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智能电网运营管理风险元传递模型及决策支持系统研究
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摘要
为了应对全球气候恶化与提高能源利用效率,各国电力工业都开始在智能电网方面做出探索与实践。智能电网实施给电力系统的各运营管理主体带来新的挑战,特别是可再生能源参与发电侧运营管理并接入电网,电力调度由电量调度转变为负荷调度,用户侧通过智能双向互动体系参与电网运营管理。相比传统电力系统,智能电网实现了电力流.信息流、资金流的高度融合,给电力系统注入了新的活力,同时在某种程度上也增加了各运营管理主体的风险性。针对这一新形势,本文基于电力风险元传递理论,从运营管理主体维入手,分别对智能电网环境下发电侧、电网侧、用户侧的运营管理风险元传递情形进行建模分析,进而探讨了基于风险元传递模型的智能电网运营管理决策支持系统。论文的研究成果主要体现在以下五个方面:
     (1)对智能电网运营管理风险元理论进行了初探,提出了智能电网运营管理风险元传递三维建模思路。借鉴电力风险元传递理论,对智能电网运营管理风险进行分析,从风险元传递参与主体维、风险元传递方法维以及风险元传递路径维三个方面,提出了智能电网运营管理风险元传递三维一体建模思路。
     (2)考虑智能电网环境下可再生能源参与的发电侧运营管理,以风力发电为例进行研究,构建风力发电独自运营管理风险元传递模型与风-火联合运营风险元传递模型。风力发电独自运营管理抓住风电上网电价与风电上网电量两条主线,分别构建风险元传递模型进行运营管理风险分析。为了弥补风力发电的间歇性、随机性,采用风-火联合运营管理模式,从负荷预测风险元、燃料价格风险元、上网电价风险元以及风电出力风险元四个方面入手,构建风-火联合运营管理风险元传递模型,分析了多风险元波动对整体收益的影响。
     (3)针对智能电网环境下电网侧运营管理面临的新挑战,从智能电网投资项目、负荷预测、市场购电、智能调度以及信息安全等,分别构建电网侧运营管理风险元传递模型。根据智能电网投资项目资金分配情况,从自然风险元、管理风险元、技术风险元、市场经济风险元以及政策风险元五方面入手,构建层次型风险元传递模型。考虑智能电网的实施对负荷预测提出更高的要求,提出了更为精确的基于MFGM的智能电网负荷风险元预测模型。针对智能电网环境下市场购电发生的新变化,同时考虑到负荷预测、上网电价以及可再生能源出力不确定性,构建智能电网下考虑风险元传递的市场购电优化模型。针对智能电网调度主观信息的不确定性对调度结果产生重要影响,构建了考虑信息不确定性的智能电网调度风险元传递模型。针对智能电网业务信息传递过程的不确定性,采用吸收马尔科夫链理论构建智能电网信息安全风险元传递模型,分析由于信息系统发生安全事件而导致的后果严重程度。
     (4)构建了考虑用户侧参与的智能电网运营风险传递模型。针对基于激励与价格两类需求响应方式,分别选取可中断负荷与峰谷分时电价作为典型代表,构建了考虑风险元传递的可中断负荷参与系统备用配置模型、考虑峰谷分时电价实施的电网收益风险元传递模型,对智能电网用户通过需求响应方式参与运营管理进行风险分析。针对用户侧供电方面,以分布式电源与电动汽车参与微电网运营管理为例,构建考虑成本、排污以及风险(风险元传递)的微电网运营多目标优化模型,并提出小生境多目标粒子群算法进行求解。
     (5)研究了基于风险元传递模型的面向电网侧的智能电网运营管理风险决策支持系统(SGOM-RDSS)。为提高智能电网运营管理决策效率,基于本文已提出的风险元传递模型,以电网侧作为运营管理主体为例,探讨了基于模型驱动的智能电网运营管理决策支持系统的设计。在对SGOM-RDSS功能需求分析的基础上,对该系统的架构设计与功能设计进行研究,并探讨了SGOM-RDSS的重要风险元提取、数据交互及模型库自定义等配套关键技术。
In order to cope with global climate change and improve energy utilization efficiency, power industries around the world have begun to carry out exploration and practice in the smart grid. For the operational management subjects of power system, smart grid brings them some new challenges as renewable energy participates in the operational management of generation side and connects to the grid, power dispatching changes from energy scheduling to load scheduling, and the user side involves in the operational management through the intelligent two-way interactive system. Compared with traditional power system, smart grid makes power flow, information flow and fund flow blend together, which injects new vitality into the power system as well as increases some risk to the operational management subjects.In this new situation, based on the theory of power risk element transmission, this dissertation makes modeling analysis of the operational management risk element transmission in the generation side, grid side and user side respectively under the environment of smart grid from the operational management subject dimension. And then the smart grid operational management decision support system (SGOM-RDSS) based on risk element transmission model is discussed. The research achievements of this thesis are mainly reflected in the following five aspects:
     (1) The smart grid operational management theory was studied, and the three-dimensional modeling of smart grid operational management risk element transmission was put forward. Drawing upon the power risk element transmission theory, and combined with the analysis of smart grid operational management risk, this paper proposed the three-dimensional modeling approach of smart grid operational management risk element transmission from the demission of smart grid operational management risk transmission participants, the demission of risk element transmission methods and the demission of risk element transmission path.
     (2) Considering the participation of renewable energy in the operational management in generation side under the environment of smart grid, and taking the wind power as an example, the risk element transmission models of wind power's own operational management and wind-fire joint operational management were constructed. As the two main lines of wind power's own operational management, the wind power feed-in tariff and grid electricity were discussed respectively to build risk element transmission models and make operational management risk analysis. To make up for the intermittency and randomness of wind power, the wind-fire joint operational management pattern was adopted. Starting from load forecast risk element, fuel price risk element, feed-in-tariff risk element and wind power output risk element, the risk element transmission model of wind-fire joint operational management was put forward to study the influence of risk element fluctuation on its overall revenue.
     (3) Considering the new challenges of smart grid to operational management in grid side, risk element transmission model of operational management in grid side was built from five aspects of smart grid investment projects, load forecast, market purchasing of power, intelligent scheduling and information security. For the funds allocation of smart grid investment projects, the hierarchical risk element transmission model was proposed considering nature risk element, management risk, technology risk element, market economy risk element and policy risk element. As the implementation of smart grid proposes higher demand to load forecast, the smart grid load risk element forecast model based on MFGM was put forward. In view of the changes of market purchasing under the circumstance of smart grid, based on load forecast, feed-in-tariff and renewable energy output uncertainty, market purchasing optimization model considering risk element transmission was constructed. As to the important influence of smart grid scheduling subjective information's uncertainty on scheduling results, the smart grid scheduling risk element transmission model considering information uncertainty was established. Considering the uncertainty of smart grid business information transfer process, the information security risk element transmission model employing absorbing Markov Chain was present to analyze the severity of consequences due to the security incident of information system.
     (4) The smart grid operational risk transmission model considering the participation of user side was put forward. With regard to two demand response ways based on motivation and price, interruptible load and peak-valley tou price were taken as typical examples to build system backup configuration model with the participation of interruptible load and considering risk element transmission, as well as the grid revenue risk element transmission model considering the implementation of peak-valley tou price, to do risk analysis of users'participation in operational management. In the aspect of power supply in user side, this paper studied distributed generation and electric car's participation in the operational management of micro grid. Then the multi-objective optimization model of micro grid operational management considering cost, pollution and risk (risk element transmission) was constructed and niche particle swarm optimization algorithm was used to solve the model.
     (5) The SGOM-RDSS based on risk element transmission model was studied. To improve the decision efficiency of smart grid operational management, considering the risk element transmission model mentioned in this paper, the research on the design of model-driven SGOM-RDSS was carried out. Based on the functional requirements analysis, the architecture design and function design of SGOM-RDSS were put forward, and the key matching technologies such as the extraction of important risk elements, data interaction and model self-defining were discussed.
引文
[1]何光宇,孙英云.智能电网基础[M].北京:中国电力出版社,2010
    [2]高涛,邓玲.智能电网及其国内外发展概述[J].东北电力技术.2012(2):0005-0011
    [3]时建锋.基于智能电网的风险管理研究[J].中国电力教育.2013(18):037-039
    [4]李存斌.项目风险元传递理论与应用[M].北京:中国水利水电出版社.2009
    [5]李存斌.电力风险元传递理论与应用[M].北京:中国电力出版社.2013
    [6]李韩房,谭忠富,王成文等.电力市场环境下基于解释结构模型的发电企业风险结构分析[J].电网技术.2007,31(13):0059-0065
    [7]张娟,谭忠富.发电企业风险识别与评价[J].电力技术经济.2003,15(6):036-042
    [8]刘艳,王黎,马光文.发电企业上网电价风险因子识别[J].水力发电.2007,33(1):0004-0007
    [9]罗志猛,周建中,张勇传等.应用网络分析法的发电企业运营绩效综合评价[J].电网技术.2010,34(9):0042-0048
    [10]Xin Jin, Xin. Zhou Operating Risk Evaluation of Thermal Power Enterprises based on Literature Content Analysis [J]. Environmental Sciences,2011,11(a):11-17
    [11]梁榕珊,王江波,丁玉珏.基于改进密切值法的火力发电企业生产运营对标管理研究[J].华东电力.2010,3(11):1663-1667
    [12]Hong-xing Sun, Zhong-fu Tan. Models of Risk Measurement and Control in PowerGeneration Investment [J]. Systems Engineering Procedia,2012(3):125-131
    [13]施泉生,吴基灵.发电企业竞价上网的经济分析与风险控制[J].改革与战略.2008,24(5):0114-0117
    [14]翟星.刘剑清.电力期货市场对发电企业的影响探讨[J].能源技术经济.2011,23(10):0024-0028
    [15]叶泽,李敏杰.基于差价合约的发电企业中长期合同市场交易策略分析[J].电力技术经济.2008,20(1):0024-0027
    [16]戴秦,严广乐,丁会凯.电力期货电价风险控制机制研究[J].华东电力.2009,37(5):0731-0735
    [17]朱逢民.火力发电企业动态成本分析[J].广西电力.2011,34(2):0015-0017
    [18]曾雪峰.基于交易成本理论的发电企业煤电一体化决策研究[J].中国电力教育.2010(15):236-238
    [19]严菲,谭忠富.竞价环境下燃煤发电企业降低发电成本的储煤优化模型[J].电力学报.2008,23(1):005-009
    [20]王高琴,沈炯,刘西陲.基于市场影响力评估的发电企业分类竞价决策[J].电力系统自动化.2008,32(5):47-53
    [21]韩冰,张粒子.基于激励理论的发电企业上网电价定价方法研究[J].华北电力大学学报.2008,35(3):0028-0033
    [22]屈可丁,金福国.基于博弈论的发电企业竞价策略模型[J].东北电力技术.2007(7):0051-0052
    [23]陈广娟,谭忠富,郭联哲等.发电企业上网电价定价模型及其对利润的影响分析[J].现代电力.2007,24(4):0084-0091
    [24]V.B.A. Kasangaki, H.M. Sendaula, S.K. Biswas.Stochastic electric power system production costing and operations planning using a Hopfield artificial neural network[J]. Electric Power Systems Research 33 (1995) 227-234
    [25]M. Mazumdar, A. Kapoor. Stochastic models for power generation system production costs[J]. Electric Power Systems Research 35 (1995) 93-100
    [26]Antonio J. Conejo, Javier Contreras, Rosa Espi'nola, Miguel A. Plazas.Forecasting electricity prices for a day-ahead pool-based electric energy market[J]. International Journal of Forecasting 21 (2005) 435-462
    [27]DeqiangGan, Chen Shen.A price competition model for power and reserve market auctions[J]. Electric Power Systems Research 70 (2004) 187-193
    [28]H.Y. Yamin, S.M. Shahidehpour, Z. Li.Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets [J]. Electrical Power and Energy Systems 26 (2004) 571-581
    [29]V. Vahidinasab, S. Jadid, A. Kazemi.Day-ahead price forecasting in restructured power systems using artificial neural networks[J]. Electric Power Systems Research 78(2008) 1332-1342
    [30]Ying Li, Peter C. Flynn. Deregulated power prices:comparison of volatility [J]. Energy Policy 32 (2004) 1591-1601
    [31]黄大为,韩学山,郭志忠.发电企业多交易市场发电量优化分配策略[J].电力系统及其自动化学报.2008,20(4):0012-0017
    [32]P.K.Marhavilas,D.E.Koulouriotis.A combined usage of stochastic and quantitative risk assessment methods in the worksites:Application on an electric power provider [J]. Reliability Engineering and System Safety 97 (2012) 36-46
    [33]NarcisNabona, Adela Pages. A three-stage short-term electric power planning procedure for a generation company in a liberalized market [J]. Electrical Power and Energy Systems 29 (2007) 408-421
    [34]Tor ArntJohnsen. Demand, generation and price in the Norwegian market for electric power[J]. Energy Economics 23(2001) 227-251
    [35]周明,聂艳丽,李庚银,等.电力市场下长期购电方案及风险评估[J].中国电机工程学报,2006,26(6):116-122
    [36]张钦,王锡凡,王建学.需求侧实时电价下供电商购售电风险决策[J].电力系统自动化,2010,34(3):22-28
    [37]柳瑞禹,邱丹.应用资本收益率风险控制方法的电网企业多市场购电组合优化[J].电网技术,2011,35(1):203-208
    [38]陈彦州,赵俊华,文福拴等.偏度风险价值下供电公司/电力零售公司动态购电组合策略[J].电力系统自动化,2011,35(6):025-029
    [39]牛东晓,曹树华,赵磊,等.电力负荷预测技术及其应用[M].北京:中国电力出版社,1998
    [40]张世英,李克民,袁学民.经济计量学教程[M].天津:天津大学出版社,2002
    [41]牛东晓,吕佳良.关联分析在电力负荷灰色神经网络预测中的应用[J].华东电力,2007,35(8):60-62
    [42]吴丹,程浩忠.基于模糊层次分析法的年最大电力负荷预测[J].电力系统及其自动化学报,2007,19(1):55-58
    [43]陈柔伊,张尧,武志刚,等.改进的模糊聚类算法在负荷预测中的应用[J].电力系统及其自动化学报,2005,17(3):73-77
    [44]魏伟,朱东晓,常征.负荷预测技术的新进展[J].华北电力大学学报,2002,29(1):10-15
    [45]肖智,叶世杰.短期电力负荷预测的粗糙集方法[J].系统工程学报.2009,24(2):0143-0149
    [46]赵登福,庞文晨,张讲社等.基于贝叶斯理论和在线学习支持向量机的短期负荷预测[J].中国电机工程学报.2005,25(13):0008-0014
    [47]师彪,李郁侠,于新花等.基于改进粒子群-模糊神经网络的短期电力负荷预测[J].系统工程理论与实践.2010,30(1):0157-0167
    [48]李莉,刘建勋,刘崇新.基于改进型遗传算法的混沌神经网络在电力负荷预测的应用[J].华中电力.2010,23(2):0013-0018
    [49]张志明,金敏.基于灰关联分段优选组合模型的短期电力负荷预测研究[J].电工技术学报.2009,24(6):115-120
    [50]CHIH-CHOU CHIU,LING-JING KAO. Combining a Neural Network with a Rule-Based Expert System Approach for Short-Term Power Load Forecasting in Taiwan [J]. Expert Systems with Applications,1997,13(4):299-305
    [51]NimaAmjady, FarshidKeynia. Mid-term load forecasting of power systems by a new prediction method[J]. Energy Conversion and Management 49 (2008) 2678-2687
    [52]蒋燕,王少杨,封芸.基于递归等权组合模型的中长期电力负荷预测[J].电力系统及其自动化学报.2012,24(1):0151-0155
    [53]牛东晓,谷志红,王会青,王维军.基于灰色支持向量机的季节型负荷预测方法[J].华东电力.2007,3(6):0001-0005
    [54]毛李帆,江岳春,龙瑞华等.基于偏最小二乘回归分析的中长期电力负荷预测[J].电网技术.2008,32(19):0071-0078
    [55]任玉珑,刘焕,望玉丽等.基于熵权法和支持向量机的中长期电力负荷预测[J].统计与决策.2009(14):0046-0049
    [56]DongxiaoNiu, Jinchao Li, Jinying Li, Da Liu. Middle-long power load forecasting based on particle swarm optimization [J]. Computers and Mathematics with Applications 57 (2009) 1883-1889
    [57]王祥龙.基于Elman神经网络的负荷预测研究[J].华北电力技术.2008(11):001-003
    [58]何永秀,陶卫君,杨卫红等.基于解释结构模型的城市电力负荷预测[J].电力系统自动化.2009,33(20):037-042
    [59]EisaAlmeshaiei, Hassan Soltan. A methodology for Electric Power Load Forecasting [J]. Alexandria Engineering Journal (2011) 50,137-144
    [60]S.Sp. Pappas, L. Ekonomou, P. Karampelas, D.C. Karamousantas, S.K. Katsikas.Electricity demand load forecasting of the Hellenic power system using an ARM A model [J]. Electric Power Systems Research 80 (2010) 256-264
    [61]DongxiaoNiu, Yongli Wang, Desheng Dash Wu. Power load forecasting using support vector machine and ant colony optimization [J]. Expert Systems with Applications 37 (2010) 2531-2539
    [62]宋艺航,谭忠富等.需求侧峰谷分时电价对供电公司购售电风险影响分析模型.电工技术学报,2010,25(11):183-190
    [63]平仙,施应玲.基于期货套期保值策略的电价风险防范研究[J].电力技术经济.2005,17(3):024-029
    [64]王绵斌,谭忠富,李雪.供电公司实行峰谷分时电价的风险价值计算模型[J].电网技术.2007,3(9):0043-0048
    [65]张荣乾.供电企业执行峰谷分时电价的风险分析及实证研究[D].华北电力大学硕士学位论文.2006
    [66]陈伟.基于分形和金融理论的电价风险管理研究[D].华东科技大学硕士学位论文.2007
    [67]盛方正,季建华.基于奇异期权的供电公司风险规避策略[J].现代电力.2008,25(1):0088-0093
    [68]王绵斌.市场环境下供电公司的电价风险控制优化模型[D].华北电力大学博士学位论文.2009
    [69]Valery A. Kholodnyi. Modeling power forward prices for power with spikes:a non-Markovian approach [J]. Nonlinear Analysis 63 (2005) 958-965
    [70]ErkanErdogdu. The impact of power market reforms on electricity price-cost margins and cross-subsidy levels:Across country panel data analysis [J]. Energy Policy 39 (2011) 1080-1092
    [71]严正,罗玮,杨立兵等.基于市场力指标的输电容量充裕度评估方法[J].中国电机工程学报,2009,26(19):75-81
    [72]Min Liu, Felix F. Wu. Portfolio optimization in electricity markets.Electric Power Systems Research,2007,77(8):1000-1009
    [73]N.M. Pindoriya, S.N. Singh, S.K. Singh. Multi-objective mean-variance-skewness model for generation portfolio allocation in electricity markets.Electric Power Systems Research,2010,80(10):1314-1321
    [74]陈启鑫,康重庆,夏清等.电力行低碳化的关键要素分析及其对电源规划的影响[J].电力系统自动化,2009,33(15):18-23
    [75]张健,廖胡.碳税与碳排放权交易对中国各行业的影响.现代化工,2009,29(6):77-82
    [76]Min Liu, Felix F. Wu.Risk management in a competitive electricity market [J]. International Journal of Electrical Power & Energy Systems,2007,29(9):690-697
    [77]Masao Nakamura, TomoakiNakashima, TakahideNiimura. Electricity markets volatility:estimates, regularities and risk management applications[J].Energy Policy, 2006,34(14):1736-1749
    [78]Ruibal C M, Mazumdar M. Forecasting the mean and the variance of electricity prices in deregulated markets. IEE Transmission on Power Systems, 2008,23(1):25-32
    [79]LudvikBartelj, DejanParavan, Andrej F. Gubina, Robert Golob. Valuating risk from sales contract offer maturity in electricity market[J]. International Journal of Electrical Power & Energy System,2010,32(2):147-155
    [80]ParomaSanyal, Laarni T. Bulan. Regulatory risk, market uncertainties, and firm financing choices:Evidence from U.S. Electricity Market Restructuring[J].The Quarterly Review of Economics and Finance,2011,51(3):248-268
    [81]Santiago Medina, Julidn Moreno. Risk evaluation in Colombian electricity market using fuzzy logic[J].Energy Economics,2007,29(5):999-1009
    [82]贺春,吴战江,李鑫.电力企业客户信用风险管理探讨[J].电力技术经济.2004,16(1):0049-0053
    [83]A.A.Chowdhury, T.C.Mielnik, L.E.Lawton, M.J.Sullivan, A.Katz. System reliability worth assessment at a midwest utility--survey results for residential customers [J].International Journal of Electrical Power & Energy Systems,2005, 27(9-10):669-673
    [84]Michael J.Sullivan,TerryVardell,MarkJohnson.Power interruption costs to industrial and commercial consumers of electricity[J].IEEE Transactions on Industry Applications,1997,33(6):1448-1558
    [85]Ali S A, Wacker G, Bilinton R. Determintation and use of sector and composite customer damage function[C]. Canadian conference on Electrical and computer Engineering 1999:1483-1488
    [86]赵珊珊,张东霞,印永华.智能电网的风险评估[J].电网技术.2009,33(19):007-011
    [87]曾鸣,陈英杰,胡献忠,董达鹏.基于多层次模糊综合评价法的我国智能电网风险评价[J].华东电力.2011,39(4):535-540
    [88]孙强,张义斌,韩冬等.多因素、多维度的智能电网风险评估[J].电网技术.2012,36(9):0051-0055
    [89]王蓓蓓,李扬.面向智能电网的电力需求侧管理规划及实施机制[J].电力自动化设备,2010,30(12):19-24
    [90]于娜,于继来.智能电网环境下需求响应参与系统备用的风险协调优化模型[J]电力系统保护与控制.2010,38(21):77-82
    [91]刘念,张建华.互动用电方式下的信息安全风险与安全需求分析[J].电力系统自动化,2011,35(2):79-81
    [92]郎为民,杨德鹏,李虎生.基于SIR模型的智能电网WCSN数据伪造攻击研究[J].技术研究.2012(1):014-016
    [93]徐娟,孙大伟.智能电网——大规模风、光电并网瓶颈问题的解决方案[J].宁夏 电力.2012(1):011-014
    [94]张粒子,周娜,王楠.大规模风电接入电力系统调度模式的经济性比较[J].电力系统自动化.2011,35(22):105-110
    [95]周任军,姚龙华,童小娇等.采用条件风险方法的含风电系统安全经济调度[J].中国电机工程学报.2012,32(1):0056-0063
    [96]张爽,董仕镇,和识之.智能电网背景下大规模风电接入技术探讨[J].广东电力.2011,24(11):048-051
    [97]王帅,李鹏,崔红芬.风电以微网的形式并入智能电网的研究[J].电气技术.2010(8):038-042
    [98]冯松起,罗卫华,施毅斌.基于智能电网调度技术支持系统的电网风险防范措施研究[J].东北电力技术.2011(6):001-007
    [99]梅生伟,王莹莹,陈来军.从复杂网络视角评述智能电网信息安全研究现状及若干展望[J].高电压技术.2011,37(3):672-080
    [100]张钦,王锡凡,付敏等.需求响应视角下的智能电网[J].电力系统自动化.2009,33(17):049-055
    [101]贾文昭,康重庆,刘长义等.智能电网促进低碳发展的能力与效益测评模型[J].电力系统自动化.2011,35(1):007-012
    [102]王允平,黄殿勋,熊浩清等.智能电网环境下采用关联分析和多变量灰色模型的用电量预测[J].电力系统保护与控制.2012,4(1):096-100
    [103]葛少云,贾鸥莎,刘洪.基于遗传灰色神经网络模型的实时电价条件下短期电力负荷预测[J].电网技术.2012,36(1):0224-0230
    [104]Zio E, Aven T. Uncertainties in smart grids behavior and modeling:What are the risks and vulnerabilities? How to analyze them? [J]. Energy Policy,2011, 39(10):6308-6320.
    [105]Varaiya P P, Wu F F, Bialek J W. Smart Operation of Smart Grid:Risk-Limiting Dispatch[C]. Proceedings of the IEEE,2011,99(1):40-57.
    [106]Hahn A. Smart Grid architecture risk optimization through vulnerability scoring[C].IEEE Conference on Innovative Technologies for an Efficient and Reliable Electricity Supply,2010,36-41.
    [107]Bialek J W, Varaiya P, Wu F, et al. Risk-limiting dispatch of smart grid[C]. IEEE Power and Energy Society General Meeting,2010,1-2
    [108]Afzal S. Siddiqui, Karl Maribu. Investment and upgrade in distributed generation under uncertainty[J]. Energy Economics,2009,31(1):25-37
    [109]Afzal S. Siddiqui, Chris Marnay. Distributed generation investment by a micro grid under uncertainty[J].Energy,2008,33(12):1729-1737
    [110]VladislavAkhmatov, Hans Knudsen. Large penetration of wind and dispersed generation into Danish power grid[J]. Electric Power Systems Research 77 (2007) 1228-1238
    [111]Bruno Soares M.C. Borba, AlexandreSzklo, Roberto Schaeffer.Plug-in hybrid electric vehicles as a way to maximize the integration of variable renewable energy in power systems:The case of wind generation in northeastern Brazil[J]. Energy 37 (2012)469-481
    [112]Fernando Olsina, Mark Roscher, Carlos Larisson, Francisco Garces.Short-term optimal wind power generation capacity in liberalized electricity markets[J]. Energy Policy 35 (2007) 1257-1273
    [113]Daniele Menniti, Nadia Scordino, Nicola Sorrentino. Secure and economic management of a power system in the presence of wind generation[J]. Electric Power Systems Research 80 (2010) 1375-1383
    [114]Hsing Hung Chen, He-Yau Kang, Amy H.I. Lee.Strategic selection of suitable projects for hybrid solar-wind power generation systems[J]. Renewable and Sustainable Energy Reviews 14 (2010) 413-421
    [115]Mendez V H, Rivier J, Fuente J I et al. Impact of distributed generation on distribution investment deferral[J].International Journal of Electrical Power & Energy Systems,2006,28(4):244-252.
    [116]倪敬敏,何光宇,沈沉等.美国智能电网评估综述[J].电力系统自动化.2010,34(8):0009-0013
    [117]杜文娟,王海风,陈中.英国智能电网研究综述[J].电网技术.2009,33(20):0009-0012
    [118]Yanshan Yu, Jin Yang, and Bin Chen. The Smart Grids in China-A Review [J]. Energies,2012(5):1321-1338
    [119]余贻鑫,栾文鹏.智能电网的基本理念[J].天津大学学报.2011,44(5):0377-0384
    [120]陈树勇,宋书芳,李兰欣等.智能电网技术综述[J].电网技术.2009,33(8):0001-0007
    [121]常康,薛峰,杨卫东.中国智能电网基本特征及其技术进展评述[J].电力系统自动化,2009,33(17):010-015
    [122]鞠平,秦川,黄桦,吴峰,金宇清.面向智能电网的建模研究展望[J].电力系统自动 化,2012,36(11):001-006
    [123]张曼.智能电网环境下对电网调度管理的思考[C].战略性新兴产业的培育和发展——首届云南省科协学术年会论文集.2011:506-509
    [124]吕春泉,厉一梅,刘宏志等.智能电网环境下可再生能源发电并网机制研究[J].华东电力,2011,39,(9):1405-1409
    [125]姚兴佳,刘国喜,朱家玲等.可再生能源及其发电技术[M].北京:科学出版社,2010
    [126]THRESHER R, ROBINSON M, VEERS P. To capture the wind[J]. Power and Energy Magazine, IEEE,2007,5(6):34-39
    [127]黄彦瑜,何祚庥.可再生能源发展与电网管理理念的初步探讨[J].科学对社会的影响,2007,(2):028-032
    [128]Cun-bin Li, Peng Li, Xia Feng. Analysis of wind power generation operation management risk in China[J]. Renewable Energy 64 (2014) 266-275
    [129]Xiaoli Zhao, FengWang, MeiWang. Large-scale utilization of wind power in China: Obstacles of conflict between market and planning [J]. Energy Policy.article in press
    [130]Guo-liangLuo, FeiZhi, Xinying Zhang. Inconsistencies between China's wind power development and grid planning:An institutional perspective[J].Renewable Energy 48 (2012) 52-56
    [131]Xiaoli Zhao, SufangZhang, RuiYang, MeiWang. Constraints on the effective utilization of wind power in China:An illustration from the northeast China grid [132].Renewable and Sustainable Energy Reviews 16 (2012) 4508-4514
    [133]Mian Yang, Dalia Patino-Echeverri, Fuxia Yang. Wind power generation in China: Understanding the mismatch between capacity and generation [J].Renewable Energy 41 (2012)145-151
    [134]Jingyi Han, ArthurPJ.Mol, YonglongLu, LeiZhang. Onshore wind power development in China:Challenges behind a successful story[J].Energy Policy 37 (2009)2941-2951
    [135]Dayang Yu, JunLiang, XueshanHan, JianguoZhao. Profiling the regional wind power fluctuation in China[J]. Energy Policy 39 (2011) 299-306
    [136]ZhenYu Zhao, JiHua, JianZuo. Performance of wind power industry development in China:A Diamond Model study[J].Renewable Energy 34 (2009) 2883-2891
    [137]CuipingLiao, EberhardJochem, Yi Zhang, Nida R. Farid. Wind power development and policies in China[J].Renewable Energy 35 (2010) 1879-1886
    [138]X. Li, K. Hubacek, Y.L. Siu. Wind power in China e Dream or reality?[J].Energy 37(2012)51-60
    [139]张正敏.中国风力发电经济激励政策研究[M].北京:中国环境科学出版社,2002
    [140]赵珊珊,张东霞,印永华等.风电的电价政策及风险管理策略[J].电网技术.2011,35(5):0142-0145
    [141]谢宏文,易跃春.风电项目2种电价测算方法的比较[J].国际电力2004,8(1):0035-0037
    [142]中国风电及电价发展研究报告[R].中国可再生能源专业委员会.2009年
    [143]Yingqi Liu, AriKokko. Wind power in China:Policy and development challenges [J].Energy Policy 38 (2010) 5520-5529
    [144]张正敏,谢宏文,王白羽.风电电价分析与政策建议[J].中国电力.2001年9月.第34卷第9期:0044-0048
    [145]朱柯丁.节能减排环境下电网企业经营风险控制方法研究[D].华北电力大学博士学位论文.2011年
    [146]胡艳梅,吴俊勇.含间歇式电源电力系统风险评估的研究综述[J].冶金电气2012(2):89-92
    [147]Clara Novoa, TongdanJin.Reliability centered planning for distributed generation considering wind power volatility [J].Electric Power Systems Research 81 (2011) 1654-1661
    [148]Wei Zhou, Hui Sun and Yu Peng. Risk Reserve Constrained Economic Dispatch Model with Wind Power Penetration [J].Energies.2010,3,1880-1894
    [149]申洪,王伟胜.一种评价风电场运行情况的新方法[J].中国电机工程学报.2003,23(9):090-098
    [150]EhsanAlishahi, MohsenP.Moghaddam, MohammadK.Sheikh-El-Eslami. An investigation on the impacts of regulatory interventions on wind power expansion in generation planning [J]. Energy Policy 39 (2011) 4614-4623
    [151]Cun-bin Li, Gong-shu Lu, Si Wu.The investment risk analysis of wind power project in China[J].Renewable Energy 50 (2013) 481-487
    [152]T. Jin, Z. Tian, Uncertainty analysis for wind energy production with dynamic power curves[C]. Proceedings of Probabilistic Modeling Applied to Power Systems, 2010,745-750
    [153]廖怀庆,刘东,黄玉辉等.基于大规模储能系统的智能电网兼容性研究[J].电力系统自动化,2010,34(2):0015-0019
    [154]于晗,钟志勇,黄杰波等.考虑负荷和风电出力不确定性的输电系统机会约束 规划[J].电力系统自动化,2009,33(2):020-024
    [155]陈广娟,谭忠富,郭联哲等.煤电价格联动下火力发电企业的风险分析模型[J].现代电力,2007,24(2):0074-0079
    [156]郭联哲,李晓军,谭忠富.煤价波动对火电厂上网电价影响的数学模型及动态分析[J].电网技术,2005,29(7):0007-0012
    [157]玉华,周任军,韩磊等.基于CVaR的风电并网发电风险效益分析[J].电力系统保护与控制,2012,40(4):043-047
    [158]Eduardo M. Gouveia, Manuel A. Matos. Evaluating operational risk in a power system with a large amount of wind power [J]. Electric Power Systems Research 79 (2009) 734-739
    [159]Lingfeng Wang, Chanan Singh. Balancing risk and cost in fuzzy economic dispatch including wind power penetration based on particle swarm optimization [J]. Electric Power Systems Research 78 (2008) 1361-1368
    [160]Dayang Yu, BoZhang,JunLiang,XueshanHan.The influence of generation mix on the wind integrating capability of North China power grids:A modeling interpretation and potential solutions[J]. Energy Policy 39 (2011)7455-7463
    [161]王成文,王绵斌,谭忠富等.上网电价服从布朗运动条件下的销售电价计算模型[J].电网技术,2008,32(9):021-027
    [162]朱兆霞,邹斌.PJM日前市场电价的统计分析[J].电力系统自动化,2006,30(23):053-057
    [163]王剑辉.电力市场中购电风险模型分析[J].电网技术.2005,29(9):0046-0050
    [164]李传健,刘前进.考虑风力发电随机性的配电网重构[J].电力系统自动化.2010,34(20):034-039
    [165]刘健,武晓朦.余健明.考虑负荷不确定性和相关性的配电网络重构[J].电工技术学报.2006,21(12):054-059
    [166]赵会茹,闫茹.基于风险效益均衡的电煤差价合约研究[J].技术经济,2009,28(7):0038-0042
    [167]王建军.智能电网环境下的自适应互动智能负荷预测研究[J].陕西电力.2010,(5):011-015
    [168]张粒子,黄仁辉.智能电网对电力市场发展模式的影响与展望[J].电力系统自动化.2010,34(8):005-008
    [169]陈来军,梅生伟,陈颖.智能电网信息安全及其对电力系统生存性的影响[J].控制理论与应用.2012,29(2):0240-0244
    [170]王坤.智能电网项目建设风险评估及应对策略研究[D].华北电力大学硕士学位论文.2011年
    [171]M. Nasseri, K. Asghari, M.J. Abedini. Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network [J]. Expert Systems with Applications 35 (2008) 1415-1421
    [172]C.W.M. Yuen, W.K. Wong, S.Q. Qian, L.K. Chan, E.H.K. Fung. A hybrid model using genetic algorithm and neural network for classifying garment defects [J]. Expert Systems with Applications 36 (2009) 2037-2047
    [173]Adel Mellit,1, Soteris A. Kalogirou, Mahmoud Drif. Application of neural networks and genetic algorithms for sizing of photovoltaic systems[J]. Renewable Energy 35 (2010)2881-2893
    [174]Rasoullrani, Reza Nasimi. Evolving neural network using real coded genetic algorithm for permeability estimation of the reservoir[J]. Expert Systems with Applications 38 (2011) 9862-9866
    [175]张鸿彦,林辉.应用混合神经网络和遗传算法的期权价格预测模型[J].管理工程学报.2009,23(1):059-062
    [176]杨梅,卿晓霞,王波.基于改进遗传算法的神经网络优化方法[J].计算机仿真,2009,26(5):198-211
    [177]Sangjae Lee, HyunchuiAhn.The hybrid model of neural networks and genetic algorithms for the design of controls for internet-based systems for business-to-consumer electronic commerce[J]. Expert Systems with Applications 38 (2011)4326-4338
    [178]黄飞华,李四杰.基于神经网络和遗传算法的FMS能力规划问题[J].系统管理学报.2011,20(4):496-502
    [179]程进军,夏智勋,胡雷刚.基于遗传神经网络的航空装备故障预测[J].空军工程大学学报(自然科学版).2011,12(1):015-019
    [180]柴熠,罗锐利,孙大帅.智能电网用户端环境下短期负荷预测方法的研究[J].低压电器.2012,7:008-012
    [181]陈伟淳.基于多神经网络的智能电网短期负荷预测研究[D].华南理工大学硕士学位论文
    [182]陈普.智能电网环境下的短期负荷预测研究及实现[D].华北电力大学硕士学位论文
    [183]胡海琴,蒋传文,蔡宏欣.中长期负荷预测的傅里叶级数残差修正模型[J].安徽电 力.2010,27(2):39-40
    [184]邓聚龙.灰预测与灰决策[M].武汉:华中科技大学出版社.第1版,2002年
    [185]石斌,刘思峰,党耀国,王正新.无偏灰色预测模型递推解法及其优化[J].系统工程理论与实践.2011,3(8):1532-1538
    [186]Yen-Tseng Hsu, Ming-Chung Liu, Jerome Yeh, Hui-Fen Hung. Forecasting the turning time of stock market based on Markov-Fourier grey model [J]. Expert Systems with Applications 36 (2009):8597-8603
    [187]颜伟,文旭,余娟等.智能电网环境下电力市场面临的机遇与挑战[J].电力系统保护与控制.2010,38(24):0224-0230
    [188]余贻鑫,栾文鹏.智能电网述评[J].中国电机工程学报.2009,29(34):0001-0008
    [189]刘亚安,管晓宏.考虑风险因素的两市场购电优化分配问题[J].电力系统自动化,2002,26(9):0001-0004
    [190]郭金,江伟,谭忠富.风险条件下供电公司最优购电问题研究[J].电网技术,2004,28(11):0018-0023
    [191]周明,聂艳丽,李庚银,倪以信.电力市场下长期购电方案及风险评估[J].中国电机工程学报,2006,26(6):116-122
    [192]周明,李庚银,严正,倪以信.考虑备用需求和风险的供电企业最优购电计划[J].电网技术,2005,29(03):0033-06
    [193]王壬,尚金成,周晓阳张勇传,张士军.基于条件风险价值的购电组合优化及风险管理[J].电网技术,2006,30(20):0072-0076
    [194]谢品杰,谭忠富,王绵斌,侯建朝.基于CVaR的供电公司现货市场购电优化决策模型[J].电工技术学报,2009,24(4):186-192
    [195]LudvikBartelj, DejanParavan, Andrej F. Gubina, Robert Golob. Valuating risk from sales contract offer maturity in electricity market[J]. Electrical Power and Energy Systems,2010,32 (2):147-155
    [196]Jianhui Wang, Antonio J. Conejo, Chengshan Wang et.al. Smart grids, renewable energy integration, and climate change mitigation--Future electric energy systems [J]. Applied Energy,2012,96 (8):1-3
    [197]魏玲,杨明皓.输配分离电力市场中含分布式发电的配电公司购电模型[J].电网技术,2008,32(8):071-076
    [198]周玮,孙辉,顾宏,马千,陈晓东.计及风险备用约束的含风电场电力系统动态经济调度[J].中国电机工程学报,2012,32(1):0047-0055
    [199]于晗,钟志勇,黄杰波,张建华.考虑负荷和风电出力不确定性的输电系统机会 约束规划[J].电力系统自动化,2009,33(2):20-24
    [200]瞿勇,张建军,宋业新.多重纳什均衡解的粒子群优化算法[J].运筹与管理,2010,19(2):0052-0055
    [201]马豫超,侯志俭,蒋传文等.基于粒子群算法求解电力市场发电商最优供给函数模型[J].电力系统自动化,2006,30(2):045-050
    [202]王晓佳,张宝霆,徐达宇.含有压缩因子的粒子群优化灰色模型在智能电网中的应用[J].运筹与管理,2012,21(3):0114-0118
    [203]Wang, X. Z., Kerre, E.E.. Reasonable properties for the ordering of fuzzy quantities[J]. Fuzzy Sets and Systems,2001,118(3):375-405
    [204]吴坚.一种新的梯形模糊数互补判断矩阵的排序方法[J].中国管理科学.2010,18(3):095-100
    [205]Chi-Cheng Huang, P in-Yu Chu, Yu-Hsiu Chiang. A fuzzy AHP application in government sponsored R&D project selection[J]. Omega 36 (2008)1038-1052
    [206]徐泽水.模糊互补判断矩阵排序的最小方差法[J].系统工程理论与实践,2001,10(10):93-96
    [207]曹军威,万宇鑫,涂国煜等.智能电网信息系统体系结构研究[J].计算机学报.2013,36(1):143-167
    [208]易锦,罗峋,凹建勋等.基于马尔科夫链的软件故障分类预测模型[J].中国科学院大学学报,2013,30(4):562-567
    [209]Chao Liang, Guang Cheng, Devin L.Wixon et al. An Absorbing Markov Chain approach to understanding the microbial role in soil carbon stablization [J], Biogeochemistry,2011,106(3):303-309
    [210]李同智.灵活互动智能用电的技术内涵及发展方向[J].电力系统自动化.2012,36(2):011-017
    [211]贾东梨,杨旭升,史常凯.智能电网对用户用电的影响[J].电力建设.2011,32(6):0013-0017
    [212]黄莉,卫志农,韦延方等.智能用电互动体系和运营模式研究[J].电网技术.2013,37(8):2230-2237
    [213]袁志坚,刘旭娜.智能电网与电力需求响应的研究[J].四川电力技术.2011,34(5):0001-0004
    [214]杨柳,袁志,张晓冬.微电网技术进展及其对实现智能电网的影响[J].山东电力高等专科学校学报.2011,14(3):003-008
    [215]李士动,施泉生.智能电网下需求响应与可中断负荷研究[J].中国电力教 育.2013(20):0202-0204
    [216]罗运虎,邢丽冬,王勤等.可靠性需求市场中用户的风险决策[J].中国电机工程学报,2008,28(22):0113-0117
    [217]常向伟,张有兵,曹一家等.计及风险因素的事故备用容量购买决策模型研究[J].电力系统保护控制,2010,38(23):82-86
    [218]BAI J, GOOI H B, XIA L M, et al. A probabilistic reserve market incorporating interruptible load [J]. IEEE Trans on Power Systems.2006,21(3):1079-1087
    [219]葛炬,张粒子.可中断负荷参与的备用市场帕累托优化模型[J].电力系统自动化,2006,30(9):034-037
    [220]李晓军,谭忠富,王绵斌等.考虑用户参与下电网公司购买备用的优化模型[J]电力系统及其自动化学报,2007,19(2):009-014
    [221]L.M.Xia, H.B.Gooi, J.Bai. Probabilistic Spinning Reserves with Interruptible Loads[C].IEEE Power Eng.Soc. General Meeting,2004(1):146-152.
    [222]Farrokh Aminifar, Mahmud Fotuhi-Firuzabad, Mohammad Shahidehpour. Unit Commitment with Probabilistic Spinning Reserve and Interruptible Load Considerations[J]. IEEE Trans on Power Systems,2009,24(l):388-397.
    [223]孔祥清,雷霞,刘斌等.节能减排背景下可中断负荷参与系统备用的优化模型[J].电网与清洁能源.2012,28(1):0035-0039
    [224]于娜,于继来.智能电网环境下需求响应参与系统备用的风险协调优化模型[J]电力系统保护与控制.2010,38(21):0077-0082
    [225]张强,韩学山,徐建政.安全经济调度与均匀调度间关系分析[J].电力系统及其自动化学报.2005,17(2):84-89
    [226]GK. Toh, H.B. Gooi. Procurement of interruptible load services in electricity supply systems[J]. Applied Energy 98 (2012) 533-539
    [227]H.A. Aalami, M. Parsa Moghaddam, G.R. Yousefi. Demand response modeling considering Interruptible/Curtailable loads and capacity market programs[J]. Applied Energy 87 (2010) 243-250
    [228]Hunt Allcott, Rethinking real-time electricity pricing [J], Resource and Energy Economics,2011,33(4):820-842
    [229]Y.X.He, L.F.Yang, H.Y.He, Electricity demand price elasticity in China based on computable general equilibrium [J], Energy,2011,36(2):1115-1123
    [230]Ahmad Faruqui, Sanem Sergici, Household response to dynamic pricing of electricity:a survey of 15 experiments [J], Journal of Regulatory Economics,2010, 38(2):193-225
    [231]陈健,王成山,赵波,张雪松,葛晓慧.考虑不同控制策略的独立型微电网优化配置[J].电力系统自动化.2013,37(11):0001-0006
    [232]刘梦璇,郭力,王成山,赵波,张雪松,刘云.风光柴储孤立微电网系统协调运行控制策略设计[J].电力系统自动化,2012,36(15):019-024
    [233]Shin'ya Obara.Analysis of a fuel cell micro-grid with a small-scale wind turbine generator[J]. International Journal of Hydrogen Energy 32 (2007) 323-336
    [234]Aliasghar Baziar, Abdollah Kavousi-Fard. Considering uncertainty in the optimal energy management of renewable micro-grids including storage devices[J]. Renewable Energy 59 (2013) 158-166
    [235]Bhuvaneswari Ramachandran, Sanjeev K. Srivastava, David A. Cartes. Intelligent power management in micro grids with EV penetration[J]. Expert Systems with Applications 40 (2013) 6631-6640
    [236]Amjad Anvari Moghaddam, Alireza Seifi, Taher Niknam. Multi-operation management of a typical micro-grids using Particle Swarm Optimization:A comparative study[J]. Renewable and Sustainable Energy Reviews 16 (2012) 1268-1281
    [237]Amjad Anvari Moghaddam, Alireza Seifi, Taher Niknam, Mohammad Reza Alizadeh Pahlavani. Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source[J]. Energy 36 (2011) 6490-6507
    [238]Taher Niknam, Rasoul Azizipanah-Abarghooee, Mohammad Rasoul Narimani. An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation. Applied Energy 99 (2012) 455-470
    [239]滕乐天,何维国,杜成刚,楼晓东.电动汽车能源供给模式及其对电网运营的影响[J].华东电力,2009,37(10):1675-7677
    [240]马溪原,吴耀文,方华亮等.采用改进细菌觅食算法的风/光/储混合微电网电源优化配置[J].中国电机工程学报,2011,31(25):0017-0025
    [241]Li Cun-bin; Wang Jian-jun. Model of generic project risk element transmission theory based on data mining [J]. Journal of central south university of technology, 2008,15(1):0026-0032
    [242]S.A. Torabi, N. Sahebjamnia, S.A. Mansouri, M. Aramon Bajestani. A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem [J]. Applied Soft Computing,2013,13(12):4750-4762
    [243]刘文颖,谢昶,文晶,王佳明,王维洲.基于小生境多目标粒子群算法的输电网检修计划优化[J].中国电机工程学报,2013,33(4):0141-0146
    [244]向长城,黄席樾,杨祖元等.小生境粒子群优化算法[J].计算机工程与应用,2007,43(15):41-43.
    [245]S. Ganguly, N.C. Sahoo, D. Das. Multi-objective particle swarm optimization based on fuzzy-Pareto-dominance for possibilistic planning of electrical distribution systems incorporating distributed generation[J].Fuzzy Sets and Systems,2013, 213(,16):47-73
    [246]顾伟,吴志,王锐.考虑污染气体排放的热电联供型微电网多目标运行优化[J].电力系统自动化,2012,36(14):177-185
    [247]刘喜梅,田惠英,秦超.基于复杂科学管理思维与MAS技术的智能电网信息管理系统研究[J].2012,36(8):0204-0208
    [248]程时杰,李兴源,张之哲.智能电网统一信息系统的电网信息全域共享和综合应用[J].中国电机工程学报,2011,31(1):0008-0014
    [249]马真.凡纳滨对虾集约化养殖水质管理决策支持系统的构建[D].中国海洋大学博士学位论文,2013年6月
    [250]胡振华,袁静.企业效益评价因子分析模型及应用[J].中国管理科学,2002,10(1):0068-0070
    [251]李博,徐宗昌,张光明,朱伟成.基于multi-agent的装备综合保障数据交互研究[J].上海理工大学学报.2012,34(5):0447-0451
    [252]胡东波.模型驱动的决策支持系统研究[D].中南大学博士学位论文.2009

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