力调度模型,提出了改进的量子粒子群优化算法进行调度模型的求解；综合考虑智能电网中混合电力系统联合调度的特点,从社会效益、环境效益、经济效益、安全性等几个方面进行综合分析,建立了效益评价指标体系,提出了基于Vague集和D-S证据理论的效益评价模型,进行智能电网中多种发电模式联合调度效益评价；针对以上提出的预测模型、调度模型和效益评价模型,分别进行了实证分析和验证。多种发电模式联合调度符合我国智能电网的发展方向,有利于达到节能减排的效果,通过系统深入的分析,针对目前我国多种发电模式联合调度管理中存在的问题,提出了相应的管理方案和建议。
本文的电力预测模型、调度模型和效益评价模型,对我国智能电网的发展、多种发电模式联合调度管理具有理论与实践指导借鉴意义。
With the development of smart grid and improvement of energy conservation and emissions reduction, clean energy proportion is increasing. Wind power and solar power are both stochastic and intermittent with strong regional. About how to improve access adaptability, operation flexibility and stability of clean energy and to seek optimal economic benefit strategy, strengthen construction of smart grid is considered to be one of the keys.
Through analysis of necessity of clean energy generation, requirement of smart grid, advantages and problems of large-scale grid-connected clean energy, the paper puts forward joint dispatch mode of diversified power generation. The proposed theory and application of joint dispatch of diversified power generation in smart grid has a positive role to achieve the goal of energy conservation and emissions reduction, the safe and stable operation of power grid, to improve economic benefits of electric power enterprise.
Through sort huge data resources, the paper analyzes development situation and problems of China's power structure and power grid, research influence on power supply and power grid of smart grid. At the same time, based on technical and economic comparison and analysis of mainly studied power modes, such as thermal power, wind power, solar photovoltaic power generation and so on, the paper establishes groundwork for power forecast, dispatch, and benefit evaluation. Aiming at characteristics of wind power, the paper proposes wind power output forecast model based on GABP neural network. Aiming at characteristics of photovoltaic power, the paper proposes photovoltaic power output forecast model based on adaptive neural network combined with scenario simulation knowledge mining. Finally the combination forecast model based on FNN similar day clustering for smart grid power is proposed. According to characteristics of joint wind and thermal power system, the paper establishes energy saving and emission reduction dispatch model of joint wind-thermal power operation in smart grid. Multi-objective decision-making intelligence algorithm based on KKT and quantum genetic algorithm is established to solve the model. According to characteristics of the wind power and photovoltaic power generation, hybrid wind-photovoltaic power supply system is researched and intelligent wind-photovoltaic power dispatch model is established. The improved chaos immune genetic algorithm is used to solve the model. Comprehensive analysis of thermal power, wind power and solar photovoltaic power generation, the dispatching model of joint thermal-wind-photovoltaic power operation is set up, the improved quantum particle swarm optimization algorithm is put forward for dispatch model. Considering characteristics of hybrid power system in smart grid, comprehensive evaluation index system including social benefit, environmental benefit, economic benefit and security is established. The comprehensive evaluation model based on vague set and of D-S evidence theory is proposed to evaluate comprehensive benefit of joint operations in smart grid. According to above presented forecast model, dispatch model and comprehensive evaluation model, respectively, the empirical analysis are verified. Joint dispatch of diversified power generation conforms to the developing direction of smart grid in China, and is conducive to achieve the result of energy conservation and emissions reduction. By system analysis, management solutions and suggestions about joint dispatch of diversified power generation are put forward.
The proposed power forecast model, dispatch model and benefit evaluation model have guiding significance in theory and practice aspect for development of smart grid in China, management of joint operation of diversified power generation.
引文
[1]史立山.构建适应可再生能源资源特点的新型电力体系[J].电网与清洁能源,2009,25(4)：1-4.
[2]王淼,尹成竹.建构坚强智能电网,引领优质清洁能源供应新模式[J].中国电力教育,2011(33)：73-74.
[3]吴贵辉.大力发展清洁能源推进电力可持续发展[J].电网与清洁能源,2008,24(3)：1-2.
[4]谢育莉.浅议发展清洁能源,建设坚强智能电网[J].商品与质量,2010(12)：14.
[5]帅军庆.创新发展建设智能电网[J].中国电力企业管理,2009,(2)：19-21.
[6]白建华,贾德香.构筑“大型能源基地”的综合输送体系[J].能源研究观点专报,2009,(3)：5-9.
[7]阮晓琴.全球经济寒冬中的光伏产业和风电产业[J].太阳能,2009,(1)：16-17.
[8]许瑞林.江苏省可再生能源发展概况[J].能源研究与利用,2007,(6)：34-36.
[9]中投顾问产业研究中心.光伏及风电行业近期走向[J].太阳能,2011,(14)：40-41.
[10]李碧君,方勇杰,杨卫东等.光伏发电并网大电网面临的问题与对策[J].电网与清洁能源,2010,26(4)：52-59.
[11]张衡.浅谈大规模风电接入对电力系统的影响[J].宁夏电力,2011,(6)：51-56.
[12]洪翠,林维明,温步瀛等.风电场风速及风电功率预测方法研究综述[J].电网与清洁能源,2011,27(1)：60-66.
[13]王澄海,胡菊,靳双龙等.中尺度WRF模式在西北西部地区低层风场模拟中的应用和检验[J].干旱气象,2011,29(2)：161-167.
[14]袁越,李强,李群等.风电功率特性分析及其不确定性解决方案[J].电力科学与技术学报,2011,26(1)：67-72
[15]付超,朱凌,王慧等.风电场并网在线预警系统研究[J].电力系统保护与控制,2011,39(17)：64-69.
[16]王丽婕,廖晓钟,高阳,高爽.风电场发电功率的建模和预测研究综述[J].电力系统保护与控制,2009,37(13)：118-121.
[17]Pinson P, Kariniotakis G N. Wind Power Forecasting Using Fuzzy Neural Networks Enhanced with On-line Prediction Risk Assessment[C]. Power Tech Conference Proceedings 2003 IEEE. Bologna:2003.
[18]Damousis I G, Alexiadis M C, Theocharis J B, et al.A Fuzzy Model for Wind Speed Prediction and Power Generation in Wind Parks Using Spatial Correlation[J].IEEE Trans on Energy Conversion,2004,19(2):352-361.
[19]Landberg L, Prediktor:An on-line prediction system[C].Wind Power for 21st Century, EUWEC Special Topic Conference, Kassel,2000.
[20]Lange B, Rohrig K, Ernst B, et al. Wind power prediction in Germany:Recent advances and future challenges[C].European Wind Energy Conference, Athens, 2006.
[21]Zack J W, Brower M C, Bailey B H. Validating of the wind model in wind forecasting application[C]. Euwec Special Topic Conference Wind Power for the 21st Century, Kassel Germany,2000.
[22]时庆华,高山.基于ARMA和卡尔曼滤波的风电场风电功率预测研究[C].中国高等学校电力系统及其自动化专业第二十五届学术年会,2009.
[23]周晖,方江晓,黄梅等.风电功率GARCH预测模型的应用研究[J].电力系统保护与控制,2011,39(5)：108-114.
[24]李俊芳,张步涵,谢光龙等.基于灰色模型的风速-风电功率预测研究[J].电力系统保护与控制,2010,38(19)：151-159.
[25]El-Fouly T. H. M., El-Saadany E. F., Salama M. M. A. Improved Grey Predictor Rolling Models for Wind Power Prediction[J]. IEEE Proceedings on Gener, Transm & Distrib,2007,1(6):928-937.
[26]陈聪聪,王维庆.基于小波神经网络法的短期风电功率预测方法研究[J].工业控制计算机,2010,23(10)：47-48.
[27]Sideratos G, Hatziargyriou N. Using Radial Basis Neural Networks to Estimate Wind Power Production[C]. Power Engineering Society General Meeting, IEEE, 2007,1-7.
[28]黄金花,彭晖.风电场短期风电功率的神经网络方法预测研究[J].电工电气,2009,(9)：57-60.
[29]彭怀午,刘方锐,杨晓峰.基于人工神经网络的风电功率短期预测研究[J].华东电力,2009,37(11)：1918-1921.
[30]齐放.基于小波神经网络的风电功率预测[J].电气开关,2011,49(4)：18-20.
[31]武小梅,白银明,文福拴等.基于RBF神经元网络的风电功率短期预测[J].电力系统保护与控制,2011,39(15)：80-83.
[32]牛晨光,刘丛.基于相空间重构的神经网络短期风电预测模型[J].中国电力,2011,44(11)：73-77.
[33]陈国初,王鹏,徐余法等.基于小波分解的风电场短期功率混合预测模型[J].上海电机学院学报,2011,14(3)：163-168.
[34]屠强.风电功率预测技术的应用现状及运行建议[J].电网与清洁能源,2009,25(10)：4-9.
[35]王健,严干贵,宋薇等.风电功率预测技术综述[J]、东北电力大学学 报,2011,31(3)：20-24.
[36]陈宁,沙倩,汤奕,等.基于交叉熵理论的风电功率组合预测方法[J].中国电机工程学报,2012,32(4)：29-36.
[37]叶林,刘鹏.基于经验模态分解和支持向量机的短期风电功率组合预测模型[J].中国电机工程学报,2011,31(31)：102-108.
[38]叶晨.风电功率组合预测研究[D].华北电力大学,2011.
[39]Chakraborty S., Weiss M. D., Simoes M. G. Distributed intelligent energy management system for a single-phase high-frequency AC microgrid [J]. IEEE Transactions on Industrial Electronics,2007,54(1):97-109.
[40]Yona A, Senjyu T, Funabashi T. Application of recurrent neural network to Short-term-ahead generating power forecasting for photovoltaic System [C]. IEEE Power Engineering Society General Meeting,2007.
[41]Mondol J. D, Yohanis Y. G, Norton B. Comparison of measured and predicted long term performance of grid a connected photovoltaic system[J]. Energy Conversion and Management,2007,48:1065-1080.
[42]Rahman Md. H., Yamashiro S. Novel distributed Power generating system of PV-ECass using solar energy estimation[C]. IEEE Transactions on Energy Conversion,2007,22(2):358-367.
[43]Yona A, Senjyu T, Funabashi T. Application of recurrent neural network to short-term-ahead generating power forecasting for photovoltaic system[C]. IEEE Power Engineering Society General Meeting,2007.
[44]Adel Mellit, Alessan and Massi Pavan. Performance prediction of 20 kW grid-connected photovoltaic plant at Trieste (Italy) using artificial neural network[J]. Energy Conversion and Management,2010(51):2431-2441.
[45]LI Yingzi, NIU Jingcang. Forecast of power generation for grid-connected photovoltaic system based on grey model and Markov chain[C]. IEEE Conference on Industrial Electronics and Applications, Singapore,2008:1729-1733.
[46]LI Yingzi, NIU Jincang. Forecast of power generation for grid-connected photovoltaic system based on markov chain[C]. Power and Energy Engineering Conference, Wuhan, China,2009:432-435.
[47]Mellit A., Pavan A.M. A 24-h forecast of solar irradiance using artificial neural network:Application for Performance Prediction of a grid-connected PV Plant at Trieste, Italy. Solar Energy,2010,84(8):807-821.
[48]栗然,李广敏.基于支持向量机回归的光伏发电出力预测[J].中国电力,2008,41(2)：74-78.
[49]程其云.基于数据挖掘的电力短期负荷预测模型及方法的研究[D].重庆：重庆大 学,2004.
[50]赵宏伟,任震,黄雯莹.基于周期自回归模型的短期负荷预测[J].中国电机工程学报,1997,17(5)：348-351.
[51]KyungBin Song, Young-Sik Baek, Dug Hun Hong and Gilsoo Jang. Short-term load foreeasting for the holidays using fuzzy liner regression method[J].IEEE Transactions Power Systems,2005,20(1):96-101.
[52]Tomonobu Senjuy, Paras Mandal, Katsumi Uezato, and Toshihisa Funabasi. Next day load curve forecasting usingh hybrid correction method [J].IEEE Transactions Power Systems,2005,20(1):102-109.
[53]焦建林,芦晶晶.基于改进时间序列法的配电网短期负荷预测模型[J].电工技术杂志,2002,5：25-28.
[54]王晚,张少华.一种应用时间序列技术的短期电力负荷预测模型[J].上海大学学报(自然科学版),2002,8(2)：133-136.
[55]王秋梅.时间序列法负荷预测的原理和应用[J].华东电力,1993,4：37-39.
[56]J.Nowieka-Zagrajeka, R.Weron. Modeling electrieity loads in California:ARMA models with hyperbolic noise [J]. Signal Processing,2002,82(12):1903-1915.
[57]S. SP. Pappas, L. Ekonomou, D.Ch. Karamousantas, G.E. Chatzarakis, S.K.Katsikasd, P.Liatsis. Eleetricity demand loads modeling using Auto Regressive Moving Average (ARMA) models[J]. Energy,2008,33(9):1353-1360.
[58]Bonnie K. Ray. Long-range forecasting of IBM Product revenues using a seasonal fraetionally differenced ARMA model [J]. International Journal of Forecasting, 1993,9(2):255-269.
[59]牛东晓,陈志业,邢棉,谢宏.具有二重趋势性的季节型电力负荷预测组合优化灰色神经网络模型[J].中国电机工程学报,2002,22(1)：29-32.
[60]康重庆,夏清,张伯明.电力系统负荷预测研究综述与发展方向的探讨[J].电力系统自动化,2004,28(17)：1-11.
[61]Ruey-Chyn Tsaur. The development of an interval grey regression model for limited time series forecasting[J].Expert Systems with Applications,2010, 37(2):1200-1206.
[62]P.Zhou, B.W.Ang, K.L.Poh. A trigonometric grey prediction approach to forecasting electricity demand [J]. Energy,2006,31(14):2839-2847.
[63]Yen-Tseng Hsu, Ming-Chung Liu, JeromeYeh, Hui-Fen Hung. Forecasting the turning time of stock market based on Markov-Fourier grey model [J]. Expert Systems with Applications,2009,36(4):8597-8603.
[64]刘宝英,杨仁刚.基于主成分分析的最小二乘支持向量机短期负荷预测模型[J].电力自动化设备,2008,28(1)：13-17.
[65]吴杰康,陈明华,陈国通.基于PSO的模糊神经网络短期负荷预测[J].电力系统及其自动化学报,2007,19(1)：63-67.
[66]冷喜武.支持向量回归在短期负荷预测中的应用研究[D].河北：华北电力大学,2008.
[67]畅广辉,刘涤尘,熊浩.基于多分辨率SVM回归估计的短期负荷预测[J].电力系统自动化,2007,31(9)：37-41.
[68]Radwan E, Abdel-aal. Short-term hourly load forecasting using abductive network[J].IEEE Transactions Power Systems,2004,19(1):164-173.
[69]T. Senjyu, H. Takara, K. Uezato, and T. Funabashi. One-hour-ahead load foreeasting using Neural network[J].IEEE Transactions Power Systems,2002, 17(1):113-118
[70]Apostolos kotsialos, Markos Papageorgiou, Antonios Poulimenos. Long-term sales forecasting using holt-winter sand neural network methods[J] Journal of Forecasting, 2005,24(5):353-368.
[71]Bo-June Chen, Ming-Wei Chang, and Chih-Jen Lin. Load forecasting using support vector machines: a study on EUNITE Competition 2001[J].IEEE Transactions Power Systems,2004,19(4):1821-1830.
[72]董军,陈小良,张婧.计及分布式电源的电网调度模型研究[J].华东电力,2009,37(5)：723-726.
[73]张子健,邱晓燕.基于多目标决策的电网无功优化[J].贵州工业大学学报(自然科学版),2008,37(3)：80-82.
[74]王云,张伏生,陈建斌.电力系统多目标无功优化研究[J].西安交通大学学报,2008,42(2)：213-217.
[75]Jose Vallejos. Multi-objective Reactive Power Compensation Applied to the Paraguayan Power System[C]. Latin America:Trans-mission & Distribution Conference and Exposition,2006:1-6.
[76]Furong L.i Genetic Algorithms for Optimal Reactive Power Compensation on the National Grid System[J]. IEEE Transactions On PowerSystems,2005,20(1): 493-500.
[77]张增年,马昊旻.多目标决策的柔性电压控制[J].系统仿真学报,2009,21(1)：135-139.
[78]张增年,马昊.基于多目标决策的协调电压控制[J].计算机仿真,2008,25(7)：252-256.
[79]J Y Wen, Q H Wu, D R Turner, SJ Cheng, J Fitch. Optimal Coordinated Voltage Control for Power System Voltage Stability[C]. IEEE Transactions on Power Systems,2004,19(2):1115-1122.
[80]Scorsi, Mpozzi, Csabelli, and Aserrani, The Coordinated Automatic Voltage Control of the Italian transmission grid—Part Ⅰ:Reasons of the Choice and Overview of the Consolidated Hierarchical System[C]. IEEE Transactions on Power Systems,2004 19(4):1723-1732.
[81]Y Li, D J Hill, T Wu, Optimal Coordinated Voltage Control of Power Systems- An Immune Algorithm Solution[C]. Control Conference,2004,5(3):1398-1403.
[82]韦化,李滨,杭乃善等.大规模水火电力系统最优潮流的现代内点理论分析[J].中国电机工程学报,2003,23(4)：5-8.
[83]胡国强,贺仁睦.基于交互式多目标决策方法的水火电力系统日有功负荷优化分配[J].电网技术,2007,31(18)：37-42.
[84]翟桥柱,管晓宏,赖菲.具有相同机组水火电调度问题的新算法[J].中国电机工程学报,2002,22(3)：38-42.
[85]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.
[86]Basu M.An interactive fuzzy satisfying method based on evolutionary programming technique for multi objective short-term hydrothermal scheduling[J]. Electric Power Systems Research,2004,69(2-3):277-285.
[87]宋海洲.基于相对目标接近度的多目标决策方法及其应用[J].数学的实践与认识,2004,34(5)：30-36.
[88]张欣莉.基于目标满意度的交互式多目标决策改进方法[J].系统工程,2004,22(9)：10-13.
[89]黄山,张学军,郑丽英.不完全信息的多目标决策分析方法研究[J].兰州交通大学学报2009,28(4)：35-37.
[90]娄素华,李研,吴耀武,等.多目标电网无功优化的量子遗传算法[J].高电压技术,2005,31(9)：69-71.
[91]Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm:NSGA-II [J]. IEEE Transactions on Evolutionary Computation,2002, 6(2):182-197.
[92]Jim Q. Chen, Sang M. Lee. An exploratory cognitive DSS for strategic decision making [J]. Decision Support Systems,2003,10(36):147-160.
[93]Akhavi, F, Hayes, C. A coparison of two multi-eriteria decision-making techniques. Systems, Man and Cybernetics,2003. IEEE International Conference,2003,1: 956-961.
[94]于尔铿,刘广一,周京阳,等.能量管理系统(EMS)[M].北京：科学出版社,1998.
[95]赵川,李永,吴琼,等.多智能体型电网调度决策支持系统[J].电网技术,2006,30(22)：59-66.
[96]康权,刘文颖,石建雄.鄂尔多斯电网智能调度决策支持系统[J].电网技术,2008,32(1)：74-76.
[97]陈文伟.决策支持系统及其开发[M].北京：清华大学出版社,南宁：广西科学技术出版社,2000.
[98]谢开,刘永奇,朱治中,等.面向未来的智能电网[J].中国电力,2008,41(6)：19-22.
[99]姚建国,杨胜春,高宗和,等.电网调度自动化系统发展趋势展望[J].电力系统自动化,2007,31(13)：7-11.
[100]姚建国,严胜,杨胜春.中国特色智能调度的实践与展望[J].电力系统自动化,2009,33(17)：16-21.
[101]赵小平.以节能减排为切入点促进能源结构调整和增长方式转变[J].财经界,2007(3)：31-33.
[102]刘振亚.加快建设坚强国家电网,促进中国能源可持续发展[J].中国电力,2006,39(9)：5-7.
[103]Azevedo G. P., Feijo B. Agents in Power System Control Centers[J].Power Engineering Society General Meeting,2005,15(2):21-26.
[104]Kip Morison, Lei Wang, Prabha Kundur, Power system security assessment[J]. IEEE power and energy magazine,2004,28(12):30-39.
[105]Azevedo G P, Oliveria Filho A L. Control centers with open architectures [J]. IEEE Computer Application in Power,2001,14(4):27-32.
[106]Zavadil R, Miller N, Ellis A, et al. Queuing up[J]. IEEE Power and Energy Magazine,2007,5(6):47-58.
[107]Smith J C, Milligan M R, DeMeo Edgar A, et al. Utility wind integration and operating impact state of the art[J]. IEEE Trans on Power Systems,2007, 22(3):900-908.
[108]沈又幸,郭玲丽,曾明.丹麦风电发展经验及对我国的借鉴[J].华东电力,2008,36(11)：153-157.
[109]Erlich I, Brakelmann H. Integration of wind powerinto the German high voltage transmission grid[C]. IEEE Power Engineering Society General Meeting, Tampa, USA:2007.
[110]Erlich I, Brakelmann H. Integration of wind power into the German high voltage transmission grid[C]. IEEE Power Engineering Society General Meeting, Tampa, USA:2007.
[111]Erlich I, Winter W, Dittrich A. Advanced requirement for the integration of wind turbines into the German transmission system[C]. IEEE Power Engineering Society General Meeting, Montreal, Canada,2006.
[112]Duduryeh I M, Holly M, Power M. Integration of wind power generation in the Irish grid[C]. IEEE Power Engineering Society General Meeting, Montreal, Canada,2006.
[113]Ummels B C, Gibescu M, Pelgrum E, et al. System integration of the large-scale wind power in the Netherlands[C]. IEEE Power Engineering Society General Meeting, Montreal, Canada,2006.
[114]Rodriguez-Garcia JuanMa, Domingues Tomas, Alonso Juan F, et al. Large integration of wind power:the Spanish experience[C]. IEEE Power Engineering Society General Meeting, Tampa, USA,2007.
[115]Aekermann T, Abbad J R, Dudurych Ivan M, etal. European balancing act[J]. IEEE Power Energy Magazine,2007,5(6):90-103.
[116]孙元章,吴俊,李国杰.风力发电对电力系统的影响[J].电网技术,2007,31(20)：55-62.
[117]董萍,郑家伟,郭攀,等.不同容量风电场对连接处的电网影响及其性能分析[J].控制理论与应用,2008,25(2)：377-382.
[118]胡文锦,武志刚,张尧,等.风电场电能质量分析与评估[J].电力系统及其自动化学报,2009,21(4)：82-87.
[119]唐会智,彭建春.基于模糊理论的电能质量综合量化指标研究[J].电网技术,2003,27(12)：85-88.
[120]赵霞,赵成勇,贾秀芳,等.基于可变权重的电能质量模糊综合评价[J].电网技术,2005,29(6)：11-16.
[121]王伟胜,范高峰,赵海翔.风电场并网技术规定比较及其综合控制系统初探[J].电网技术,2007,31(18)：73-77.
[122]张红光,张粒子.风电场接入电网的安全稳定分析[J].中国电力,2007,40(5)：105-109.
[123]廖萍,李兴源.风电场穿透功率极限计算方法综述[J].电网技术,2008,32(10)：50-53.
[124]王海潮,鲁宗相,周双喜.风电场发电容量可信度研究[J].中国电机工程学报,2005,25(10)：103-106.
[125]张节潭,程浩忠,黄微,等.含风电场的电源规划综述[J].电力系统及其自动化学报,2009,21(2)：35-41.
[126]王正明,陆正南.风电项目投资及其运行的经济性分析[J].可再生能源,2008,26(6)：21-24.
[127]刘佳明.风电成本的简易估算[J].内蒙古电力技术,2007,25(4)：45-47.
[128]曾鸣,刘万福,未瑞,等.风力发电可行性评价指标体系及方法研究[J].华东电力,2008,36(8)：85-88.
[129]相娟.风电场运行经济性综合评价方法[J].时代经贸,2007,5(2)：16-18.
[130]吕太,张连升,李卓,等.层次分析法在风电场运行经济性评价中的应用[J].中国电力,2006,39(9)：42-44.
[131]靳静,艾芊.我国风电场建设及运行现状评估与发展前景研究[J].华东电力,2007,35(8)：44-49.
[132]谭志忠,刘德有,欧传奇,等.风电-抽水蓄能联合系统的优化运行模型[J].河海大学学报：自然科学版,2008,36(1)：58-62.
[133]杨振斌,朱瑞兆,薛桁.风电场风能资源评价两个新参数-相当风速、有功风功率密度[J].太阳能学报,2007,28(3)：248-251.
[134]陈雷,邢作霞,钟明方等.分布式风电场规划中的若干效益量化指标分析[J].沈阳工业大学学报,2007,29(6)：637-641.
[135]沈又幸,刘琳,曾鸣.风电社会效益的评价模型及其应用研究[J].华东电力,2009,37(5)：852-855.
[136]Abdullah S. M., Muslih I. M., Abu Husain W. National Grid, Diesel, and Photovoltaic Power Generation Systems in Jordan: An Engineering and Economical Evaluation [J]. Energy Sources Part B-Economics Planning and Policy, 2010,5(4):370-383.
[137]Burgio A., Menniti D., Pinnarelli A, et al. The reliability evaluation of a power system in presence of photovoltaic and wind power generation plants and UPS[C]. Proceeding of 9th International Conference on Electrical Power Quality and Utilisation, Barcelona, SPAIN,2007:696-701.
[138]孙向东,张琦,任碧莹,等.用于光伏并网发电系统的电路拓扑结构性能评价[J].变频器世界,2011,4：45-48.
[139]李伟,李世超,王丹.太阳能光伏发电风险评价[J].农业工程学报,2011,27(S1)：176-181.
[140]谢传胜,徐耀伦,董达鹏等.基于多层次模糊综合评价法的我国太阳能光伏发电行业风险评价研究[J].科技和产业,2011,11(1)：45-48.
[141]王效华,张东凤.户用风光互补发电系统设计与经济评价[J].农业工程学报,2008,24(10)：214-216.
[142]陆亚萍,梁小冰.太阳能光伏发电系统性能分析的模糊综合评价方法[C].中国电工技术学会武汉(南方九省)电工理论学会第十九届电工理论学术年会论文汇编,2007：222-226.
[143]Nishimura A., Hayashi Y., Tanaka K, et al. Life cycle assessment and evaluation of energy payback time on high-concentration photovoltaic power generation system [J]. Applied Energy,2010,87(9):2797-2807.
[144]Furushima K, Nawata Y. Performance evaluation of photovoltaic power-generation system equipped with a cooling device utilizing siphonage [J]. Journal of Solar Energy Engineering-Transactions of the ASME,2006, 128(2):146-151.
[145]张希良,汪婧.西部地区发展并网光伏发电系统的社会成本效益分析与政策评价[J].太阳能学报,2007,28(1)：32-36.
[146]马军杰,张信,王恩琦等.并网光伏发电价值的综合评价[J].水电能源科学,2011,29(6)：169-171.
[147]朱明琪,张卓然,周航等.基于模糊数学的光伏发电综合效益评价[J].科技风,2011,(11)：185-186.
[148]孟忠.太阳能光伏发电项目的后评价及实证研究[D].华北电力大学(北京),2010.
[2]王淼,尹成竹.建构坚强智能电网,引领优质清洁能源供应新模式[J].中国电力教育,2011(33)：73-74.
[3]吴贵辉.大力发展清洁能源推进电力可持续发展[J].电网与清洁能源,2008,24(3)：1-2.
[4]谢育莉.浅议发展清洁能源,建设坚强智能电网[J].商品与质量,2010(12)：14.
[5]帅军庆.创新发展建设智能电网[J].中国电力企业管理,2009,(2)：19-21.
[6]白建华,贾德香.构筑“大型能源基地”的综合输送体系[J].能源研究观点专报,2009,(3)：5-9.
[7]阮晓琴.全球经济寒冬中的光伏产业和风电产业[J].太阳能,2009,(1)：16-17.
[8]许瑞林.江苏省可再生能源发展概况[J].能源研究与利用,2007,(6)：34-36.
[9]中投顾问产业研究中心.光伏及风电行业近期走向[J].太阳能,2011,(14)：40-41.
[10]李碧君,方勇杰,杨卫东等.光伏发电并网大电网面临的问题与对策[J].电网与清洁能源,2010,26(4)：52-59.
[11]张衡.浅谈大规模风电接入对电力系统的影响[J].宁夏电力,2011,(6)：51-56.
[12]洪翠,林维明,温步瀛等.风电场风速及风电功率预测方法研究综述[J].电网与清洁能源,2011,27(1)：60-66.
[13]王澄海,胡菊,靳双龙等.中尺度WRF模式在西北西部地区低层风场模拟中的应用和检验[J].干旱气象,2011,29(2)：161-167.
[14]袁越,李强,李群等.风电功率特性分析及其不确定性解决方案[J].电力科学与技术学报,2011,26(1)：67-72
[15]付超,朱凌,王慧等.风电场并网在线预警系统研究[J].电力系统保护与控制,2011,39(17)：64-69.
[16]王丽婕,廖晓钟,高阳,高爽.风电场发电功率的建模和预测研究综述[J].电力系统保护与控制,2009,37(13)：118-121.
[17]Pinson P, Kariniotakis G N. Wind Power Forecasting Using Fuzzy Neural Networks Enhanced with On-line Prediction Risk Assessment[C]. Power Tech Conference Proceedings 2003 IEEE. Bologna:2003.
[18]Damousis I G, Alexiadis M C, Theocharis J B, et al.A Fuzzy Model for Wind Speed Prediction and Power Generation in Wind Parks Using Spatial Correlation[J].IEEE Trans on Energy Conversion,2004,19(2):352-361.
[19]Landberg L, Prediktor:An on-line prediction system[C].Wind Power for 21st Century, EUWEC Special Topic Conference, Kassel,2000.
[20]Lange B, Rohrig K, Ernst B, et al. Wind power prediction in Germany:Recent advances and future challenges[C].European Wind Energy Conference, Athens, 2006.
[21]Zack J W, Brower M C, Bailey B H. Validating of the wind model in wind forecasting application[C]. Euwec Special Topic Conference Wind Power for the 21st Century, Kassel Germany,2000.
[22]时庆华,高山.基于ARMA和卡尔曼滤波的风电场风电功率预测研究[C].中国高等学校电力系统及其自动化专业第二十五届学术年会,2009.
[23]周晖,方江晓,黄梅等.风电功率GARCH预测模型的应用研究[J].电力系统保护与控制,2011,39(5)：108-114.
[24]李俊芳,张步涵,谢光龙等.基于灰色模型的风速-风电功率预测研究[J].电力系统保护与控制,2010,38(19)：151-159.
[25]El-Fouly T. H. M., El-Saadany E. F., Salama M. M. A. Improved Grey Predictor Rolling Models for Wind Power Prediction[J]. IEEE Proceedings on Gener, Transm & Distrib,2007,1(6):928-937.
[26]陈聪聪,王维庆.基于小波神经网络法的短期风电功率预测方法研究[J].工业控制计算机,2010,23(10)：47-48.
[27]Sideratos G, Hatziargyriou N. Using Radial Basis Neural Networks to Estimate Wind Power Production[C]. Power Engineering Society General Meeting, IEEE, 2007,1-7.
[28]黄金花,彭晖.风电场短期风电功率的神经网络方法预测研究[J].电工电气,2009,(9)：57-60.
[29]彭怀午,刘方锐,杨晓峰.基于人工神经网络的风电功率短期预测研究[J].华东电力,2009,37(11)：1918-1921.
[30]齐放.基于小波神经网络的风电功率预测[J].电气开关,2011,49(4)：18-20.
[31]武小梅,白银明,文福拴等.基于RBF神经元网络的风电功率短期预测[J].电力系统保护与控制,2011,39(15)：80-83.
[32]牛晨光,刘丛.基于相空间重构的神经网络短期风电预测模型[J].中国电力,2011,44(11)：73-77.
[33]陈国初,王鹏,徐余法等.基于小波分解的风电场短期功率混合预测模型[J].上海电机学院学报,2011,14(3)：163-168.
[34]屠强.风电功率预测技术的应用现状及运行建议[J].电网与清洁能源,2009,25(10)：4-9.
[35]王健,严干贵,宋薇等.风电功率预测技术综述[J]、东北电力大学学 报,2011,31(3)：20-24.
[36]陈宁,沙倩,汤奕,等.基于交叉熵理论的风电功率组合预测方法[J].中国电机工程学报,2012,32(4)：29-36.
[37]叶林,刘鹏.基于经验模态分解和支持向量机的短期风电功率组合预测模型[J].中国电机工程学报,2011,31(31)：102-108.
[38]叶晨.风电功率组合预测研究[D].华北电力大学,2011.
[39]Chakraborty S., Weiss M. D., Simoes M. G. Distributed intelligent energy management system for a single-phase high-frequency AC microgrid [J]. IEEE Transactions on Industrial Electronics,2007,54(1):97-109.
[40]Yona A, Senjyu T, Funabashi T. Application of recurrent neural network to Short-term-ahead generating power forecasting for photovoltaic System [C]. IEEE Power Engineering Society General Meeting,2007.
[41]Mondol J. D, Yohanis Y. G, Norton B. Comparison of measured and predicted long term performance of grid a connected photovoltaic system[J]. Energy Conversion and Management,2007,48:1065-1080.
[42]Rahman Md. H., Yamashiro S. Novel distributed Power generating system of PV-ECass using solar energy estimation[C]. IEEE Transactions on Energy Conversion,2007,22(2):358-367.
[43]Yona A, Senjyu T, Funabashi T. Application of recurrent neural network to short-term-ahead generating power forecasting for photovoltaic system[C]. IEEE Power Engineering Society General Meeting,2007.
[44]Adel Mellit, Alessan and Massi Pavan. Performance prediction of 20 kW grid-connected photovoltaic plant at Trieste (Italy) using artificial neural network[J]. Energy Conversion and Management,2010(51):2431-2441.
[45]LI Yingzi, NIU Jingcang. Forecast of power generation for grid-connected photovoltaic system based on grey model and Markov chain[C]. IEEE Conference on Industrial Electronics and Applications, Singapore,2008:1729-1733.
[46]LI Yingzi, NIU Jincang. Forecast of power generation for grid-connected photovoltaic system based on markov chain[C]. Power and Energy Engineering Conference, Wuhan, China,2009:432-435.
[47]Mellit A., Pavan A.M. A 24-h forecast of solar irradiance using artificial neural network:Application for Performance Prediction of a grid-connected PV Plant at Trieste, Italy. Solar Energy,2010,84(8):807-821.
[48]栗然,李广敏.基于支持向量机回归的光伏发电出力预测[J].中国电力,2008,41(2)：74-78.
[49]程其云.基于数据挖掘的电力短期负荷预测模型及方法的研究[D].重庆：重庆大 学,2004.
[50]赵宏伟,任震,黄雯莹.基于周期自回归模型的短期负荷预测[J].中国电机工程学报,1997,17(5)：348-351.
[51]KyungBin Song, Young-Sik Baek, Dug Hun Hong and Gilsoo Jang. Short-term load foreeasting for the holidays using fuzzy liner regression method[J].IEEE Transactions Power Systems,2005,20(1):96-101.
[52]Tomonobu Senjuy, Paras Mandal, Katsumi Uezato, and Toshihisa Funabasi. Next day load curve forecasting usingh hybrid correction method [J].IEEE Transactions Power Systems,2005,20(1):102-109.
[53]焦建林,芦晶晶.基于改进时间序列法的配电网短期负荷预测模型[J].电工技术杂志,2002,5：25-28.
[54]王晚,张少华.一种应用时间序列技术的短期电力负荷预测模型[J].上海大学学报(自然科学版),2002,8(2)：133-136.
[55]王秋梅.时间序列法负荷预测的原理和应用[J].华东电力,1993,4：37-39.
[56]J.Nowieka-Zagrajeka, R.Weron. Modeling electrieity loads in California:ARMA models with hyperbolic noise [J]. Signal Processing,2002,82(12):1903-1915.
[57]S. SP. Pappas, L. Ekonomou, D.Ch. Karamousantas, G.E. Chatzarakis, S.K.Katsikasd, P.Liatsis. Eleetricity demand loads modeling using Auto Regressive Moving Average (ARMA) models[J]. Energy,2008,33(9):1353-1360.
[58]Bonnie K. Ray. Long-range forecasting of IBM Product revenues using a seasonal fraetionally differenced ARMA model [J]. International Journal of Forecasting, 1993,9(2):255-269.
[59]牛东晓,陈志业,邢棉,谢宏.具有二重趋势性的季节型电力负荷预测组合优化灰色神经网络模型[J].中国电机工程学报,2002,22(1)：29-32.
[60]康重庆,夏清,张伯明.电力系统负荷预测研究综述与发展方向的探讨[J].电力系统自动化,2004,28(17)：1-11.
[61]Ruey-Chyn Tsaur. The development of an interval grey regression model for limited time series forecasting[J].Expert Systems with Applications,2010, 37(2):1200-1206.
[62]P.Zhou, B.W.Ang, K.L.Poh. A trigonometric grey prediction approach to forecasting electricity demand [J]. Energy,2006,31(14):2839-2847.
[63]Yen-Tseng Hsu, Ming-Chung Liu, JeromeYeh, Hui-Fen Hung. Forecasting the turning time of stock market based on Markov-Fourier grey model [J]. Expert Systems with Applications,2009,36(4):8597-8603.
[64]刘宝英,杨仁刚.基于主成分分析的最小二乘支持向量机短期负荷预测模型[J].电力自动化设备,2008,28(1)：13-17.
[65]吴杰康,陈明华,陈国通.基于PSO的模糊神经网络短期负荷预测[J].电力系统及其自动化学报,2007,19(1)：63-67.
[66]冷喜武.支持向量回归在短期负荷预测中的应用研究[D].河北：华北电力大学,2008.
[67]畅广辉,刘涤尘,熊浩.基于多分辨率SVM回归估计的短期负荷预测[J].电力系统自动化,2007,31(9)：37-41.
[68]Radwan E, Abdel-aal. Short-term hourly load forecasting using abductive network[J].IEEE Transactions Power Systems,2004,19(1):164-173.
[69]T. Senjyu, H. Takara, K. Uezato, and T. Funabashi. One-hour-ahead load foreeasting using Neural network[J].IEEE Transactions Power Systems,2002, 17(1):113-118
[70]Apostolos kotsialos, Markos Papageorgiou, Antonios Poulimenos. Long-term sales forecasting using holt-winter sand neural network methods[J] Journal of Forecasting, 2005,24(5):353-368.
[71]Bo-June Chen, Ming-Wei Chang, and Chih-Jen Lin. Load forecasting using support vector machines: a study on EUNITE Competition 2001[J].IEEE Transactions Power Systems,2004,19(4):1821-1830.
[72]董军,陈小良,张婧.计及分布式电源的电网调度模型研究[J].华东电力,2009,37(5)：723-726.
[73]张子健,邱晓燕.基于多目标决策的电网无功优化[J].贵州工业大学学报(自然科学版),2008,37(3)：80-82.
[74]王云,张伏生,陈建斌.电力系统多目标无功优化研究[J].西安交通大学学报,2008,42(2)：213-217.
[75]Jose Vallejos. Multi-objective Reactive Power Compensation Applied to the Paraguayan Power System[C]. Latin America:Trans-mission & Distribution Conference and Exposition,2006:1-6.
[76]Furong L.i Genetic Algorithms for Optimal Reactive Power Compensation on the National Grid System[J]. IEEE Transactions On PowerSystems,2005,20(1): 493-500.
[77]张增年,马昊旻.多目标决策的柔性电压控制[J].系统仿真学报,2009,21(1)：135-139.
[78]张增年,马昊.基于多目标决策的协调电压控制[J].计算机仿真,2008,25(7)：252-256.
[79]J Y Wen, Q H Wu, D R Turner, SJ Cheng, J Fitch. Optimal Coordinated Voltage Control for Power System Voltage Stability[C]. IEEE Transactions on Power Systems,2004,19(2):1115-1122.
[80]Scorsi, Mpozzi, Csabelli, and Aserrani, The Coordinated Automatic Voltage Control of the Italian transmission grid—Part Ⅰ:Reasons of the Choice and Overview of the Consolidated Hierarchical System[C]. IEEE Transactions on Power Systems,2004 19(4):1723-1732.
[81]Y Li, D J Hill, T Wu, Optimal Coordinated Voltage Control of Power Systems- An Immune Algorithm Solution[C]. Control Conference,2004,5(3):1398-1403.
[82]韦化,李滨,杭乃善等.大规模水火电力系统最优潮流的现代内点理论分析[J].中国电机工程学报,2003,23(4)：5-8.
[83]胡国强,贺仁睦.基于交互式多目标决策方法的水火电力系统日有功负荷优化分配[J].电网技术,2007,31(18)：37-42.
[84]翟桥柱,管晓宏,赖菲.具有相同机组水火电调度问题的新算法[J].中国电机工程学报,2002,22(3)：38-42.
[85]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.
[86]Basu M.An interactive fuzzy satisfying method based on evolutionary programming technique for multi objective short-term hydrothermal scheduling[J]. Electric Power Systems Research,2004,69(2-3):277-285.
[87]宋海洲.基于相对目标接近度的多目标决策方法及其应用[J].数学的实践与认识,2004,34(5)：30-36.
[88]张欣莉.基于目标满意度的交互式多目标决策改进方法[J].系统工程,2004,22(9)：10-13.
[89]黄山,张学军,郑丽英.不完全信息的多目标决策分析方法研究[J].兰州交通大学学报2009,28(4)：35-37.
[90]娄素华,李研,吴耀武,等.多目标电网无功优化的量子遗传算法[J].高电压技术,2005,31(9)：69-71.
[91]Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm:NSGA-II [J]. IEEE Transactions on Evolutionary Computation,2002, 6(2):182-197.
[92]Jim Q. Chen, Sang M. Lee. An exploratory cognitive DSS for strategic decision making [J]. Decision Support Systems,2003,10(36):147-160.
[93]Akhavi, F, Hayes, C. A coparison of two multi-eriteria decision-making techniques. Systems, Man and Cybernetics,2003. IEEE International Conference,2003,1: 956-961.
[94]于尔铿,刘广一,周京阳,等.能量管理系统(EMS)[M].北京：科学出版社,1998.
[95]赵川,李永,吴琼,等.多智能体型电网调度决策支持系统[J].电网技术,2006,30(22)：59-66.
[96]康权,刘文颖,石建雄.鄂尔多斯电网智能调度决策支持系统[J].电网技术,2008,32(1)：74-76.
[97]陈文伟.决策支持系统及其开发[M].北京：清华大学出版社,南宁：广西科学技术出版社,2000.
[98]谢开,刘永奇,朱治中,等.面向未来的智能电网[J].中国电力,2008,41(6)：19-22.
[99]姚建国,杨胜春,高宗和,等.电网调度自动化系统发展趋势展望[J].电力系统自动化,2007,31(13)：7-11.
[100]姚建国,严胜,杨胜春.中国特色智能调度的实践与展望[J].电力系统自动化,2009,33(17)：16-21.
[101]赵小平.以节能减排为切入点促进能源结构调整和增长方式转变[J].财经界,2007(3)：31-33.
[102]刘振亚.加快建设坚强国家电网,促进中国能源可持续发展[J].中国电力,2006,39(9)：5-7.
[103]Azevedo G. P., Feijo B. Agents in Power System Control Centers[J].Power Engineering Society General Meeting,2005,15(2):21-26.
[104]Kip Morison, Lei Wang, Prabha Kundur, Power system security assessment[J]. IEEE power and energy magazine,2004,28(12):30-39.
[105]Azevedo G P, Oliveria Filho A L. Control centers with open architectures [J]. IEEE Computer Application in Power,2001,14(4):27-32.
[106]Zavadil R, Miller N, Ellis A, et al. Queuing up[J]. IEEE Power and Energy Magazine,2007,5(6):47-58.
[107]Smith J C, Milligan M R, DeMeo Edgar A, et al. Utility wind integration and operating impact state of the art[J]. IEEE Trans on Power Systems,2007, 22(3):900-908.
[108]沈又幸,郭玲丽,曾明.丹麦风电发展经验及对我国的借鉴[J].华东电力,2008,36(11)：153-157.
[109]Erlich I, Brakelmann H. Integration of wind powerinto the German high voltage transmission grid[C]. IEEE Power Engineering Society General Meeting, Tampa, USA:2007.
[110]Erlich I, Brakelmann H. Integration of wind power into the German high voltage transmission grid[C]. IEEE Power Engineering Society General Meeting, Tampa, USA:2007.
[111]Erlich I, Winter W, Dittrich A. Advanced requirement for the integration of wind turbines into the German transmission system[C]. IEEE Power Engineering Society General Meeting, Montreal, Canada,2006.
[112]Duduryeh I M, Holly M, Power M. Integration of wind power generation in the Irish grid[C]. IEEE Power Engineering Society General Meeting, Montreal, Canada,2006.
[113]Ummels B C, Gibescu M, Pelgrum E, et al. System integration of the large-scale wind power in the Netherlands[C]. IEEE Power Engineering Society General Meeting, Montreal, Canada,2006.
[114]Rodriguez-Garcia JuanMa, Domingues Tomas, Alonso Juan F, et al. Large integration of wind power:the Spanish experience[C]. IEEE Power Engineering Society General Meeting, Tampa, USA,2007.
[115]Aekermann T, Abbad J R, Dudurych Ivan M, etal. European balancing act[J]. IEEE Power Energy Magazine,2007,5(6):90-103.
[116]孙元章,吴俊,李国杰.风力发电对电力系统的影响[J].电网技术,2007,31(20)：55-62.
[117]董萍,郑家伟,郭攀,等.不同容量风电场对连接处的电网影响及其性能分析[J].控制理论与应用,2008,25(2)：377-382.
[118]胡文锦,武志刚,张尧,等.风电场电能质量分析与评估[J].电力系统及其自动化学报,2009,21(4)：82-87.
[119]唐会智,彭建春.基于模糊理论的电能质量综合量化指标研究[J].电网技术,2003,27(12)：85-88.
[120]赵霞,赵成勇,贾秀芳,等.基于可变权重的电能质量模糊综合评价[J].电网技术,2005,29(6)：11-16.
[121]王伟胜,范高峰,赵海翔.风电场并网技术规定比较及其综合控制系统初探[J].电网技术,2007,31(18)：73-77.
[122]张红光,张粒子.风电场接入电网的安全稳定分析[J].中国电力,2007,40(5)：105-109.
[123]廖萍,李兴源.风电场穿透功率极限计算方法综述[J].电网技术,2008,32(10)：50-53.
[124]王海潮,鲁宗相,周双喜.风电场发电容量可信度研究[J].中国电机工程学报,2005,25(10)：103-106.
[125]张节潭,程浩忠,黄微,等.含风电场的电源规划综述[J].电力系统及其自动化学报,2009,21(2)：35-41.
[126]王正明,陆正南.风电项目投资及其运行的经济性分析[J].可再生能源,2008,26(6)：21-24.
[127]刘佳明.风电成本的简易估算[J].内蒙古电力技术,2007,25(4)：45-47.
[128]曾鸣,刘万福,未瑞,等.风力发电可行性评价指标体系及方法研究[J].华东电力,2008,36(8)：85-88.
[129]相娟.风电场运行经济性综合评价方法[J].时代经贸,2007,5(2)：16-18.
[130]吕太,张连升,李卓,等.层次分析法在风电场运行经济性评价中的应用[J].中国电力,2006,39(9)：42-44.
[131]靳静,艾芊.我国风电场建设及运行现状评估与发展前景研究[J].华东电力,2007,35(8)：44-49.
[132]谭志忠,刘德有,欧传奇,等.风电-抽水蓄能联合系统的优化运行模型[J].河海大学学报：自然科学版,2008,36(1)：58-62.
[133]杨振斌,朱瑞兆,薛桁.风电场风能资源评价两个新参数-相当风速、有功风功率密度[J].太阳能学报,2007,28(3)：248-251.
[134]陈雷,邢作霞,钟明方等.分布式风电场规划中的若干效益量化指标分析[J].沈阳工业大学学报,2007,29(6)：637-641.
[135]沈又幸,刘琳,曾鸣.风电社会效益的评价模型及其应用研究[J].华东电力,2009,37(5)：852-855.
[136]Abdullah S. M., Muslih I. M., Abu Husain W. National Grid, Diesel, and Photovoltaic Power Generation Systems in Jordan: An Engineering and Economical Evaluation [J]. Energy Sources Part B-Economics Planning and Policy, 2010,5(4):370-383.
[137]Burgio A., Menniti D., Pinnarelli A, et al. The reliability evaluation of a power system in presence of photovoltaic and wind power generation plants and UPS[C]. Proceeding of 9th International Conference on Electrical Power Quality and Utilisation, Barcelona, SPAIN,2007:696-701.
[138]孙向东,张琦,任碧莹,等.用于光伏并网发电系统的电路拓扑结构性能评价[J].变频器世界,2011,4：45-48.
[139]李伟,李世超,王丹.太阳能光伏发电风险评价[J].农业工程学报,2011,27(S1)：176-181.
[140]谢传胜,徐耀伦,董达鹏等.基于多层次模糊综合评价法的我国太阳能光伏发电行业风险评价研究[J].科技和产业,2011,11(1)：45-48.
[141]王效华,张东凤.户用风光互补发电系统设计与经济评价[J].农业工程学报,2008,24(10)：214-216.
[142]陆亚萍,梁小冰.太阳能光伏发电系统性能分析的模糊综合评价方法[C].中国电工技术学会武汉(南方九省)电工理论学会第十九届电工理论学术年会论文汇编,2007：222-226.
[143]Nishimura A., Hayashi Y., Tanaka K, et al. Life cycle assessment and evaluation of energy payback time on high-concentration photovoltaic power generation system [J]. Applied Energy,2010,87(9):2797-2807.
[144]Furushima K, Nawata Y. Performance evaluation of photovoltaic power-generation system equipped with a cooling device utilizing siphonage [J]. Journal of Solar Energy Engineering-Transactions of the ASME,2006, 128(2):146-151.
[145]张希良,汪婧.西部地区发展并网光伏发电系统的社会成本效益分析与政策评价[J].太阳能学报,2007,28(1)：32-36.
[146]马军杰,张信,王恩琦等.并网光伏发电价值的综合评价[J].水电能源科学,2011,29(6)：169-171.
[147]朱明琪,张卓然,周航等.基于模糊数学的光伏发电综合效益评价[J].科技风,2011,(11)：185-186.
[148]孟忠.太阳能光伏发电项目的后评价及实证研究[D].华北电力大学(北京),2010.
© 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 |