含风电场的电力系统动态经济调度问题研究
|
摘要
随着能源需求增长与化石燃料资源日趋枯竭,风力发电作为可再生能源中最具有经济发展前景的发电方式之一越来越受到人们的青睐和重视。与传统发电机组不同,风电输出功率具有间歇性和随机性的特点,大规模风电并网势必给电力系统的经济调度和安全运行带来新的挑战和要求。因此,研究含有大规模并网风电场的动态经济调度是一项十分紧迫的课题,本文针对这一课题开展了相关的调度模型和方法研究。
首先,为方便后续调度模型的求解,进行了非线性规划相关理论与方法的研究工作。分别就基于随机搜索技术的粒子群算法和基于梯度方向的原对偶内点算法的机理进行了详细分析。通过引入最大偏离函数、可行化调整策略以及变异调整策略,提高了粒子群算法进入可行域的速度,避免了算法出现早熟现象。将改进粒子群算法的全局搜索特性和预估—校正原对偶内点算法的局部寻优特性相结合,让两种算法优势互补,提出一种混合优化策略。研究表明该混合策略可用于解决具有多约束、非凸、多峰特性的复杂优化问题,并能获得更高质量的解。
然后,深入分析含风电场电力系统动态经济调度的影响因素,在前人相关研究成果的基础上,考虑了风电功率的波动性和难以预测性对动态经济调度旋转备用的新要求,提出一种计及正、负旋转备用容量约束的含风电场电力系统动态经济调度新模型。此外,在发电成本函数中还计及了阀点效应的影响,从而使得优化模型具有非线性,多峰值和不可微的特点。在对该模型利用凝聚函数法进行光滑化处理以后,应用本文提出的粒子群内点混合优化策略求取优化问题的全局最优解。
在上述调度模型的基础上,引入风速和负荷预测误差的概率分布函数来描述系统中存在的不确定性因素,提出了一种基于风险备用约束的含风电场电力系统动态经济调度新模型,并采用惩罚的方式在目标函数中引入风电出力计划相对于实际出力过多或过少的惩罚成本(备用成本)。从而通过优化调度得到一个相对合理的风电计划出力值。利用含有一个风电场的10机组系统算例证明了该调度模型的可行性。
最后,为进一步验证本文所提模型和方法的实用性,以辽宁电网为应用案例,研究凌海风电场大规模风电并网后的动态调度问题。实际案例分析结果表明,采用本文提出的基于风险备用的含风电场动态经济调度模型计算获得的调度方案能够更加全面、合理地反映系统实际运行对风险和成本的要求。
With the increase of energy demand and depletion of global fossil oil resources, wind power generation has been paid more attention as a good alternative to thermal energy power generation. However, different from conventional generators, the large-scale integration of wind power generators has associated operational challenges due to its uncertainty and variability. As a result, it becomes more and more necessary to study the dynamic economic dispatch (DED) issues in wind power integrated system. The corresponding model and method have been investigated in this paper.
Firstly, the general overviews of optimization techniques used in the paper are presented. The optimization principles of particle swarm optimization (PSO) algorithm and nonlinear primal-dual interior point (IP) method are analyzed in detail. The PSO global searching ability is improved by applying maximum deviation function, feasible regulation scheme and mutation regulation scheme. This paper proposes a new hybrid methodology combining these two methods. The proposed method is developed in such a way that the improved PSO algorithm is applied as a based level search, which can give a good direction to the optimal global region, and a local search primal-dual IP method is used as a fine tuning to determine the optimal solution at the final. Investigation indicates this method is suitable for solving the optimaztion model with nonlinear, noncovex and multi-peak-value characteristics and a higher quality solution can be obtained.
Then the influence factors of DED issue in wind integrated power systems are analyzed. In the optimization model, the constraints of up spinning reserve and down spinning reserve are introduced to deal with the influence of wind power forecast errors on dynamic economic dispatch. The valve point effect is also considered in the objective function. That is to say, the optimization model is of non-differentiable characteristics. On the basis of smoothing technology, this paper applies the hybrid PSO and IP algorithm to calucate the global optimal solution.
Based on the above DED model, the probabilistic distributions of wind speed and load forecast errors are introduced to describe the uncertainty in power systems. So a modified DED optimization model with wind power penetration is proposed. In this model, the risk based up and down spinning reserve constraints are considered. The required up and down spinning reserve costs are also included in the objective function, which stand for the costs caused by the risk of shedding load and penalty of wasting energy respectively. Using the proposed model, a reasonable scheduled output of wind power can be obtained. Simulation results of a system with ten conventional generators and one wind farm demonstrate the effectiveness of the proposed method.
Finally, the DED issue for Liaoning power system integrating large wind farms is analyzed and discussed to validate the practicability of the proposed methods. It can be concluded from simulation results that the dispatch scheme determined by the risk reserve constrained DED model with wind power penetration shows its superiority in regard to comprehensively and reasonably reflecting the requirement of risk and cost in power systems.
首先,为方便后续调度模型的求解,进行了非线性规划相关理论与方法的研究工作。分别就基于随机搜索技术的粒子群算法和基于梯度方向的原对偶内点算法的机理进行了详细分析。通过引入最大偏离函数、可行化调整策略以及变异调整策略,提高了粒子群算法进入可行域的速度,避免了算法出现早熟现象。将改进粒子群算法的全局搜索特性和预估—校正原对偶内点算法的局部寻优特性相结合,让两种算法优势互补,提出一种混合优化策略。研究表明该混合策略可用于解决具有多约束、非凸、多峰特性的复杂优化问题,并能获得更高质量的解。
然后,深入分析含风电场电力系统动态经济调度的影响因素,在前人相关研究成果的基础上,考虑了风电功率的波动性和难以预测性对动态经济调度旋转备用的新要求,提出一种计及正、负旋转备用容量约束的含风电场电力系统动态经济调度新模型。此外,在发电成本函数中还计及了阀点效应的影响,从而使得优化模型具有非线性,多峰值和不可微的特点。在对该模型利用凝聚函数法进行光滑化处理以后,应用本文提出的粒子群内点混合优化策略求取优化问题的全局最优解。
在上述调度模型的基础上,引入风速和负荷预测误差的概率分布函数来描述系统中存在的不确定性因素,提出了一种基于风险备用约束的含风电场电力系统动态经济调度新模型,并采用惩罚的方式在目标函数中引入风电出力计划相对于实际出力过多或过少的惩罚成本(备用成本)。从而通过优化调度得到一个相对合理的风电计划出力值。利用含有一个风电场的10机组系统算例证明了该调度模型的可行性。
最后,为进一步验证本文所提模型和方法的实用性,以辽宁电网为应用案例,研究凌海风电场大规模风电并网后的动态调度问题。实际案例分析结果表明,采用本文提出的基于风险备用的含风电场动态经济调度模型计算获得的调度方案能够更加全面、合理地反映系统实际运行对风险和成本的要求。
With the increase of energy demand and depletion of global fossil oil resources, wind power generation has been paid more attention as a good alternative to thermal energy power generation. However, different from conventional generators, the large-scale integration of wind power generators has associated operational challenges due to its uncertainty and variability. As a result, it becomes more and more necessary to study the dynamic economic dispatch (DED) issues in wind power integrated system. The corresponding model and method have been investigated in this paper.
Firstly, the general overviews of optimization techniques used in the paper are presented. The optimization principles of particle swarm optimization (PSO) algorithm and nonlinear primal-dual interior point (IP) method are analyzed in detail. The PSO global searching ability is improved by applying maximum deviation function, feasible regulation scheme and mutation regulation scheme. This paper proposes a new hybrid methodology combining these two methods. The proposed method is developed in such a way that the improved PSO algorithm is applied as a based level search, which can give a good direction to the optimal global region, and a local search primal-dual IP method is used as a fine tuning to determine the optimal solution at the final. Investigation indicates this method is suitable for solving the optimaztion model with nonlinear, noncovex and multi-peak-value characteristics and a higher quality solution can be obtained.
Then the influence factors of DED issue in wind integrated power systems are analyzed. In the optimization model, the constraints of up spinning reserve and down spinning reserve are introduced to deal with the influence of wind power forecast errors on dynamic economic dispatch. The valve point effect is also considered in the objective function. That is to say, the optimization model is of non-differentiable characteristics. On the basis of smoothing technology, this paper applies the hybrid PSO and IP algorithm to calucate the global optimal solution.
Based on the above DED model, the probabilistic distributions of wind speed and load forecast errors are introduced to describe the uncertainty in power systems. So a modified DED optimization model with wind power penetration is proposed. In this model, the risk based up and down spinning reserve constraints are considered. The required up and down spinning reserve costs are also included in the objective function, which stand for the costs caused by the risk of shedding load and penalty of wasting energy respectively. Using the proposed model, a reasonable scheduled output of wind power can be obtained. Simulation results of a system with ten conventional generators and one wind farm demonstrate the effectiveness of the proposed method.
Finally, the DED issue for Liaoning power system integrating large wind farms is analyzed and discussed to validate the practicability of the proposed methods. It can be concluded from simulation results that the dispatch scheme determined by the risk reserve constrained DED model with wind power penetration shows its superiority in regard to comprehensively and reasonably reflecting the requirement of risk and cost in power systems.
引文
[1]仇卫东,王智冬,李隽.我国风电发展相关问题分析[J].电力技术经济,2008,20(1)：19-23.
[2]陈炳森,胡华丽.我国风电发展概况及展望[J].电网技术,2008,32(2)：272-275.
[3]施鹏飞.国内外风电持续迅猛发展[J].中外能源,2009,14：36-40.
[4]白建华,辛颂旭,贾德香.我国风电大规模开发面临的规划和运行问题分析[J].电力技术经济,2009,21(2)：7-11.
[5]迟永宁,李群英,李琰,王真,石文辉,王跃峰.大规模风电并网引起的电力系统运行与稳定问题及对策[J].电力设备,2008,9(11)：16-19.
[6]Potter C W, Negnevitsky M. Very short-term wind forecasting for tasmanian power generation [J]. IEEE Transactions on Power Systems,2006,21(2):965-972.
[7]杨秀媛,肖洋,陈树勇.风电场风速和发电功率预测研究[J].中国电机工程学报,2005,25(11)：1-5.
[8]肖永山,王维庆,霍晓萍.基于神经网络的风电场风速时间序列预测研究[J].节能技术,2007,25(142)：106-108,175.
[9]Alexiadis M C, Dokopoulos P S, Sahsamanoglou H S. Wind speed and power forecasting based on spatial correlation models[J]. IEEE Transactions on Energy Conversion,1999,14(3):836-842.
[10]孟祥星,王宏.大规模风电并网条件下的电力系统调度[J].东北电力大学学报,2009,29(1)：1-7.
[11]雷亚洲.与风电并网相关的研究课题[J].电力系统自动化,2003,27(8)：84-89.
[12]戴慧珠,王伟胜,迟永宁.风电场接入电力系统研究的新进展[J].电网技术,2007,31(20)：16-23.
[13]Q／GDW 432-2010风电调度运行管理规范[EB/OL]. http://www. cnwpem. com/news/6268. html.
[14]Chowdhury B H, Rahman S. A review of recent advances in economic dispatch [J]. IEEE Transactions on Power Systems,1990,5(4):1248-1259.
[15]Bechert T E, Kwatny H G. On the optimal dynamic dispatch of real power[J]. IEEE Transactions on Power Apparatus and Systems,1972, PAS-91:889-898.
[16]Travers D L, Kaye R J. Dynamic dispatch by constructive dynamic programming [J]. IEEE Transactions on Power Systems,1998,13(1):72-78.
[17]Attaviriyanupap P, Kita H, Tanaka E, et al. A hybrid EP and SQP for dynamic economic dispatch with nonsmooth incremental fuel cost function [J].IEEE Transactions on Power Systems,2002,17(2):411-416.
[18]Yuan X H, Su A, Yuan Y B, et al. An improved PSO for dynamic load dispatch of generators with valve-point effects [J]. Energy,2009,34:67-74.
[19]Yuan X H, Wang L, Zhang Y C, et al. A hybrid differential evolution method for dynamic economic dispatch with valve-point effects [J]. Expert Systems with Applications,2009,36:4042-4048.
[20]Attaviriyanupap P, Kita H, Tanaka E, et al. A fuzzy-optimization approach to dynamic economic dispatch considering uncertainties [J].IEEE Transactions on Power Systems,2004,19(3):1299-1307.
[21]Lin W M, Chen S J. Bid-based dynamic economic dispatch with an efficient interior point algorithm [J]. International Journal of Eclectrical Power & Energy Systems,2002,24(1):51-57.
[22]赵波.应用粒子群优化算法求解市场环境下的电力系统动态经济调度问题[J].继电器,2004,32(21)：1-5,66.
[23]Basu M. Particle swarm optimization based goal-attainment method for dynamic economic emission dispatch [J]. Electric Power Components and Systems,2006, 34:1015-1025.
[24]Basu M. Dynamic economic emission dispatch using evolutionary programming and fuzzy satisfied method [J]. International Journal of Emerging Electric Power Systems,2007, 8(4):1-5.
[25]Basu M. Dynamic economic emission dispatch using nondominted sorting genetic algorithm-II [J]. International Journal of Eclectrical Power & Energy Systems,2008, 30:140-149.
[26]Victoire T A, Jeyakumar A E. Reserve constrained dynamic dispatch of units with valve-point effects [J].IEEE Transactions on Power Systems,2005,20(3):1273-1282.
[27]Ross D W, Kim S. Dynamic economic dispatch of generation [J].IEEE Transactions on Power Apparatus and Systems,1980, PAS-99:2060-2068.
[28]Irisarri G, Kimball L M, Clements K A, et al. Economic dispatch with network and ramping constraints via interior point methods [J].IEEE Transactions on Power Systems,1998,13(1):236-242.
[29]Miao Z Y, Yasuda K, Yokoyama R. Fuuzy dynamic load dispatching [C]. IEE International Conference on Advances in Power System Control, Operation and Management, Hong Kong,1991.
[30]Wood W G. Spinning reserve constrained static and dynamic economic dispatch [J]. IEEE Transactions on Power Apparatus and Systems,1982, PAS-101:381-388.
[31]Joned A M, Musirin I, Rahman T K. Solving dynamic economic dispatch using evolutionary programming [C]. First International Power and Energy Conference, Putrajaya, Malaysia,2006.
[32]柳焯.电力系统动态负荷瓶颈分析[J].哈尔滨工业大学学报,1994,26(6)：69-74.
[33]韩学山,柳焯.动态优化调度积留量法的基本理论——动态负荷瓶颈序列与临界紧段落分析[J].东北电力学院学报,1995,15(2)：1-7.
[34]韩学山,柳焯.动态优化调度积留量法的基本理论——紧段落与准备段落的分析[J].东北电力学院学报,1995,15(3)：1-7.
[35]韩学山,柳焯.动态优化调度积留量法的基本理论——动态优化调度的解耦求解模型[J].电力系统自动化,1996,16(1)：1-7.
[36]Han X S, Gooi H B. Dynamic economic dispatch:Feasible and Optimal solutions [J]. IEEE Transactions on Power Systems,2001,16(1):22-28.
[37]韩学山,柳焯.考虑机组爬坡速度和网络安全约束的经济调度解耦算法[J].电力系统自动化,2002,26(13)：32-37.
[38]Granelli G P, Marannino P, Montagna M, et al. Fast and efficient gradient projection algorithm for dynamic generation dispatching [J].IEE Proceedings,1989,136(5):295-302.
[39]Hindi K S, Ghani M R. Dynamic economic dispatch for large scale power systems:a lagrangian relaxation approach [J]. International Journal of Eclectrical Power & Energy Systems,1991,13(1):51-56.
[40]Bosch P P J. Optimal dynamic dispatch owing to spinning-reserve and power-rate limits [J]. IEEE Transactions on Power Apparatus and Systems,1985, PAS-104(12):3395-3401.
[41]Kirkpatrick S, Gelatt C D, Vecchi Jr M P. Optimization by simulated annealing [J].Science,1983,200:671-680.
[42]Panigrahi C K, Chattopadhyay P K, Chakrabarti R N, et al. Simulated annealing technique for dynamic economic dispatch [J]. Electric Power Components and Systems,2006,34(5):577-586.
[43]曹一家.并行遗传算法在电力系统经济调度中的应用_迁移策略对算法性能的影响[J].电力系统自动化,2002,26(13)：20-24.
[44]Song Y H, Li F, Morgan R, et al. Comparison studies of genetic algorithms in power system economic dispatch [J]. Power System Technology,1995,19(3):27-33,38.
[45]石立宝,徐国禹.遗传算法在有功安全经济调度中的应用[J].电力系统自动化,1997,21(6)：42-44.
[46]Li F, Morgan R, Williams D. Towards more cost saving under stricter ramping rate constraints of dynamic economic dispatch problems-a genetic-based approach [C].2nd Int. Conf. Genetic Algorithms Eng. Syst.:Innovations and Applications, Glasgow, UK,1997.
[47]Ongsakul W, Tippayachai J. Parallel micro genetic algorithm based on merit order loading solutions for constrained dynamic economic dispatch [J]. Electric Power Systems Research,2002,61(2):77-88.
[48]Li F, Morgan R, Williams D. Hybrid genetic approaches to ramping rate constrained dynamic economic dispatch [J]. Electric Power Systems Research,1997,43:97-103.
[49]Hindi K S, Ab Ghani M R. Multi-period economic dispatch for large scale power systems [J]. Proc. IEE Pt. C,1989,136(3):130-136.
[50]Ongsakul W, Ruangpayoongsak N. Constrained dynamic economic dispatch by simulated annealing/genetic algorithms [C]. Proc.22nd Power Ind. Comput. Appl. (PICA), Sydney, Australia,2001.
[51]Zhang G L, Lu H Y, Li G Y, et al. A new hybrid real-coded genetic algorithm and application in dynamic economic dispatch [C].Proc.6th World Congr. Intell. Control and Autom., Dalian, China,2006.
[52]Revisiting Evolutionary Programming. Rogers S K, Fogel D B, Bezdek J C, et al. Proc. of Applications and Science of Computational Intelligence [M]. Drlando, Florida, 1998.
[53]Fogel D B, Fogel L J. An introduction to evolutionary programming [C]. European Conf. on AE 95, Berlin, Springer,1996.
[54]Joned A M, Musirin I, Abdul Rahman T K. Solving dynamic economic dispatch using evolutionary programming [C].Proc.1st IEEE Int. Power Energy Conf. Putrajaya, Malaysia,2006.
[55]Swarup K S, Natarrajan A. Constrained optimization using evolutionary programming for dynamic economic dispatch [C].Proc.3rd Int. Conf. Intell. Sensing Inform. Processing,2005.
[56]Storm R, Price K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces [J].J. Global Optim.,1997,11:341-359.
[57]Balamurugan B, Subramanian R. Differential evolution-based dynamic economic dispatch of generating units with valve-point effects [J]. Electric Power Components and Systems,2008,36(8):828-843.
[58]Balamurugan B, Subramuanian R. An improved differential evolution based dynamic economic dispatch with nonsmooth fuel cost function [J]. J. Electr. Syst.,2007,3(3): 151-161.
[59]Yuan X, Wang L, Yuan Y, et al. A modified differential evolution approach for.dynamic economic dispatch with valve-point effects [J]. Energy Conversion and Management,2008,49:3447-3453.
[60]Kennedy J, Eberhar R. Particle swarm optimization [C]. Proc. IEEE Int. Conf. Neural Networks, Piscataway,1995.
[61]黄平,张尧,曾红.改进的粒子群优化算法求解电力经济调度[J].华中科技大学学报(自然科学版),2010,38(1)：121-124.
[62]蒋秀洁,徐满清,龚学会等.计及阀点效应负荷经济分配的杂交粒子群算法[J].电力系统保护与控制,2009,37(8)：10-13.
[63]刘涌,侯志俭,蒋传文.求解经济调度问题的改进粒子群算法[J].继电器,2006,34(20)：24-27,39.
[64]Gaing Z L. Constrained dynamic economic dispatch solution using particle swarm optimization [C]. Proc. IEEE Power Engineering Society General Meeting,2004.
[65]Panigrahi C K, Chattopadhyay P K, Chakrabarti R. Load dispatch and PSO algorithm for DED control [J]. Int. J. Autom. control,2007,1(2-3):182-194.
[66]Chakrabarti R, Chattopadhyay P K, Basu M. Particle swarm optimization technique for dynamic economic dispatch [J]. IE(I) Journal-EL,2006,87:48-54.
[67]张雪雯,李艳君.基于自调节粒子群算法的电力系统经济负荷分配[J].电网技术,2006,30(18)：8-13.
[68]Sriyanong P. A hybrid particle swarm optimization solution to ramping rate constrained dynamic economic dispatch [J]. Proc. World Acad.Sci. Eng. Technol.,2008,30:991-996.
[69]Victoire T A A, Jeyakumar. Deterministically guided PSO for dynamic dispatch considering valve-point effect [J]. Electric Power Systems Research* 2005, 73(3):313-322.
[70]Titus S, Jeyakumar A E. A hybrid EP-PSO-SQP algorithm for dynamic dispatch considering prohibited operating zones [J]. Electric Power Components ans Systems, 2008,36:449-467.
[71]Li F, Aggarwal R K. Fast and accurate power dispatch using a relaxed genetic algorithm and a local gradient technique [J]. Expert Syst. Appl.,2000,19:159-165.
[72]Abdelaziz A Y, Kamh M Z, Mekhamer S F, et al. A hybrid HNN-QP approach for dynamic economic dispatch problem [J]. Electric Power Systems Research,2008, 78 (10):1784-1788.
[73]Zhang G L, Lu H Y, Li G Y, et al. Dynamic economic load dispatch using hybrid genetic algorithm and the method of fuzzy number ranking [C]. Proc.4th Int. Conf. Mach. Learning Cybernetics, Guangzhou, China,2005.
[74]Ummels B C, Gibescu M, Pelgrum E, et al. Impacts of wind power on thermal generation unit commitment and dispatch [J]. IEEE Transactions on Energy Conversion,2007,22(1): 44-51.
[75]Lee T Y. Optimal spinning reserve for a wind-thermal power system using EIPSO [J]. IEEE Transactions on Power Systems,2007,22(4):1612-1621.
[76]Chen C L. Optimal wind-thermal generating unit commitment [J]. IEEE Transactions on Energy Conversion,2008,23 (1):273-280.
[77]Chen C L, Lee T Y, Jan R M. Optimal wind-thermal coordination dispatch in isolated power systems with large integration of wind capacity [J]. Energy Conversion and Management,2006,47:3456-3472.
[78]Miranda V, Hang P S. Economic dispatch model with fuzzy wind constraints and attitudes of dispatchers [J]. IEEE Transactions on Power Systems,2005,20(4):2143-2145.
[79]Wang L F, Singh C. Tradeoff between risk and cost in economic dispatch including wind power penetration using particle swarm optimization [C]. International Conference on Power System Technology, Chongqing, China,2006.
[80]陈海焱,陈金富,段献忠.含风电场电力系统经济调度的模糊建模及优化算法[J].电力系统自动化,2006,30(2)：22-26.
[81]Hong Y Y, Li C T. Short-term real-power scheduling considering fuzzy factors in an autonomous system using genetic algorithms [J]. The Institution of Engineering and Technology,2006,153(6):684-692.
[82]Hetzer J, Yu D C. An economic dispatch model incorporating wind power [J]. IEEE Transactions on Energy Conversion,2008,23(2):603-611.
[83]Jabr R A, Pal B C. Intermittent wind generation in optimal power flow dispatching [J]. IET Gener. Transm. Distrib.,2009,3(1):66-74.
[84]Wang J H, Shahidehpour M, Li Z Y. Security-constrained unit commitment with volatile wind power generation [J]. IEEE Transactions on Power Systems,2008,23(3): 1319-1327.
[85]Bouffard F, Galiana F D. Stochastic security for operations planning with significant wind power generation [J]. IEEE Transactions on Power Systems,2008,23(2):306-316.
[86]Doherty R, Malley M 0. A new approach to quantify reserve demand in systems with significant installed wind capacity [J]. IEEE Transactions on Power Systems,2005, 20(2):587-595.
[87]Lange M. On the uncertainty of wind powr predictions-analysis of the forecast accuracy and statistical distribution of errors [J]. Solar Energy Eng.,2005,127: 177-184.
[88]Soder L. Reserve margin planning in a wind-hydro-thermal power system [J]. IEEE Transactions on Power Systems,1993,8(2):564-571.
[89]Fabbri A, Roman T G S, Abbad J R. Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market [J]. IEEE Transactions on Power Systems,2005,20:1440-1446.
[90]Matevosyan J, Soder L. Minimization of imbalance cost trading wind power on the short-term power market [J]. IEEE Transactions on Power Systems,2006,21:1396-1404.
[91]Siahkali H, Vakilian M. Stochastic unit commitment of wind farms integrated in power system [J]. Electric Power Systems Research,2010,80:1006-1017.
[92]Chen C L, Lee T Y, Jan R M. Optimal wind-thermal coordination dispatch in isolated power systems with large integration of wind capacity [J]. Energy Conversion and Management,2006,47:3456-3472.
[93]Chen C L. Simulated annealing-based optimal wind-thermal coordination scheduling [J]. The Institution of Engineering and Technology,2007,1(3):447-455.
[94]江岳文,陈冲,温步瀛.基于随机模拟粒子群算法的含风电场电力系统经济调度[J].电工电能新技术,2007,26(3)：37-41.
[95]孙元章,吴俊,李国杰等.基于风速预测和随机规划的含风电场电力系统动态经济调度[J].中国电机工程学报,2009,29(4)：41-47.
[96]Kennedy S. Wind power planning:assessing long-term costs and benefits [J]. Energy Policy,2005,1661-1675.
[97]袁铁江,晁勤,吐尔逊伊不拉音.面向电力市场的含风电电力系统的环境经济调度优化[J].电网技术,2009,33(20)：131-135.
[98]任博强,蒋传文,彭鸣鸿等.基于改进遗传算法的含风电场的电力系统短期经济调度及其风险管理[J].现代电力,2010,27(1)：76-80.
[99]刘明波,王晓村.内点法在求解电力系统优化问题中的应用综述[J].电网技术,1999,23(8)：61-68.
[100]徐进东,丁晓群,覃振成等.基于非线性预报-校正内点法的电力系统无功优化研究[J].电网技术,2005,29(9)：36-40.
[101]Wu Y C, Debs A S, Marsten R E. A direct nonlinear predictor-corrector primal-dual interior point algorithm for optimal power flows [J]. IEEE Transactions on Power Systems,1994,9(2):876-883.
[102]Li Xingsi. An efficient approach to a class of non-smooth optimization problems [J]. SCIENCE IN CHINA (Series A),1994,37(3):323-330.
[103]Zhao M, Chen Z, Blaabjerg F. Probabilistic capacity of a grid connected wind farm based on optimization method [J]. Renewable Energy,2006,31:2171-2187.
[2]陈炳森,胡华丽.我国风电发展概况及展望[J].电网技术,2008,32(2)：272-275.
[3]施鹏飞.国内外风电持续迅猛发展[J].中外能源,2009,14：36-40.
[4]白建华,辛颂旭,贾德香.我国风电大规模开发面临的规划和运行问题分析[J].电力技术经济,2009,21(2)：7-11.
[5]迟永宁,李群英,李琰,王真,石文辉,王跃峰.大规模风电并网引起的电力系统运行与稳定问题及对策[J].电力设备,2008,9(11)：16-19.
[6]Potter C W, Negnevitsky M. Very short-term wind forecasting for tasmanian power generation [J]. IEEE Transactions on Power Systems,2006,21(2):965-972.
[7]杨秀媛,肖洋,陈树勇.风电场风速和发电功率预测研究[J].中国电机工程学报,2005,25(11)：1-5.
[8]肖永山,王维庆,霍晓萍.基于神经网络的风电场风速时间序列预测研究[J].节能技术,2007,25(142)：106-108,175.
[9]Alexiadis M C, Dokopoulos P S, Sahsamanoglou H S. Wind speed and power forecasting based on spatial correlation models[J]. IEEE Transactions on Energy Conversion,1999,14(3):836-842.
[10]孟祥星,王宏.大规模风电并网条件下的电力系统调度[J].东北电力大学学报,2009,29(1)：1-7.
[11]雷亚洲.与风电并网相关的研究课题[J].电力系统自动化,2003,27(8)：84-89.
[12]戴慧珠,王伟胜,迟永宁.风电场接入电力系统研究的新进展[J].电网技术,2007,31(20)：16-23.
[13]Q／GDW 432-2010风电调度运行管理规范[EB/OL]. http://www. cnwpem. com/news/6268. html.
[14]Chowdhury B H, Rahman S. A review of recent advances in economic dispatch [J]. IEEE Transactions on Power Systems,1990,5(4):1248-1259.
[15]Bechert T E, Kwatny H G. On the optimal dynamic dispatch of real power[J]. IEEE Transactions on Power Apparatus and Systems,1972, PAS-91:889-898.
[16]Travers D L, Kaye R J. Dynamic dispatch by constructive dynamic programming [J]. IEEE Transactions on Power Systems,1998,13(1):72-78.
[17]Attaviriyanupap P, Kita H, Tanaka E, et al. A hybrid EP and SQP for dynamic economic dispatch with nonsmooth incremental fuel cost function [J].IEEE Transactions on Power Systems,2002,17(2):411-416.
[18]Yuan X H, Su A, Yuan Y B, et al. An improved PSO for dynamic load dispatch of generators with valve-point effects [J]. Energy,2009,34:67-74.
[19]Yuan X H, Wang L, Zhang Y C, et al. A hybrid differential evolution method for dynamic economic dispatch with valve-point effects [J]. Expert Systems with Applications,2009,36:4042-4048.
[20]Attaviriyanupap P, Kita H, Tanaka E, et al. A fuzzy-optimization approach to dynamic economic dispatch considering uncertainties [J].IEEE Transactions on Power Systems,2004,19(3):1299-1307.
[21]Lin W M, Chen S J. Bid-based dynamic economic dispatch with an efficient interior point algorithm [J]. International Journal of Eclectrical Power & Energy Systems,2002,24(1):51-57.
[22]赵波.应用粒子群优化算法求解市场环境下的电力系统动态经济调度问题[J].继电器,2004,32(21)：1-5,66.
[23]Basu M. Particle swarm optimization based goal-attainment method for dynamic economic emission dispatch [J]. Electric Power Components and Systems,2006, 34:1015-1025.
[24]Basu M. Dynamic economic emission dispatch using evolutionary programming and fuzzy satisfied method [J]. International Journal of Emerging Electric Power Systems,2007, 8(4):1-5.
[25]Basu M. Dynamic economic emission dispatch using nondominted sorting genetic algorithm-II [J]. International Journal of Eclectrical Power & Energy Systems,2008, 30:140-149.
[26]Victoire T A, Jeyakumar A E. Reserve constrained dynamic dispatch of units with valve-point effects [J].IEEE Transactions on Power Systems,2005,20(3):1273-1282.
[27]Ross D W, Kim S. Dynamic economic dispatch of generation [J].IEEE Transactions on Power Apparatus and Systems,1980, PAS-99:2060-2068.
[28]Irisarri G, Kimball L M, Clements K A, et al. Economic dispatch with network and ramping constraints via interior point methods [J].IEEE Transactions on Power Systems,1998,13(1):236-242.
[29]Miao Z Y, Yasuda K, Yokoyama R. Fuuzy dynamic load dispatching [C]. IEE International Conference on Advances in Power System Control, Operation and Management, Hong Kong,1991.
[30]Wood W G. Spinning reserve constrained static and dynamic economic dispatch [J]. IEEE Transactions on Power Apparatus and Systems,1982, PAS-101:381-388.
[31]Joned A M, Musirin I, Rahman T K. Solving dynamic economic dispatch using evolutionary programming [C]. First International Power and Energy Conference, Putrajaya, Malaysia,2006.
[32]柳焯.电力系统动态负荷瓶颈分析[J].哈尔滨工业大学学报,1994,26(6)：69-74.
[33]韩学山,柳焯.动态优化调度积留量法的基本理论——动态负荷瓶颈序列与临界紧段落分析[J].东北电力学院学报,1995,15(2)：1-7.
[34]韩学山,柳焯.动态优化调度积留量法的基本理论——紧段落与准备段落的分析[J].东北电力学院学报,1995,15(3)：1-7.
[35]韩学山,柳焯.动态优化调度积留量法的基本理论——动态优化调度的解耦求解模型[J].电力系统自动化,1996,16(1)：1-7.
[36]Han X S, Gooi H B. Dynamic economic dispatch:Feasible and Optimal solutions [J]. IEEE Transactions on Power Systems,2001,16(1):22-28.
[37]韩学山,柳焯.考虑机组爬坡速度和网络安全约束的经济调度解耦算法[J].电力系统自动化,2002,26(13)：32-37.
[38]Granelli G P, Marannino P, Montagna M, et al. Fast and efficient gradient projection algorithm for dynamic generation dispatching [J].IEE Proceedings,1989,136(5):295-302.
[39]Hindi K S, Ghani M R. Dynamic economic dispatch for large scale power systems:a lagrangian relaxation approach [J]. International Journal of Eclectrical Power & Energy Systems,1991,13(1):51-56.
[40]Bosch P P J. Optimal dynamic dispatch owing to spinning-reserve and power-rate limits [J]. IEEE Transactions on Power Apparatus and Systems,1985, PAS-104(12):3395-3401.
[41]Kirkpatrick S, Gelatt C D, Vecchi Jr M P. Optimization by simulated annealing [J].Science,1983,200:671-680.
[42]Panigrahi C K, Chattopadhyay P K, Chakrabarti R N, et al. Simulated annealing technique for dynamic economic dispatch [J]. Electric Power Components and Systems,2006,34(5):577-586.
[43]曹一家.并行遗传算法在电力系统经济调度中的应用_迁移策略对算法性能的影响[J].电力系统自动化,2002,26(13)：20-24.
[44]Song Y H, Li F, Morgan R, et al. Comparison studies of genetic algorithms in power system economic dispatch [J]. Power System Technology,1995,19(3):27-33,38.
[45]石立宝,徐国禹.遗传算法在有功安全经济调度中的应用[J].电力系统自动化,1997,21(6)：42-44.
[46]Li F, Morgan R, Williams D. Towards more cost saving under stricter ramping rate constraints of dynamic economic dispatch problems-a genetic-based approach [C].2nd Int. Conf. Genetic Algorithms Eng. Syst.:Innovations and Applications, Glasgow, UK,1997.
[47]Ongsakul W, Tippayachai J. Parallel micro genetic algorithm based on merit order loading solutions for constrained dynamic economic dispatch [J]. Electric Power Systems Research,2002,61(2):77-88.
[48]Li F, Morgan R, Williams D. Hybrid genetic approaches to ramping rate constrained dynamic economic dispatch [J]. Electric Power Systems Research,1997,43:97-103.
[49]Hindi K S, Ab Ghani M R. Multi-period economic dispatch for large scale power systems [J]. Proc. IEE Pt. C,1989,136(3):130-136.
[50]Ongsakul W, Ruangpayoongsak N. Constrained dynamic economic dispatch by simulated annealing/genetic algorithms [C]. Proc.22nd Power Ind. Comput. Appl. (PICA), Sydney, Australia,2001.
[51]Zhang G L, Lu H Y, Li G Y, et al. A new hybrid real-coded genetic algorithm and application in dynamic economic dispatch [C].Proc.6th World Congr. Intell. Control and Autom., Dalian, China,2006.
[52]Revisiting Evolutionary Programming. Rogers S K, Fogel D B, Bezdek J C, et al. Proc. of Applications and Science of Computational Intelligence [M]. Drlando, Florida, 1998.
[53]Fogel D B, Fogel L J. An introduction to evolutionary programming [C]. European Conf. on AE 95, Berlin, Springer,1996.
[54]Joned A M, Musirin I, Abdul Rahman T K. Solving dynamic economic dispatch using evolutionary programming [C].Proc.1st IEEE Int. Power Energy Conf. Putrajaya, Malaysia,2006.
[55]Swarup K S, Natarrajan A. Constrained optimization using evolutionary programming for dynamic economic dispatch [C].Proc.3rd Int. Conf. Intell. Sensing Inform. Processing,2005.
[56]Storm R, Price K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces [J].J. Global Optim.,1997,11:341-359.
[57]Balamurugan B, Subramanian R. Differential evolution-based dynamic economic dispatch of generating units with valve-point effects [J]. Electric Power Components and Systems,2008,36(8):828-843.
[58]Balamurugan B, Subramuanian R. An improved differential evolution based dynamic economic dispatch with nonsmooth fuel cost function [J]. J. Electr. Syst.,2007,3(3): 151-161.
[59]Yuan X, Wang L, Yuan Y, et al. A modified differential evolution approach for.dynamic economic dispatch with valve-point effects [J]. Energy Conversion and Management,2008,49:3447-3453.
[60]Kennedy J, Eberhar R. Particle swarm optimization [C]. Proc. IEEE Int. Conf. Neural Networks, Piscataway,1995.
[61]黄平,张尧,曾红.改进的粒子群优化算法求解电力经济调度[J].华中科技大学学报(自然科学版),2010,38(1)：121-124.
[62]蒋秀洁,徐满清,龚学会等.计及阀点效应负荷经济分配的杂交粒子群算法[J].电力系统保护与控制,2009,37(8)：10-13.
[63]刘涌,侯志俭,蒋传文.求解经济调度问题的改进粒子群算法[J].继电器,2006,34(20)：24-27,39.
[64]Gaing Z L. Constrained dynamic economic dispatch solution using particle swarm optimization [C]. Proc. IEEE Power Engineering Society General Meeting,2004.
[65]Panigrahi C K, Chattopadhyay P K, Chakrabarti R. Load dispatch and PSO algorithm for DED control [J]. Int. J. Autom. control,2007,1(2-3):182-194.
[66]Chakrabarti R, Chattopadhyay P K, Basu M. Particle swarm optimization technique for dynamic economic dispatch [J]. IE(I) Journal-EL,2006,87:48-54.
[67]张雪雯,李艳君.基于自调节粒子群算法的电力系统经济负荷分配[J].电网技术,2006,30(18)：8-13.
[68]Sriyanong P. A hybrid particle swarm optimization solution to ramping rate constrained dynamic economic dispatch [J]. Proc. World Acad.Sci. Eng. Technol.,2008,30:991-996.
[69]Victoire T A A, Jeyakumar. Deterministically guided PSO for dynamic dispatch considering valve-point effect [J]. Electric Power Systems Research* 2005, 73(3):313-322.
[70]Titus S, Jeyakumar A E. A hybrid EP-PSO-SQP algorithm for dynamic dispatch considering prohibited operating zones [J]. Electric Power Components ans Systems, 2008,36:449-467.
[71]Li F, Aggarwal R K. Fast and accurate power dispatch using a relaxed genetic algorithm and a local gradient technique [J]. Expert Syst. Appl.,2000,19:159-165.
[72]Abdelaziz A Y, Kamh M Z, Mekhamer S F, et al. A hybrid HNN-QP approach for dynamic economic dispatch problem [J]. Electric Power Systems Research,2008, 78 (10):1784-1788.
[73]Zhang G L, Lu H Y, Li G Y, et al. Dynamic economic load dispatch using hybrid genetic algorithm and the method of fuzzy number ranking [C]. Proc.4th Int. Conf. Mach. Learning Cybernetics, Guangzhou, China,2005.
[74]Ummels B C, Gibescu M, Pelgrum E, et al. Impacts of wind power on thermal generation unit commitment and dispatch [J]. IEEE Transactions on Energy Conversion,2007,22(1): 44-51.
[75]Lee T Y. Optimal spinning reserve for a wind-thermal power system using EIPSO [J]. IEEE Transactions on Power Systems,2007,22(4):1612-1621.
[76]Chen C L. Optimal wind-thermal generating unit commitment [J]. IEEE Transactions on Energy Conversion,2008,23 (1):273-280.
[77]Chen C L, Lee T Y, Jan R M. Optimal wind-thermal coordination dispatch in isolated power systems with large integration of wind capacity [J]. Energy Conversion and Management,2006,47:3456-3472.
[78]Miranda V, Hang P S. Economic dispatch model with fuzzy wind constraints and attitudes of dispatchers [J]. IEEE Transactions on Power Systems,2005,20(4):2143-2145.
[79]Wang L F, Singh C. Tradeoff between risk and cost in economic dispatch including wind power penetration using particle swarm optimization [C]. International Conference on Power System Technology, Chongqing, China,2006.
[80]陈海焱,陈金富,段献忠.含风电场电力系统经济调度的模糊建模及优化算法[J].电力系统自动化,2006,30(2)：22-26.
[81]Hong Y Y, Li C T. Short-term real-power scheduling considering fuzzy factors in an autonomous system using genetic algorithms [J]. The Institution of Engineering and Technology,2006,153(6):684-692.
[82]Hetzer J, Yu D C. An economic dispatch model incorporating wind power [J]. IEEE Transactions on Energy Conversion,2008,23(2):603-611.
[83]Jabr R A, Pal B C. Intermittent wind generation in optimal power flow dispatching [J]. IET Gener. Transm. Distrib.,2009,3(1):66-74.
[84]Wang J H, Shahidehpour M, Li Z Y. Security-constrained unit commitment with volatile wind power generation [J]. IEEE Transactions on Power Systems,2008,23(3): 1319-1327.
[85]Bouffard F, Galiana F D. Stochastic security for operations planning with significant wind power generation [J]. IEEE Transactions on Power Systems,2008,23(2):306-316.
[86]Doherty R, Malley M 0. A new approach to quantify reserve demand in systems with significant installed wind capacity [J]. IEEE Transactions on Power Systems,2005, 20(2):587-595.
[87]Lange M. On the uncertainty of wind powr predictions-analysis of the forecast accuracy and statistical distribution of errors [J]. Solar Energy Eng.,2005,127: 177-184.
[88]Soder L. Reserve margin planning in a wind-hydro-thermal power system [J]. IEEE Transactions on Power Systems,1993,8(2):564-571.
[89]Fabbri A, Roman T G S, Abbad J R. Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market [J]. IEEE Transactions on Power Systems,2005,20:1440-1446.
[90]Matevosyan J, Soder L. Minimization of imbalance cost trading wind power on the short-term power market [J]. IEEE Transactions on Power Systems,2006,21:1396-1404.
[91]Siahkali H, Vakilian M. Stochastic unit commitment of wind farms integrated in power system [J]. Electric Power Systems Research,2010,80:1006-1017.
[92]Chen C L, Lee T Y, Jan R M. Optimal wind-thermal coordination dispatch in isolated power systems with large integration of wind capacity [J]. Energy Conversion and Management,2006,47:3456-3472.
[93]Chen C L. Simulated annealing-based optimal wind-thermal coordination scheduling [J]. The Institution of Engineering and Technology,2007,1(3):447-455.
[94]江岳文,陈冲,温步瀛.基于随机模拟粒子群算法的含风电场电力系统经济调度[J].电工电能新技术,2007,26(3)：37-41.
[95]孙元章,吴俊,李国杰等.基于风速预测和随机规划的含风电场电力系统动态经济调度[J].中国电机工程学报,2009,29(4)：41-47.
[96]Kennedy S. Wind power planning:assessing long-term costs and benefits [J]. Energy Policy,2005,1661-1675.
[97]袁铁江,晁勤,吐尔逊伊不拉音.面向电力市场的含风电电力系统的环境经济调度优化[J].电网技术,2009,33(20)：131-135.
[98]任博强,蒋传文,彭鸣鸿等.基于改进遗传算法的含风电场的电力系统短期经济调度及其风险管理[J].现代电力,2010,27(1)：76-80.
[99]刘明波,王晓村.内点法在求解电力系统优化问题中的应用综述[J].电网技术,1999,23(8)：61-68.
[100]徐进东,丁晓群,覃振成等.基于非线性预报-校正内点法的电力系统无功优化研究[J].电网技术,2005,29(9)：36-40.
[101]Wu Y C, Debs A S, Marsten R E. A direct nonlinear predictor-corrector primal-dual interior point algorithm for optimal power flows [J]. IEEE Transactions on Power Systems,1994,9(2):876-883.
[102]Li Xingsi. An efficient approach to a class of non-smooth optimization problems [J]. SCIENCE IN CHINA (Series A),1994,37(3):323-330.
[103]Zhao M, Chen Z, Blaabjerg F. Probabilistic capacity of a grid connected wind farm based on optimization method [J]. Renewable Energy,2006,31:2171-2187.