电力市场环境下无功定价及无功优化模型研究
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摘要
电力市场环境中,无功服务已经被认定为最重要的服务之一。首先,电力系统的稳定性需要通过适量且分布合理的无功服务予以支持。其次,无功服务的优化配置利用可以进一步改善有功的合理分配和交易,以达到实现提升电力系统的效率的目的。再者,电力市场环境下用于降损的最直接手段就是调节无功分布,通过无功功率的合理分配,可以实现降低系统的电能损耗。从电力服务角度上看,无功支持可以改善电压波形,减少系统中出现的过电压对电网安全和设备绝缘寿命的影响。随着分布式新能源发电不断并入电网中运行,由于分布式能源分散性、随机性和间歇性的特点,出现了谐波污染、电压失稳及电压闪变等问题,从而导致电网的安全稳定面临新的考验,如何充分调配并发挥无功资源设备对电力系统稳定调节的作用显得十分重要,同时为实现电力系统中各供应商的经济利益和国家能源节约的整体效益,采用无功定价及无功优化措施实现激励供应商积极改善无功服务支持的目标,最终通过资源的整合调配和高效利用达到经济效益最优化。
本文针对分布式能源不断并网运行的背景下,基于电力市场的特点对无功负荷预测、无功定价及无功优化模型问题展开了研究,并对无功优化后的效果进行了评价,具体研究内容包括以下几个方面：
(1)在对负荷预测研究的基础上,综合分析各种预测方法,结合无功负荷的特点提出了一种时间序列与支持向量机组合无功预测模型,利用该模型对无功负荷进行预测,采用均方根相对误差RMSRE对预测效果进行评估,算例证明该组合模型精度高,对无功负荷预测有较强的适用性。
(2)充分考虑分布式能源接入电网后系统安全稳定问题,在无功定价经典模型的基础上,建立了基于电压稳定的无功定价新模型,通过计算仿真验证了新型模型具有合理引导消费和提升电力系统安全稳定的双重效果。新型模型具有非线性特点,建立新型改进算法求解(结合免疫算法的改进多中心-校正内点法),通过多种算法对比,该计算准确度高且速度快。
(3)本文建立了考虑分布式新能源发电机组组合的无功经济调度模型,利用该模型对电力系统无功经济调度优化计算,以IEEE-14节点为例,分析计算了在考虑或不考虑网络潮流约束及N-1故障集等多种情况下的发电机出力与无功优化结果,得到了满足电力系统安全约束且经济性明显的发电计划。
(4)文中最后对电网无功优化的效果进行了评价,综合分析了多种评价方法,结合无功服务的特点以及数据特点,构建了无功效果评价的指标体系,采用了基于自主决策视角和数据包络分析结合的方法对无功优化的效果进行了综合评价,计算结论表明无功优化后的效果是明显的。
A reactive service is generally regarded as one of the most important services in the electricity market services. Firstly, the stability of the power system needs to be supported by an appropriate amount and rational allocation of reactive services. Secondly, optimizing reactive service configuration can further improve the rational allocation and active transactions for the purpose of improving the efficiency of the power system. Furthermore, the most direct means for reducing losses in the electricity market environment is to regulate the distribution of reactive power, which can be achieved to reduce the power losses of the system. From the point of view of electricity services, reactive support can improve the voltage waveform, diminishing the impact on the grid overvoltage for safety and equipment insulation life. With the new distributed energy generation and constantly running into the grid, due to the distributed energy dispersed, random and intermittent characteristics, appeared harmonic pollution, voltage instability and voltage flicker and other issues, leading to grid security stability are facing new challenges, how to make the deployment of resources and play equipment for reactive power system stability is very important regulatory role, as well as economic benefits and overall effectiveness of the power system of the country each supplier of energy saving, the use of reactive power pricing and Reactive Power Optimization measures to achieve an incentive for vendors to actively improve reactive support service goals, and ultimately economic optimization by integrating the deployment and efficient use of resources to achieve. To sum up the electricity market environment reactive and reactive pricing optimization model is a very important issue.
In this study, distributed energy grid run continuously in the background, based on the characteristics of the electricity market for reactive power load forecasting, pricing and reactive power optimization model launched a research question, which lead a evaluation about the effect on reactive optimizing.The study includes the following aspects:
(1) On the basis of load forecasting study, a comprehensive analysis of the various forecasting methods, presents a forecasting model that combine a time series with support vector machines, using the model to predict the reactive load, and using the relative root mean square error of prediction RMSRE to assess the effect, examples prove that the combination of high accuracy of the model of reactive power load forecasting has strong applicability.
(2) After full consideration of distributed energy systems connected to the grid security and stability issues, based on the classic reactive pricing model, established a new reactive pricing model based on the stable voltage, by calculating a new simulation model to guide rational consumption and enhance the power system security and stability of double effect. The new model has a non-linear characteristics, the establishment of a new algorithm into (a combination of immune algorithm to improve multi-center-Correction interior-point method).This method has high accuracy and efficiency by comparing a variety of algorithms.
(3) This paper established a reactive economic dispatch model that considers new energy generators distributed portfolio, using the model of economic dispatch reactive power optimization calculation to IEEE-14node example, the analysis calculated the trend with or without considering the network constraints and generator reactive power output and a variety of situations, such as N-1fault set optimization results, got to meet the power system security constraints and generating significant economic plan.
(4) Finally, the paper reactive power optimization results were evaluated, a comprehensive analysis of a variety of evaluation methods, combined with the characteristics of reactive services and data features, build reactive effect evaluation index system, using a perspective based on their own decisions and packet DEA method of combining the effect of reactive power optimization conducted a comprehensive evaluation, the conclusions show that the effect of reactive power is obvious after optimizing reactive.
本文针对分布式能源不断并网运行的背景下,基于电力市场的特点对无功负荷预测、无功定价及无功优化模型问题展开了研究,并对无功优化后的效果进行了评价,具体研究内容包括以下几个方面：
(1)在对负荷预测研究的基础上,综合分析各种预测方法,结合无功负荷的特点提出了一种时间序列与支持向量机组合无功预测模型,利用该模型对无功负荷进行预测,采用均方根相对误差RMSRE对预测效果进行评估,算例证明该组合模型精度高,对无功负荷预测有较强的适用性。
(2)充分考虑分布式能源接入电网后系统安全稳定问题,在无功定价经典模型的基础上,建立了基于电压稳定的无功定价新模型,通过计算仿真验证了新型模型具有合理引导消费和提升电力系统安全稳定的双重效果。新型模型具有非线性特点,建立新型改进算法求解(结合免疫算法的改进多中心-校正内点法),通过多种算法对比,该计算准确度高且速度快。
(3)本文建立了考虑分布式新能源发电机组组合的无功经济调度模型,利用该模型对电力系统无功经济调度优化计算,以IEEE-14节点为例,分析计算了在考虑或不考虑网络潮流约束及N-1故障集等多种情况下的发电机出力与无功优化结果,得到了满足电力系统安全约束且经济性明显的发电计划。
(4)文中最后对电网无功优化的效果进行了评价,综合分析了多种评价方法,结合无功服务的特点以及数据特点,构建了无功效果评价的指标体系,采用了基于自主决策视角和数据包络分析结合的方法对无功优化的效果进行了综合评价,计算结论表明无功优化后的效果是明显的。
A reactive service is generally regarded as one of the most important services in the electricity market services. Firstly, the stability of the power system needs to be supported by an appropriate amount and rational allocation of reactive services. Secondly, optimizing reactive service configuration can further improve the rational allocation and active transactions for the purpose of improving the efficiency of the power system. Furthermore, the most direct means for reducing losses in the electricity market environment is to regulate the distribution of reactive power, which can be achieved to reduce the power losses of the system. From the point of view of electricity services, reactive support can improve the voltage waveform, diminishing the impact on the grid overvoltage for safety and equipment insulation life. With the new distributed energy generation and constantly running into the grid, due to the distributed energy dispersed, random and intermittent characteristics, appeared harmonic pollution, voltage instability and voltage flicker and other issues, leading to grid security stability are facing new challenges, how to make the deployment of resources and play equipment for reactive power system stability is very important regulatory role, as well as economic benefits and overall effectiveness of the power system of the country each supplier of energy saving, the use of reactive power pricing and Reactive Power Optimization measures to achieve an incentive for vendors to actively improve reactive support service goals, and ultimately economic optimization by integrating the deployment and efficient use of resources to achieve. To sum up the electricity market environment reactive and reactive pricing optimization model is a very important issue.
In this study, distributed energy grid run continuously in the background, based on the characteristics of the electricity market for reactive power load forecasting, pricing and reactive power optimization model launched a research question, which lead a evaluation about the effect on reactive optimizing.The study includes the following aspects:
(1) On the basis of load forecasting study, a comprehensive analysis of the various forecasting methods, presents a forecasting model that combine a time series with support vector machines, using the model to predict the reactive load, and using the relative root mean square error of prediction RMSRE to assess the effect, examples prove that the combination of high accuracy of the model of reactive power load forecasting has strong applicability.
(2) After full consideration of distributed energy systems connected to the grid security and stability issues, based on the classic reactive pricing model, established a new reactive pricing model based on the stable voltage, by calculating a new simulation model to guide rational consumption and enhance the power system security and stability of double effect. The new model has a non-linear characteristics, the establishment of a new algorithm into (a combination of immune algorithm to improve multi-center-Correction interior-point method).This method has high accuracy and efficiency by comparing a variety of algorithms.
(3) This paper established a reactive economic dispatch model that considers new energy generators distributed portfolio, using the model of economic dispatch reactive power optimization calculation to IEEE-14node example, the analysis calculated the trend with or without considering the network constraints and generator reactive power output and a variety of situations, such as N-1fault set optimization results, got to meet the power system security constraints and generating significant economic plan.
(4) Finally, the paper reactive power optimization results were evaluated, a comprehensive analysis of a variety of evaluation methods, combined with the characteristics of reactive services and data features, build reactive effect evaluation index system, using a perspective based on their own decisions and packet DEA method of combining the effect of reactive power optimization conducted a comprehensive evaluation, the conclusions show that the effect of reactive power is obvious after optimizing reactive.
引文
[1]宋晓辉,白晓民,易俗.一种考虑负荷特性变化的改进线性回归负荷预测法[J].供用电,2006(1)：13-18.
[2]张步涵,刘小华,万建平,刘沛,程时杰.基于混沌时间序列的负荷预测及其关键问题分析[J].电网技术,2004(1)：34-37.
[3]Wang Xiaodong, Zhang Haoran. Prediction of Chaotic time series using LS-SVM with automatic parameter[J].IEEE Sixth International Conference on Parallel and Distributed Computing, Applications and Technologies, Boston Embassy Suites. 2005:962-965.
[4]崔勇,王川,陈小龙.基于灰色马尔科夫预测模型的中长期电力负荷预测[J].现代电力,2011,(3)：38-41.[5]Park D C, El-Sharkawi M A, Marks R J, et al. Electric Load Forecasting Using an Artificial Neural Network[J].IEEE Transactions on Power Systems,1991,6(2): 442-449.
[6]王成纲,郭辉,张文静.基于小波分解的电力系统短期负荷预测方法研究[J].河北电力技术,2010,29(2)：11-14.
[7]Tony van Gestel,Johan A.K. Suykens,Bart Baesens,Stijn Viaene, Jan Vanthienen, Guido Dedene,Bart de Moor,Joos Vandewalle. Benchmarking Least Squares Support Vector Machine Classifiers[J]. Machine Learning,2004,54(1):5-32.
[8]Wang Xiaodong, Zhang Haoran. Prediction of Chaotic time series using LS-SVM with automatic parameter[J].IEEE Sixth International Conference on Parallel and Distributed Computing, Applications and Technologies, Boston Embassy Suites. 2005:962-965.
[9]祝燕萍,方鸽飞.基于动态自适应神经网络和人体舒适度的短期负荷预测[J].电力系统保护与控制,2012,40(1)：56-61.
[10]刘红文,张葛祥,方纯.基于优化最小二乘支持向量机的负荷预测[J].电气应用,2010(4)：43-48.
[11]杨毅强,曾晓辉.基于贝叶斯理论的小波变换支持向量机负荷预测模型[J].中国高新技术企业,2008(2)：76-82.
[12]高荣,刘晓华.基于DA聚类的多支持向量机负荷预测[J].控制工程,2005,12(2)：45-49.
[13]汪灵枝,韦增欣,陈进来.基于支持向量机的粒子群神经网络集成股市预测模型[J].数学的实践与认识,2010,22(2)：54-59.
[14]翟永杰,刘林,王朋.基于ADE-SVM和模糊理论的电力系统中期负荷预测[J].电力系统保护与控制,2012,40(8)：110-120.
[15]Zhu Zhi-hui, Sun Yun-lian, LI Huang-qiang. Hybrid of EMD and SVMs for short-term load forecasting[C].IEEE International Conference on Control and Automation.2007:1044-1047.
[16]王洪波,朱启兵.基于EMD和LS-SVM的非平稳振动信号趋势预测[J].计算机工程与应用,2008,44(16)：157-159.
[17]谢景新,程春田,周桂红,等.基于经验模式分解与混沌分析的直接多步预测模型[J].自动化学报,2008,34(6)：684-689.
[18]Yang J N, Lei Y, Pan SW, et al. System identification of linear structures based on hilbert-huang spectral analysis,part 1:normal modes[J]. Earthquake Engineering and Structural Dynamics,2003,32:1443-1467.
[19]白玮莉,刘志刚,彭权威,周翔.一种基于HHT的电力系统短期负荷预测模型[J].电力系统保护与控制,2011,39(2)：55-61.
[20]Chacn J E, Alberto R C. A note on the universal consistency of the kernel distribution function estimator[J]. Statistics & Probability Letters 2010,80(17-18): 1414-1419.
[21]Guzman M A, Delgado A, De Carvalho J. A novel multiobjective optimization algorithm based on bacterial chemotaxis[J]. Engineering Applications of Artificial Intelligence,2010,23(3):292-301.
[22]Mmaandur K R C, Chenoweth R D. Optimal Control of Reactive Power Flow for Improvements in Voltage Profiles and for Real Loss Minimization[J]. IEEE Transactions on Power Apparatus and Systems,1981,PAS-100(7):3185-3194.
[23]Assef Zobian, Marija D Ilie. A Steady State Voltage Monitoring and Control Algorithm Using Localized Least Square Minimization of Load Voltage Deviations[J]. IEEE Transactions on Power Systems,1996,11(2):929-938.
[24]JohnW. Lamont, Jian Fu. Cost Analysis of Reaetive Power Support[J]. IEEE Transactions on Power Systems,1999,14(3):890-898.
[25]戴彦,倪以信.电力市场下的无功定价研究[J].电力系统自动化,2000,24(5)：9-14.
[26]舒立平,陈允平.一种新的无功电力定价算法[J].电网技术,2004,28(5)：59-63,68.
[26]蒋志平.电力市场下的无功定价研究[D].南京理工大学,2003.
[27]王正风,汪胜和,电力市场下无功定价综述[J].东北电力技术,2009,2：10-13.
[28]于尔铿,韩放,谢开,曹肪.电力市场[M].北京,中国电力出版社,1998.
[29]电力市场下无功定价方法综述[J].企业经营管理,2009,10：265-266.
[30]于辉,赵冬梅.单边开放电力市场下的无功电价研究[J].电网技术,2004,28(14)： 62-67.
[31]李坚.张永平.一种新的无功电力定价研究[J].电网技术,2004,28(5)：59-63.
[32]Choi Joon-Young, Rim Seong-Hwang, Jong-Keun Park. Optimal Real Time Pricing of Real and Reactive Power[J]. IEEE Transactions on Power Systems,1998, 13(4):1226-1231.
[33]舒立平,陈允平.一种新的无功电力定价算法[J].电网技术,2004,28(5)：59-63,68.
[34]代红才,彭建春,杨帮宇,王娜.基于边际成本和潮流追踪的无功定价[J].电网技术,2007,31(7)：59-63.
[35]ChattopadhyayD,Bhattacharya K.Optimal reactive power planning and its spot-pricing:an integrated approach [J]. IEEE Transactions on Power Systems,1995, 10(4):2014-2020.
[36]Wen-Chen Chu. Allocating the Costs of Reactive Power Purchased in an Ancillary Service Market by Modified Y-Bus Matrix Method[J]. IEEE Transactions On Power Systems,2004,19(1):174-179.
[37]Jin Zhong, Bhattaeharya karkar. Toward a Competitive Market for Reactive Power[J]. IEEE Transactions on Power Systems,2002,17(4):1206-1215.
[38]Dandachi N H, Rawlines M J, AlsacO, eta.l OPF for Reactive Pricing Studies on the NGC System [J]. IEEE Transactions on Power Systems,1996,11(1):226-232.
[39]牛怀平,刘俊勇,吴集光,段登伟.电力市场初期两部制无功定价方法[J].电力自动化设备,2006,26(2)：11-15.
[40]牛怀平,刘俊勇,吴集光.一种新的两部制无功定价方法[J].电网技术,2005,29：1-6.
[41]孙飘红,刘俊勇,潘蕾蕾.基于统一优化补偿的无功电力市场[J].电网技术2004,28：16-20.
[42]牛怀平.电力市场初期的无功定价研究[D].四川大学博士学位论文,2005,24-35.
[43]张勇军,任震,李邦峰.电力系统无功优化调度研究综述[J].电网技术,2005,29(2)：50-56.
[44]岳青,魏宇存,孙蕾,刘崇新,张峰,李艳华.榆林电网无功优化运行研究[J].电工电气,2012,3：21-25.
[45]林娇燕.基于蚁群算法的无功优化方法的研究[J].低压电器,2011,20：43-46.
[46]孙建鹏,韩文花,朱长东.电力系统无功优化模型及算法研究[J].电力电容器与无功补偿,2011,32(6)：6-11.
[47]L.D.Arya,S.C.Choube,D.P.Kothari.Reactive power optimization using static voltage stableility index. Electric Power Components and Systems,2001.29(7):615-628.
[48]李亚男,张粒子,杨以涵.考虑电压约束裕度的无功优化及其内点解法[J].中国电机工程学报,2001,21(9)：1-4.
[49]宋军英,刘涤尘,陈允平.电力系统模糊无功优化的建模及算法[J].电网技术,2001,25(3)：22-25.
[50]曾嘉俊,刘志刚,何士玉,杨红梅.一种基于子区域粒子群的无功优化算法研究[J].电力系统保护与控制,2012,40(1)：37-42.
[51]孟祥星.市场环境下电力系统有功调度与无功优化的经济规律研究[D].山东大学,2007.
[52]舒隽,张粒子,刘易,彭永华,郭琳.电力市场下日无功计划优化模型和算法的研究[J].中国电机工程学报,2005,25(13)：80-85.
[53]李智欢.无功优化进化计算的局部搜索策略及多目标处理方法[D].华中科技大学,2010.
[54]孙东杰.电力系统经济调度和无功优化中的多目标算法研究[D].湖南大学,2010
[55]王韶,刘光时,邹青林.计及UPFO的电力系统多目标无功优化[J].电力系统保护与控制,2012,40(8)：95-100.
[56]H.W.Dommel,W.F.Tinney.Option Power Flow Solution.IEEE Transactions on Power System.1968,87(10):1866-1876.
[57]李林川,王建勇,陈礼义,等.电力系统无功补偿优化规划[J].中国电机工程学报,1999,19(2)：66-69.
[58]Ponnambalam K, Quintata V H, Vannelli A.A fast algorithm for Power system Optimization problems using an interior point method[J].IEEE Trans on Power Systems,1992,7(2):892-899.
[59]Jabr R A,Coonick A H,Cory B J.A primal-dual interior point method for Optimal power flow dispatching[J].IEEE Trans on Power Systems,2002,17 (3):654-662.
[60]李宏伟.基于改进遗传算法的电力系统无功优化的研究[J].自动化与仪器表,2011,6：7-11.
[61]杜欢.邻域拓扑粒子群优化算法及其在电力系统无功优化中的应用研究[D].浙江大学,2006.
[62]危雪.基于混沌神经网络的配电网无功优化[J].电气开关,2011,49(5)：55-56.
[63]刘科研,盛万兴,李运华.基于改进遗传模拟退火算法的无功优化[J].电网技术,2007,31(3)：13-18.
[64]任晓莉,程红丽,刘健.基于禁忌搜索算法的变电站电压无功优化控制[J].继电器,2008,36(8)：31-34.
[65]葛莲,理文祥.基于混合智能算法的电力系统无功优化研究[J].江西电力职业技术学院学报,2012,25(1)：1-4.
[66]范征.混合智能优化算法在电力系统无功优化中的应用研究[D].山东大学,2008.
[67]张立,刘洪.城市电网现状无功评价[J].电力系统及其自动化学报,2009,21(2)：115-120.
[68]胡扬,耿静,蒋传文.评价无功优化进化算法的非整秩次秩和比法[J].水电能源科学,2010,28(12)：139-142.
[69]朱景锋.基于三角模糊AHP的物联网电子政务安全评价模型分析[J].制造业自动化,2012,34(4)：90-92.
[70]Yur dakul M. AHP as a strategic decision-making to ol to justify machine tool selection[J]. Jo urnal of Materials Processing Technology,2004,146 (3):365-376.
[71]Wang M, Yu A B, Zhang Y. Grindability evaluation of advanced ceramics with AHP approach[J].Key Engineering Materials,2006,304/305:222-226.
[72]张少敏,栗军,王保义.竞争电力市场下的电网公司经营风险综合评级模型[J].电力系统保护与控制,2011,39(8)：70-74.
[73]金菊良,张礼兵,魏一鸣.水资源可持续利用评价的改进层次分析法[J].水科学进展,2004,15(2)：227-232.
[74]毛安家,何金.一种基于可信性理论的电网安全性综合评估方法[J].电力系统保护与控制,2011,39(18)：80-87.
[75]肖美丹.农机装备结构优化的多层次灰色评价[J].中国农机化,2005.
[76]王佳明,刘文颖,李群炬等.基于多层次模糊综合评价模型的短路电流限制措施优化配置[J].电网技术,2011,35(11)：125-129.
[77]林济铿,李童飞,赵子明等.基于熵权模糊综合评价模型的电力系统黑启动方案评估[J].电网技术,2012,36(2)：115-120.
[78]陈辰,胡甚平.基于模糊DEA的航空公司安全管理有效性评价[J].上海海事大学学报,2012,33(1)：12-19.
[79]Mei L W, Huai S L. Fuzzy data envelopment analysis(DEA):Model and ranking method[J]. J of Computer and Applied Mathematics,2009,223(2):872-878.
[80]王志良,单淑贞,邱林,杨成武.基于混沌优化算法的城市供水绩效DEA评价模型[J].水电能源科学,2007,25(2)：11-13.
[81]王科,魏法杰.三参数区间交叉效率DEA评价力法[J].工业工程,2010,13(2)：19-23.
[82]郭清娥,王雪青,位珍.基于DEA交叉评价的模糊综合评价模型及其应用[J].控制与决策,2012,27(4)：575-579.
[83]陈君,李聪颖,丁光明.基于BP神经网络的高速公路交通安全评价[J].同济大学学报(自然科学版),2008,36(7)927-931.
[84]鲍玉昆,张金隆.基于BP神经网络的科技项目评标模型[J].华中科技大学学报(自然科学版),2002,30(8)：85-87.
[85]张建国,雷雨龙,王健等.基于BP神经网络的换挡品质评价方法[J].吉林大学学报 (工学版),2007,37(5)：1019-1022.
[86]李世武,田晶晶,沙学锋等.基于模糊综合评价和BP神经网络的车辆危险状态辨识[J].吉林大学学报(工学版),011,41(6)：1609-1613.
[87]Lotfollahi-Yaghin, M. A. Artificial neural network ability in evaluation of random wave. Induced Inline Force on A Vertical Cylinder[J].China Ocean Eng,2012,26(1): 19-36.
[88]王雁凌,李艳君,许奇超.改进雷达图法在输变电工程综合评价中的应用[J].电力系统保护与控制,2012,40(5)：119-123.
[89]姜秋香,付强,王子龙.基于粒子群优化投影寻踪模型的区域土地资源承载力综合评价[J].农业工程学报,2011,27(11)：319-324.
[90]欧阳森,廖一键,刘洋等.基于自主式决策的综合评价方法在电能质量领域的分析与运用[J].电网技术,2012,36(2)70-74.
[91]马占新,伊茹.基于经验数据评价的非参数系统分析方法[J].控制与决策,2012,27(2)：199-204.
[92]崔勇,洪浩,肖君.大型变压器运行效率改造优化设计[J].高压电器,2012,(9)：27-30.
[93]崔勇,王川,杨锦红.断路器控制回路隐性故障检修方法[J].高压电器,2011,(12)：96-99.
[94]崔勇,陈湘华,王川.谐波测试对变电设备绝缘特性的影响研究[J].电测与仪表,2012,49(10)：5-8.
[95]黄元生,崔勇,洪浩.特高压变电站无功装置主断路器合闸控制优化改进及效益评估[J].高压电器,2013,49(6)：57-61.
[96]崔勇,王川,牛满萍.10kV电容器组专用断路器控制回路优化设计[J].工矿自动化,2012,(10)：67-69.
[97]黄元生,崔勇,李建一.安全约束条件下的经济调度优化模型研究[J].运筹与管理,2014,(2)：10-12.
[98]Yong Cui, Yuan-sheng Huang, Xin-bo Dai. Evaluation and Analysis of Competitiveness in Market Industry [J]. The 19th International Conference on Industrial Engineering and Engineering Management,2013.
[99]谢亚军.综合评价理论、方法及应用[M].北京：科学出版社,2007.
[100]邓佳佳.考虑分布式能源的电力系统优化运营模型研究[D].华北电力大学,2012
[2]张步涵,刘小华,万建平,刘沛,程时杰.基于混沌时间序列的负荷预测及其关键问题分析[J].电网技术,2004(1)：34-37.
[3]Wang Xiaodong, Zhang Haoran. Prediction of Chaotic time series using LS-SVM with automatic parameter[J].IEEE Sixth International Conference on Parallel and Distributed Computing, Applications and Technologies, Boston Embassy Suites. 2005:962-965.
[4]崔勇,王川,陈小龙.基于灰色马尔科夫预测模型的中长期电力负荷预测[J].现代电力,2011,(3)：38-41.[5]Park D C, El-Sharkawi M A, Marks R J, et al. Electric Load Forecasting Using an Artificial Neural Network[J].IEEE Transactions on Power Systems,1991,6(2): 442-449.
[6]王成纲,郭辉,张文静.基于小波分解的电力系统短期负荷预测方法研究[J].河北电力技术,2010,29(2)：11-14.
[7]Tony van Gestel,Johan A.K. Suykens,Bart Baesens,Stijn Viaene, Jan Vanthienen, Guido Dedene,Bart de Moor,Joos Vandewalle. Benchmarking Least Squares Support Vector Machine Classifiers[J]. Machine Learning,2004,54(1):5-32.
[8]Wang Xiaodong, Zhang Haoran. Prediction of Chaotic time series using LS-SVM with automatic parameter[J].IEEE Sixth International Conference on Parallel and Distributed Computing, Applications and Technologies, Boston Embassy Suites. 2005:962-965.
[9]祝燕萍,方鸽飞.基于动态自适应神经网络和人体舒适度的短期负荷预测[J].电力系统保护与控制,2012,40(1)：56-61.
[10]刘红文,张葛祥,方纯.基于优化最小二乘支持向量机的负荷预测[J].电气应用,2010(4)：43-48.
[11]杨毅强,曾晓辉.基于贝叶斯理论的小波变换支持向量机负荷预测模型[J].中国高新技术企业,2008(2)：76-82.
[12]高荣,刘晓华.基于DA聚类的多支持向量机负荷预测[J].控制工程,2005,12(2)：45-49.
[13]汪灵枝,韦增欣,陈进来.基于支持向量机的粒子群神经网络集成股市预测模型[J].数学的实践与认识,2010,22(2)：54-59.
[14]翟永杰,刘林,王朋.基于ADE-SVM和模糊理论的电力系统中期负荷预测[J].电力系统保护与控制,2012,40(8)：110-120.
[15]Zhu Zhi-hui, Sun Yun-lian, LI Huang-qiang. Hybrid of EMD and SVMs for short-term load forecasting[C].IEEE International Conference on Control and Automation.2007:1044-1047.
[16]王洪波,朱启兵.基于EMD和LS-SVM的非平稳振动信号趋势预测[J].计算机工程与应用,2008,44(16)：157-159.
[17]谢景新,程春田,周桂红,等.基于经验模式分解与混沌分析的直接多步预测模型[J].自动化学报,2008,34(6)：684-689.
[18]Yang J N, Lei Y, Pan SW, et al. System identification of linear structures based on hilbert-huang spectral analysis,part 1:normal modes[J]. Earthquake Engineering and Structural Dynamics,2003,32:1443-1467.
[19]白玮莉,刘志刚,彭权威,周翔.一种基于HHT的电力系统短期负荷预测模型[J].电力系统保护与控制,2011,39(2)：55-61.
[20]Chacn J E, Alberto R C. A note on the universal consistency of the kernel distribution function estimator[J]. Statistics & Probability Letters 2010,80(17-18): 1414-1419.
[21]Guzman M A, Delgado A, De Carvalho J. A novel multiobjective optimization algorithm based on bacterial chemotaxis[J]. Engineering Applications of Artificial Intelligence,2010,23(3):292-301.
[22]Mmaandur K R C, Chenoweth R D. Optimal Control of Reactive Power Flow for Improvements in Voltage Profiles and for Real Loss Minimization[J]. IEEE Transactions on Power Apparatus and Systems,1981,PAS-100(7):3185-3194.
[23]Assef Zobian, Marija D Ilie. A Steady State Voltage Monitoring and Control Algorithm Using Localized Least Square Minimization of Load Voltage Deviations[J]. IEEE Transactions on Power Systems,1996,11(2):929-938.
[24]JohnW. Lamont, Jian Fu. Cost Analysis of Reaetive Power Support[J]. IEEE Transactions on Power Systems,1999,14(3):890-898.
[25]戴彦,倪以信.电力市场下的无功定价研究[J].电力系统自动化,2000,24(5)：9-14.
[26]舒立平,陈允平.一种新的无功电力定价算法[J].电网技术,2004,28(5)：59-63,68.
[26]蒋志平.电力市场下的无功定价研究[D].南京理工大学,2003.
[27]王正风,汪胜和,电力市场下无功定价综述[J].东北电力技术,2009,2：10-13.
[28]于尔铿,韩放,谢开,曹肪.电力市场[M].北京,中国电力出版社,1998.
[29]电力市场下无功定价方法综述[J].企业经营管理,2009,10：265-266.
[30]于辉,赵冬梅.单边开放电力市场下的无功电价研究[J].电网技术,2004,28(14)： 62-67.
[31]李坚.张永平.一种新的无功电力定价研究[J].电网技术,2004,28(5)：59-63.
[32]Choi Joon-Young, Rim Seong-Hwang, Jong-Keun Park. Optimal Real Time Pricing of Real and Reactive Power[J]. IEEE Transactions on Power Systems,1998, 13(4):1226-1231.
[33]舒立平,陈允平.一种新的无功电力定价算法[J].电网技术,2004,28(5)：59-63,68.
[34]代红才,彭建春,杨帮宇,王娜.基于边际成本和潮流追踪的无功定价[J].电网技术,2007,31(7)：59-63.
[35]ChattopadhyayD,Bhattacharya K.Optimal reactive power planning and its spot-pricing:an integrated approach [J]. IEEE Transactions on Power Systems,1995, 10(4):2014-2020.
[36]Wen-Chen Chu. Allocating the Costs of Reactive Power Purchased in an Ancillary Service Market by Modified Y-Bus Matrix Method[J]. IEEE Transactions On Power Systems,2004,19(1):174-179.
[37]Jin Zhong, Bhattaeharya karkar. Toward a Competitive Market for Reactive Power[J]. IEEE Transactions on Power Systems,2002,17(4):1206-1215.
[38]Dandachi N H, Rawlines M J, AlsacO, eta.l OPF for Reactive Pricing Studies on the NGC System [J]. IEEE Transactions on Power Systems,1996,11(1):226-232.
[39]牛怀平,刘俊勇,吴集光,段登伟.电力市场初期两部制无功定价方法[J].电力自动化设备,2006,26(2)：11-15.
[40]牛怀平,刘俊勇,吴集光.一种新的两部制无功定价方法[J].电网技术,2005,29：1-6.
[41]孙飘红,刘俊勇,潘蕾蕾.基于统一优化补偿的无功电力市场[J].电网技术2004,28：16-20.
[42]牛怀平.电力市场初期的无功定价研究[D].四川大学博士学位论文,2005,24-35.
[43]张勇军,任震,李邦峰.电力系统无功优化调度研究综述[J].电网技术,2005,29(2)：50-56.
[44]岳青,魏宇存,孙蕾,刘崇新,张峰,李艳华.榆林电网无功优化运行研究[J].电工电气,2012,3：21-25.
[45]林娇燕.基于蚁群算法的无功优化方法的研究[J].低压电器,2011,20：43-46.
[46]孙建鹏,韩文花,朱长东.电力系统无功优化模型及算法研究[J].电力电容器与无功补偿,2011,32(6)：6-11.
[47]L.D.Arya,S.C.Choube,D.P.Kothari.Reactive power optimization using static voltage stableility index. Electric Power Components and Systems,2001.29(7):615-628.
[48]李亚男,张粒子,杨以涵.考虑电压约束裕度的无功优化及其内点解法[J].中国电机工程学报,2001,21(9)：1-4.
[49]宋军英,刘涤尘,陈允平.电力系统模糊无功优化的建模及算法[J].电网技术,2001,25(3)：22-25.
[50]曾嘉俊,刘志刚,何士玉,杨红梅.一种基于子区域粒子群的无功优化算法研究[J].电力系统保护与控制,2012,40(1)：37-42.
[51]孟祥星.市场环境下电力系统有功调度与无功优化的经济规律研究[D].山东大学,2007.
[52]舒隽,张粒子,刘易,彭永华,郭琳.电力市场下日无功计划优化模型和算法的研究[J].中国电机工程学报,2005,25(13)：80-85.
[53]李智欢.无功优化进化计算的局部搜索策略及多目标处理方法[D].华中科技大学,2010.
[54]孙东杰.电力系统经济调度和无功优化中的多目标算法研究[D].湖南大学,2010
[55]王韶,刘光时,邹青林.计及UPFO的电力系统多目标无功优化[J].电力系统保护与控制,2012,40(8)：95-100.
[56]H.W.Dommel,W.F.Tinney.Option Power Flow Solution.IEEE Transactions on Power System.1968,87(10):1866-1876.
[57]李林川,王建勇,陈礼义,等.电力系统无功补偿优化规划[J].中国电机工程学报,1999,19(2)：66-69.
[58]Ponnambalam K, Quintata V H, Vannelli A.A fast algorithm for Power system Optimization problems using an interior point method[J].IEEE Trans on Power Systems,1992,7(2):892-899.
[59]Jabr R A,Coonick A H,Cory B J.A primal-dual interior point method for Optimal power flow dispatching[J].IEEE Trans on Power Systems,2002,17 (3):654-662.
[60]李宏伟.基于改进遗传算法的电力系统无功优化的研究[J].自动化与仪器表,2011,6：7-11.
[61]杜欢.邻域拓扑粒子群优化算法及其在电力系统无功优化中的应用研究[D].浙江大学,2006.
[62]危雪.基于混沌神经网络的配电网无功优化[J].电气开关,2011,49(5)：55-56.
[63]刘科研,盛万兴,李运华.基于改进遗传模拟退火算法的无功优化[J].电网技术,2007,31(3)：13-18.
[64]任晓莉,程红丽,刘健.基于禁忌搜索算法的变电站电压无功优化控制[J].继电器,2008,36(8)：31-34.
[65]葛莲,理文祥.基于混合智能算法的电力系统无功优化研究[J].江西电力职业技术学院学报,2012,25(1)：1-4.
[66]范征.混合智能优化算法在电力系统无功优化中的应用研究[D].山东大学,2008.
[67]张立,刘洪.城市电网现状无功评价[J].电力系统及其自动化学报,2009,21(2)：115-120.
[68]胡扬,耿静,蒋传文.评价无功优化进化算法的非整秩次秩和比法[J].水电能源科学,2010,28(12)：139-142.
[69]朱景锋.基于三角模糊AHP的物联网电子政务安全评价模型分析[J].制造业自动化,2012,34(4)：90-92.
[70]Yur dakul M. AHP as a strategic decision-making to ol to justify machine tool selection[J]. Jo urnal of Materials Processing Technology,2004,146 (3):365-376.
[71]Wang M, Yu A B, Zhang Y. Grindability evaluation of advanced ceramics with AHP approach[J].Key Engineering Materials,2006,304/305:222-226.
[72]张少敏,栗军,王保义.竞争电力市场下的电网公司经营风险综合评级模型[J].电力系统保护与控制,2011,39(8)：70-74.
[73]金菊良,张礼兵,魏一鸣.水资源可持续利用评价的改进层次分析法[J].水科学进展,2004,15(2)：227-232.
[74]毛安家,何金.一种基于可信性理论的电网安全性综合评估方法[J].电力系统保护与控制,2011,39(18)：80-87.
[75]肖美丹.农机装备结构优化的多层次灰色评价[J].中国农机化,2005.
[76]王佳明,刘文颖,李群炬等.基于多层次模糊综合评价模型的短路电流限制措施优化配置[J].电网技术,2011,35(11)：125-129.
[77]林济铿,李童飞,赵子明等.基于熵权模糊综合评价模型的电力系统黑启动方案评估[J].电网技术,2012,36(2)：115-120.
[78]陈辰,胡甚平.基于模糊DEA的航空公司安全管理有效性评价[J].上海海事大学学报,2012,33(1)：12-19.
[79]Mei L W, Huai S L. Fuzzy data envelopment analysis(DEA):Model and ranking method[J]. J of Computer and Applied Mathematics,2009,223(2):872-878.
[80]王志良,单淑贞,邱林,杨成武.基于混沌优化算法的城市供水绩效DEA评价模型[J].水电能源科学,2007,25(2)：11-13.
[81]王科,魏法杰.三参数区间交叉效率DEA评价力法[J].工业工程,2010,13(2)：19-23.
[82]郭清娥,王雪青,位珍.基于DEA交叉评价的模糊综合评价模型及其应用[J].控制与决策,2012,27(4)：575-579.
[83]陈君,李聪颖,丁光明.基于BP神经网络的高速公路交通安全评价[J].同济大学学报(自然科学版),2008,36(7)927-931.
[84]鲍玉昆,张金隆.基于BP神经网络的科技项目评标模型[J].华中科技大学学报(自然科学版),2002,30(8)：85-87.
[85]张建国,雷雨龙,王健等.基于BP神经网络的换挡品质评价方法[J].吉林大学学报 (工学版),2007,37(5)：1019-1022.
[86]李世武,田晶晶,沙学锋等.基于模糊综合评价和BP神经网络的车辆危险状态辨识[J].吉林大学学报(工学版),011,41(6)：1609-1613.
[87]Lotfollahi-Yaghin, M. A. Artificial neural network ability in evaluation of random wave. Induced Inline Force on A Vertical Cylinder[J].China Ocean Eng,2012,26(1): 19-36.
[88]王雁凌,李艳君,许奇超.改进雷达图法在输变电工程综合评价中的应用[J].电力系统保护与控制,2012,40(5)：119-123.
[89]姜秋香,付强,王子龙.基于粒子群优化投影寻踪模型的区域土地资源承载力综合评价[J].农业工程学报,2011,27(11)：319-324.
[90]欧阳森,廖一键,刘洋等.基于自主式决策的综合评价方法在电能质量领域的分析与运用[J].电网技术,2012,36(2)70-74.
[91]马占新,伊茹.基于经验数据评价的非参数系统分析方法[J].控制与决策,2012,27(2)：199-204.
[92]崔勇,洪浩,肖君.大型变压器运行效率改造优化设计[J].高压电器,2012,(9)：27-30.
[93]崔勇,王川,杨锦红.断路器控制回路隐性故障检修方法[J].高压电器,2011,(12)：96-99.
[94]崔勇,陈湘华,王川.谐波测试对变电设备绝缘特性的影响研究[J].电测与仪表,2012,49(10)：5-8.
[95]黄元生,崔勇,洪浩.特高压变电站无功装置主断路器合闸控制优化改进及效益评估[J].高压电器,2013,49(6)：57-61.
[96]崔勇,王川,牛满萍.10kV电容器组专用断路器控制回路优化设计[J].工矿自动化,2012,(10)：67-69.
[97]黄元生,崔勇,李建一.安全约束条件下的经济调度优化模型研究[J].运筹与管理,2014,(2)：10-12.
[98]Yong Cui, Yuan-sheng Huang, Xin-bo Dai. Evaluation and Analysis of Competitiveness in Market Industry [J]. The 19th International Conference on Industrial Engineering and Engineering Management,2013.
[99]谢亚军.综合评价理论、方法及应用[M].北京：科学出版社,2007.
[100]邓佳佳.考虑分布式能源的电力系统优化运营模型研究[D].华北电力大学,2012