摘要
分布式发电（Distributed Generation, DG）与集中式发电相结合是电力系统发展的趋势，更是智能电网的重要组成部分。目前，DG设备研发、制造和设备自身控制方面具有一些较成熟的技术，DG对电力系统的影响已经有大量的研究，但涉及DG并网及并网后的系统优化运行、协调控制等领域的研究大多处于初始阶段。因此，为提高DG并网后配电网运行的安全性、经济性和可靠性，提高配电网对分布式可再生能源的吸纳能力，研究分布式电源在配电网中的优化配置及其并网后的配电网优化运行管理具有重要的理论意义和实际意义。本文以分布式电源的优化配置和含分布式电源的配电网无功优化为对象，围绕含分布式电源的配电网多目标优化问题展开研究，同时，探讨基于群集智能和Pareto支配关系的多目标粒子群优化算法，为求解电力系统多目标优化问题构造更具实用价值的优化算法。主要研究内容包括：
（1）围绕智能配电网多目标优化问题，研究基于群集智能理论、能解决多任务、多约束、多目标协同优化问题的智能优化策略，提出一种能够求解复杂多目标优化问题的综合自适应多目标粒子群优化（CAMPSO）算法。CAMPSO算法引入随机黑洞机制和动态惯性权重策略以兼顾粒子群的开拓与探索能力，使算法以较高的精度逼近真实的Pareto前沿；引入基于细菌群体感应机理的扰动机制和动态选择领导粒子策略以保证种群的多样性；采用逐步淘汰策略提高Pareto解的多样性和分布均匀性。
（2）针对DG在配电网规划中的优化配置问题，建立带偏好策略的DG多目标优化配置模型，采用CAMPSO算法求解，实现DG容量和位置的全局优化配置，为决策者提供多样化的方案提供支撑。模型兼顾配电网运行的经济性、可靠性、安全性和DG的环境友好性，同时考虑用户对电压质量和供电可靠性的特殊要求，提出电压偏好策略和供电可靠性策略。
（3）为了以最少的储能设备投资取得最大的风电输出稳定性，构建以混合储能系统（HESS）安装和运行维护成本最低、风电输出功率合格率最高为目标函数的HESS多目标优化配置模型，采用模糊控制对HESS中的各储能设备进行功率分配，以保证储能设备的循环使用寿命和保障HESS有充足的可用能量平抑风电输出的波动性。
（4）将能够提供无功功率的DG与传统的无功调节手段相结合，兼顾系统的经济运行、电能质量和无功补偿设备投资与运行成本等多任务要求，研究含DG的配电网多目标无功优化策略，建立含DG的配电网多目标无功优化数学模型，运用CAMPSO算法求解配电网多目标无功优化问题，为决策人员提供灵活选择的多样化解决方案。
Distrubuted generation (DG) is an important portion of smart grid and it’s a trendto combine centralized generation with DG. It’s the fact that DG’s technologies,manufacture and control methods have developed well and the effects to distributionsystem caused by DG have been analyzed deeply. Howerver, DG’s integration todistribution system and the operation and coordinated control of distribution systempenetrated with DG are in development. Therefore, in order to take advantages of DGand decrease its negative effectes caused to power system, studying optimal allocationof DG in distribution system and the optimal operation of distribution system penetratedwith DG is of great theoretical guidance and pratical importance.
Focusing on the optimal allocation of DG in distribution system, optimal allocationof hybrid energy storage system (HESS) and reactive power optimization in distributionsystem, the thesis attempts to study the multi-objective optimization problems related todistribution system penetrated with DG. Meanwhile, multi-objective particle swarmoptimization algorithm is studied to provide some effective optimization method formulti-objective optimization problems related to power system.
Main points include:
(1) Considering the fact that most multi-objective optimization algorithms havelow convergence accuracy and the identified Pareto solutions do not have good diversityand even distribution, a comprenhensively adaptive multi-objective particle swarmoptimization (CAMPSO) algorithm is presented, which can effectively deal withcomplex multi-objective optimization problems. The CAMPSO introduces mechanismof random black hole and dynamic inertia weight to balance the swarm’s capacity ofexploration and exploitation and to convergen to the true Pareto Front with highaccuracy and high speed. Besides, it combines the turbulence mechamism based onquorum sensing and dynamic selection of leader particles to maintain the diversity ofpartice swarm. Moreover, step-by-step elimination is proposed to the diversity anddistribution property of idendtified Pareto solutions.
(2) To deal with the optimal placement and capacity of DG integrated intodistribution system, for allocating DG in distribution system, a multi-objectiveoptimization model with preference strategy is estabilished. The CAMPSO is employedto solve the multi-objective problem, which realizes the true multi-objectiveoptimization of allocating DG and provides diverse solutions to decision maker. In the model, the economy, reliability, security of the sytem and environmental advantages ofDG are fully considered, and the multiple objectives include minizing active power lossof the system, maximizing the stability of the system and miniming the environmentalcost. In addition, voltage preference strategy (VPS) and power supply preferencestrategy (PSPS) are presented to meet some special consumers’ requirements.
(3) To compensate the fluctuation of wind power better with less investment andmaintance&operation cost of HESS, a multi-objective optimization model withobjectives of minimizing the investment and maintance&operation cost of HESS andmaximizing the probability of satisfying compensated wind power output is developed.Fuzzy controd method is adopted to to allocating power between supercapacitor andbattery, and extend the life of energy storage equipment and to gurantee that HESS hasenough available energy to conpensate for next period.
(4) By integrating the reactive power of the distributed generators (DGs) to be thecontrol variables, and concurrently considering the economic operation of distributionsystem, power quality and investment of reactive power equipment, the multi-objectiveoptimization strategy of reactive power in distribution power system penetrated withDGs is discussed. A multi-objective model for optimizing reactive power is establishedand the CAMPSO algorithm is applied the multi-objective reactive power optimizationof distribution system penetrated with DGs. The proposed strategy provides decisionmaker with diverse solutions and facilitates the decision maker to analyze the relationship between the objectives and variables.
引文
[1] A. Soroudi, M. Ehsan. A distribution network expansion planning model consideringdistributed generation options and techo-economical issues [J], Energy,2010,35(8):3364-3374.
[2]王成山,陈恺,谢莹华,郑海峰.配电网扩展规划中分布式电源的选址和定容[J].电力系统自动化,2006,30(3):38-43.
[3]徐玉琴,李雪冬,张继刚,李鹏.考虑分布式发电的配电网规划问题的研究[J].电力系统保护与控制,2011,39(1):87-117.
[4]朱勇,杨京燕,张冬清.基于有功网损最优的分布式电源规划[J].电力系统保护与控制,2011,39(21):12-16.
[5]戴朝华.计及DG时基于群体智能与复杂系统理论的配网多目标综合运行优化问题研究[J].学术动态,2010,(02):3-10.
[6]常光旗.智能配电网的运行与管理[J].湖南水利水电,2011,(5):73-74.
[7]余贻鑫.智能电网的技术组成和实现顺序[J].南方电网技术,2009,3(2):1-6.
[8]王成山,王守相.分布式发电供能系统若干问题研究[J].电力系统自动化,2008,32(20):1-5.
[9] W. Caisheng, M.H. Nehrir. Analytical approaches for optimal placement of distributedgeneration sources in power systems [J]. IEEE Transactions on Power Systems,2004,19(4):2068-2076.
[10] M.F. Akorede, H. Hizam, I. Aris, M.Z.A. Ab Kadir. Effective method for optimal allocation ofdistributed generation units in meshed electric power systems [J]. IET Generation,Transmission&Distribution,2011,5(2):276-287.
[11] Y.M. Atwa, E.F. El-Saadany, M.M.A. Salama, R. Seethapathy. Optimal Renewable ResourcesMix for Distribution System Energy Loss Minimization[J]. IEEE Trans. Power Syst.,2010,25(1):360-370.
[12]刘波,张焰,杨娜.改进的粒子群优化算法在分布式电源选址和定容中的应用[J].电工技术学报,2008,23(2):103-108.
[13]张立梅,唐巍,赵云军,王少林.分布式发电接入配电网后对系统电压及损耗的影响分析[J].电力系统保护与控制，2011,39(5):91-101.
[14] M.H. Moradi, M. Abedini. A combination of genetic algorithm and particle swarmoptimization for optimal DG location and sizing in distribution systems [J]. Int. J. Electric.Power Energy Syst.,2012,34(1):66-74.
[15]郑漳华，艾芊，顾承红，蒋传文.考虑环境因素的分布式发电多目标优化配置[J].中国电机工程学报,2009,29(13):23-28.
[16] T. Griffin, K. Tomsovic, D. Secrest, A. Law. Placement of dispersed generation systems forreduced losses[C]. in Proc.33rd Hawaii Int. Conf. Syst. Sci.,2000:104-112.
[17]赵晶晶,符杨,李东东.考虑双馈电机风电场无功调节能力的配电网无功优化[J].电力系统自动化,2011,35(11):33-38.
[18]王守相,王慧,蔡声霞.分布式发电优化配置研究综述[J].电力系统自动化,2009,(18):110-115.
[19]韦钢,吴伟力,胡丹云,李智华.分布式电源及其并网时对电网的影响[J].高电压技术,2007,33(1):36-40.
[20] GB／T15945-2008电能质量电力系统频率偏差[S],2008.
[21]张步涵,曾杰,毛承雄等.电池储能系统在改善并网风电场电能质量和稳定性中的应用[J].电网技术,2006,30(15):54-58.
[22]梁亮,李建林,惠东等.大型风电场用储能装置容量的优化配置[J].高电压技术,2011,37(4):930-936.
[23]石嘉川,刘玉田.计及分布式发电的配电网多目标电压优化控制[J].电力系统自动化,2007,31(13):47-51.
[24]史常凯,徐斌,孟晓丽,宋晓辉,孙军平.智能配电网条件下的网络重构与供电恢复探讨[J].电力建设,2012,33(01):12-16.
[25]黄安平,蒋金良.考虑分布式发电的无功电压优化控制研究[J].华东电力,2010,38(8):1231-1236.
[26]王瑞,林飞,游小杰,郑琼林.基于遗传算法的分布式发电系统无功优化控制策略研究[J].电力系统保护与控制,2009,37(2):24-27+52.
[27]石嘉川,刘玉田.计及分布式发电的配电网多目标电压优化控制[J].电力系统自动化,2007,31(13):47-51.
[28]王兆宇.微电网及智能配电网的能量管理与故障恢复[D].上海交通大学硕士学位论文,2012.
[29]王新刚，艾芊，徐伟华，韩鹏.含分布式发电的微电网能量管理多目标优化[J].电力系统保护与控制，2009,37(20):79-83.
[30]陈海焱,陈金富,杨雄平等.配电网中计及短路电流约束的分布式发电规划[J].电力系统自动化,2006,30(21):16-21.
[31] S.H. Lee, J.W. Park. Selection of Optimal Location and Size of Multiple DistributedGenerations by Using Kalman Filter Algorithm [J]. IEEE Trans. Power Syst.,2009,24(3).
[32] F.S. Abu-Mouti, M.E. El-Hawary. A priority-ordered constrained search technique for optimaldistributed generation allocation in radial distribution feeder systems[C]. in201023rdCanadian Conf. Electrical and Computer Engineering (CCECE),2010:1-7.
[33]张勇,吴淳.分布式发电机在配电网中的优化配置[J].电力系统保护与控制,2010,38(11):33-37+43.
[34]张节潭,程浩忠,姚良忠,王淳.分布式风电源选址定容规划研究[J].中国电机工程学报,2009,29(16):1-7.
[35] W. El-Khattam, Y.G. Hegazy, M.M.A. Salama. An integrated distributed generationoptimization model for distribution system planning [J]. IEEE Transactions on Power Systems,2005,20(2):1158-1165.
[36] R. William, N. Ed. Optimal placement of distributed generation[C]. presented at the14thpower system computation conference, Sevilla, Spain,2002.
[37] N. Khalesi, N. Rezaei, M.R. Haghifam. DG allocation with application of dynamicprogramming for loss reduction and reliability improvement [J]. Int. J. Electric. Power EnergySyst.,2011,33(2),288-295.
[38] H.M. Ayres, W. Freitas, M.C. De Almeida, L.C.P. Da Silva. Method for determining themaximum allowable penetration level of distributed generation without steady-state voltageviolations [J]. IET Generation, Transmission&Distribution,2010,4(4):495-508.
[39] A. Keane, M. O’Malley. Optimal Allocation of Embedded Generation on DistributionNetworks [J]. IEEE Trans. Power Syst.,2005,20(3):1640-1646.
[40] B. Phan Thi Thanh, Q. Nguyen Huu, D. Phan Quoc, K. Le Dinh. Multi objective placement ofdistributed generation [C]. in20104th Int. Power Engineering and Optimization Conf.,2010:484-489.
[41]邱晓燕,夏莉丽,李兴源.智能电网建设中分布式电源的规划[J].电网技术,2010,34(4):7-10.
[42] D. Singh, K.S. Verma. Multiobjective Optimization for DG Planning With Load Models [J].IEEE Trans. Power Syst.,2009,24(1):427-436.
[43] A.A. Abou El-Ela, S.M. Allam, M.M. Shatla. Maximal optimal benefits of distributedgeneration using genetic algorithms[J]. Electr. Power Syst. Res.,2010,80(7):869-877.
[44]叶萌,刘文霞,张鑫.考虑电压质量的分布式电源选址定容[J].现代电力,2010,27(4):30-34.
[45] A.I. Aly, Y.G. Hegazy, M.A. Alsharkawy. A simulated annealing algorithm for multi-objectivedistributed generation planning[C]. in IEEE Power and Energy Society General Meeting,2010:1-7.
[46]崔弘,郭熠昀,夏成军.考虑环境效益的分布式电源优化配置研究[J].华东电力,2010,38(12):1968-1971.
[47] R. Jahani, H.C. Nejad, A.H. Araskalaei, M. Hajinasiri. Optimal Distributed GenerationLocation in Radial Distribution Systems Using A New Heuristic Method[J]. Aust. J. BasicAppl. Sci.,2011,5(7):612-621.
[48] A. Barin, L.F. Pozzatti, L.N. Canha, R.Q. Machado, A.R. Abaide, G. Arend. Multi-objectiveanalysis of impacts of distributed generation placement on the operational characteristics ofnetworks for distribution system planning [J]. Int. J. Electric. Power Energy Syst.,2010,32(10):1157-1164.
[49] T. Kaya, C. Kahraman. Multicriteria renewable energy planning using an integrated fuzzyVIKOR&AHP methodology: The case of Istanbul [J]. Energy,2010,35(6):2517-2527.
[50] L.F. Ochoa, A. Padilha-Feltrin, G.P. Harrison. Evaluating distributed generation impacts witha multiobjective index [J]. IEEE Trans. Power Delivery,2006,21(3):1452-1458.
[51] A. Kornelakis. Multiobjective Particle Swarm Optimization for the optimal design ofphotovoltaic grid-connected systems [J]. Sol. Energy,2010,84(12):2022-2033.
[52] P. Phonrattanasak,"Optimal placement of DG using multiobjective particle swarmoptimization," in20102nd International Conference on Mechanical and ElectricalTechnology (ICMET),2010:342-346.
[53] W. Lingfeng, C. Singh. Multicriteria Design of Hybrid Power Generation Systems Based on aModified Particle Swarm Optimization Algorithm [J]. IEEE Transactions on EnergyConversion,2009,24(1):163-172.
[54] A. Zangeneh, S. Jadid, A. Rahimi-Kian. A fuzzy environmental-technical-economic modelfor distributed generation planning [J]. Energy,2011,36(5):3437-3445.
[55] Y.A. Katsigiannis, P.S. Georgilakis, E.S. Karapidakis. Multiobjective genetic algorithmsolution to the optimum economic and environmental performance problem of smallautonomous hybrid power systems with renewables [J]. IET Renewable Power Generation,2010,4(5):404-419.
[56]谢石骁,杨莉,李丽娜.基于机会约束规划的混合储能优化配置方法[J].电网技术,2012,36(5):79-84.
[57] T. Ise, M. Kita, A. Taguchi. A hybrid energy storage with a SMES and secondary battery [J].IEEE Transactions on Applied Superconductivity,2005,15(2):1915-1918.
[58] R.A. Dougal, S. Liu, R.E. White. Power and life extension of battery-ultracapacitor hybrids[J]. IEEE Transactions on Components and Packaging Technologies,2002,25(1):120-131.
[59]张国驹,唐西胜,齐智平.超级电容器与蓄电池混合储能系统在微网中的应用[J].电力系统自动化,2010,34(12):85-89.
[60]丁明,林根德,陈自年,罗亚桥,赵波.一种适用于混合储能系统的控制策略[J].中国电机工程学报,2012,32(07):1-6+184.
[61]吴红斌,陈斌,郭彩云.风光互补发电系统中混合储能单元的容量优化[J].农业工程学报,2011,27(04):241-245.
[62] C. Abbey, K. Strunz, G. Joos. A Knowledge-Based Approach for Control of Two-LevelEnergy Storage for Wind Energy Systems [J]. IEEE Transactions on Energy Conversion,2009,24(2):539-547.
[63]万乐.风电场储能装置容量及最大充放电功率的确定[J].江西电力职业技术学院学报,2012,25(02):17-20.
[64]王辉.含分布式电源的配电网络重构研究[D].湖南大学硕士论文,2012.
[65] M.E. De Oliveira, L.F. Ochoa, A. Padilha-Feltrin, J.R.S. Mantovani, Network reconfigurationand loss allocation for distribution systems with distributed generation [C]. IEEE/PESTransmission and Distribution Conference and Exposition: Latin America,2004:206-211.
[66] J. Olamaei, G. Gharehpetian, T. Niknam. An approach based on Particle Swarm Optimizationfor distribution feeder reconfiguration considering Distributed Generators [J]. Power SystemsConference: Advanced Metering, Protection, Control, Communication, and DistributedResources,2007:326-330.
[67]景乾明,邹必昌,应若冰等.含分布式发电以综合费用最低为目标的配电网重构[J].中国农村水利水电，2011,(09):130-133.
[68]邹必昌,李涛,唐陶波.含分布式发电以提高供电电压质量为目的的配电网重构研究[J].电气应用,2011,(12):23-26.",
[69]邹必昌,许可,李涛,唐涛波.基于负荷平衡的含分布式发电配电网重构研究[J].陕西电力,2011,(07):5-8.
[70]邹必昌,刘晔,李涛,唐桃波.含分布式发电的配电网重构蚁群算法研究[J].陕西电力,2011,(09):14-18.
[71]代江.分布式电源优化配置与配电网络重构研究[D].重庆大学硕士论文,2011.
[72] C. Rung-Fang, C. Shi-Jaw, C. Ya-Chin, L. Chan-Nan. Modified particle swarm optimizationfor solving distribution feeder reconfiguration problem with distributed generation [C].2010IEEE Region10Conference,2010:1796-1801.
[73]徐玉琴,张丽,王增平,李雪冬.基于多智能体遗传算法并考虑分布式电源的配电网大面积断电供电恢复算法[J].电工技术学报,2010,25(04):135-141.
[74] J.H. Yu Xiaodan, Wang Chengshan, et al.Network reconfiguration for distribution system withmicro-grids[C]. The1stSUPERGEN conference, Nanjing, China,2009.
[75]万强,孙昊,王乾,谢晓琳,王鹏.基于合作型协同进化遗传算法分布式发电供电恢复[J].四川电力技术,2012,35(03):23-26+35.
[76]卢志刚,董玉香.含分布式电源的配电网故障恢复策略[J].电力系统自动化,2007,31(1):89-92.
[77]易新,陆于平.分布式发电条件下的配电网孤岛划分算法[J].电网技术,2006,30(7):50-54.
[78]安有为.基于自适应多种群遗传算法的分布式发电多目标故障恢复[J].信息系统工程,2010,(11):38-42.,2010.
[79]刘佳,李丹,高立群，宋立新.多目标无功优化的向量评价自适应粒子群算法[J].2008,28(31):22-28.
[80]娄素华,吴耀武,熊信银.基于适应度空间距离评估选取的多目标粒子群算法在电网无功优化中的应用[J].电网技术,2007,31(19):41-46.
[81]段献忠，李智欢，李银红．采用多局部搜索策略的无功优化多模因算法[J]．中国电机工程学报,2008,28(34):59-65.
[82]刘述奎，李奇，陈维荣，等．改进粒子群优化算法在电力系统多目标无功优化中应用[J]．电力自动化设备，2009，29(11)：31-36．
[83]张丽,徐玉琴,王增平,李雪冬,李鹏.包含分布式电源的配电网无功优化[J].电工技术学报,2011,26(3):168-173.
[84]何禹清,彭建春,毛丽林,曹丽华.含多个风电机组的配电网无功优化[J].电力系统自动化,2010,34(19):37-41.
[85] M. Martinez-Rojas, A. Sumper, O. Gomis-Bellmunt, A. Sudrià-Andreu. Reactive powerdispatch in wind farms using particle swarm optimization technique and feasible solutionssearch [J]. Appl. Energy,2011,88(12):4678-4686.
[86]张长胜.含分布式电源的配电网无功补偿优化配置[D].天津大学硕士论文.2010.
[87]陈琳,钟金,倪以信,甘德强,熊军,夏翔.含分布式发电的配电网无功优化[J].电力系统自动化,2006,30(14):20-24.
[88] X.H. Wang, Y.M. Zhang. Multi-Objective Reactive Power Optimization Based On the FuzzyAdaptive Particle Swarm Algorithm [J]. Procedia Engineering,2011,16:230-238.
[89] A.R. Malekpour, S. Tabatabaei, T. Niknam. Probabilistic approach to multi-objective Volt/Varcontrol of distribution system considering hybrid fuel cell and wind energy sources usingImproved Shuffled Frog Leaping Algorithm [J]. Renewable Energy,2012,39(1):228-240.
[90] T. Niknam. A new HBMO algorithm for multiobjective daily Volt/Var control in distributionsystems considering Distributed Generators [J]. Appl. Energy,2011,88(3),778-788.
[91]魏希文,邱晓燕,李兴源,张子健.含风电场的电网多目标无功优化[J].电力系统保护与控制,2010,38(17):107-111.
[92] M. Alonso, H. Amaris, C. Alvarez-Ortega. A multiobjective approach for reactive powerplanning in networks with wind power generation [J]. Renewable Energy,2012,37(1):180-191.
[93]陈海焱,陈金富,段献忠.含风电机组的配网无功优化[J].中国电机工程学报,2008,28(7):40-45.
[94] M.ó. Alonso, H. Amarís, M. Chindris. A multiobjective Var/Volt Management System inSmartgrids [J]. Energy Procedia,2012,14:1490-1495.
[95]赵亮,吕剑虹.基于改进遗传算法的风电场多目标无功优化[J].电力自动化设备,2010,30(10):84-88.
[96]杨佩佩,艾欣,崔明勇,雷之力.基于粒子群优化算法的含多种供能系统的微网经济运行分析[J].电网技术,2009,33(20):38-42.
[97] A. Bagherian, S.M.M. Tafreshi. A developed energy management system for a microgrid inthe competitive electricity market [C]. IEEE Bucharest PowerTech,2009:1-6.
[98]郭力,王守相,许东,王成山.冷电联供分布式供能系统的经济运行分析[J].电力系统及其自动化学报,2009,21(05):8-12.
[99]丁明,包敏,吴红斌,马婉玲,茆美琴.复合能源分布式发电系统的机组组合问题[J].电力系统自动化,2008,32(06):46-50.
[100] J.-J. Wang, Y.-Y. Jing, C.-F. Zhang,"Optimization of capacity and operation for CCHP systemby genetic algorithm", Appl. Energy,87(4),1325-1335,2010.
[101]艾欣,崔明勇,雷之力.基于混沌蚁群算法的微网环保经济调度[J].华北电力大学学报(自然科学版),2009,36(05):1-6.
[102]公茂果,焦李成,杨咚咚,马文萍.进化多目标优化算法研究[J].软件学报，2009,20(2):271-289.
[103] S. Carter. Emissions from distributed generation [EB/01]. http://www.distributed-generation.com/Library/Emissions.PDF.2000
[104]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.
[105] M.R. Sierra, C.A.C. Coello. Multi-objective particle swarm optimizers: A survey of thestate-of-the-art[J]. International Journal of Computational Intelligence Research,2006,2(3):287-308.
[106] K. Deb. Multi-objective optimization using evolutionary algorithms [M]1st ed. New York:Wiley,2001.
[107]邱威,张建华,刘念.自适应多目标差分进化算法在计及电压稳定性的无功优化中的应用[J].电网技术,2011,35(8):81-86.
[108]胡彩娥.考虑静态电压稳定裕度的多目标电压/无功规划[D].中国农业大学硕士论文.2004.
[109]李鑫滨,朱庆军.一种改进粒子群优化算法在多目标无功优化中的应用[J].电工技术学报,2010,25(7):137-143.
[110]熊宁,陈恳,戴伟华.基于禁忌算法的多目标无功优化[J].继电器,2006,34(24):21-25+32.
[111]王勤,方鸽飞.考虑电压稳定性的电力系统多目标无功优化[J].电力系统自动化,1999,23(3):31-34.
[112]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.
[113] W. Zhang, Y. Liu. Multi-objective reactive power and voltage control based on fuzzyoptimization strategy and fuzzy adaptive particle swarm [J]. International Journal of ElectricalPower and Energy Systems,2008,30(9):525-532.
[114]陈得宇,张仁忠,沈继红，高世伟.基于适应性权重遗传算法的多目标无功优化研究[J].电力系统保护与控制,2010,38(6):1-7+25.
[115]夏可青,赵明奇,李扬.用于多目标无功优化的自适应遗传算法[J].电网技术,2006,30(13):55-60.
[116] M. Gen, R. Cheng. Genetic Algorithms and Engineering Optimization [M]. NewYork:Wiley-Interscience,1999.
[117] Y. Lu, J. Zhou, H. Qin, Y. Wang, Y. Zhang. A hybrid multi-objective cultural algorithm forshort-term environmental/economic hydrothermal scheduling [J]. Energy Convers. Manage.,2011,52(5):2121-2134.
[118] X.D. Wang, C. Hirsch, S. Kang, C. Lacor. Multi-objective optimization of turbomachineryusing improved NSGA-II and approximation model [J]. Computer Methods in AppliedMechanics and Engineering,2011,200(9-12):883-895.
[119]徐鸣.基于群智能的鲁棒多目标优化方法及应用[D].浙江大学博士学位论文,2011.
[120] C.H. Antunes, D.F. Pires, C. Barrico, á. Gomes, A.G. Martins. A multi-objective evolutionaryalgorithm for reactive power compensation in distribution networks[J]. Appl. Energy,2009,86(7–8):977-984.
[121] A. Alarcon-Rodriguez, G. Ault, S. Galloway. Multi-objective planning of distributed energyresources: A review of the state-of-the-art [J]. Renewable Sustainable Energy Rev.,2010,14(5):1353-1366.
[122]黄平.粒子群算法改进及其在电力系统的应用[D].华南理工大学博士学位论文，2012.
[123]冯士刚,艾芊.带精英策略的快速非支配排序遗传算法在多目标无功优化中的应用[J],电工技术学报,2007,22(12):146-151.
[124] M. Azzam, A.A. Mousa. Using genetic algorithm and TOPSIS technique for multiobjectivereactive power compensation [J]. Electr. Power Syst. Res.,2010,80(6):675-681.
[125] S. Jeyadevi, S. Baskar, C.K. Babulal, M. Willjuice Iruthayarajan. Solving multiobjectiveoptimal reactive power dispatch using modified NSGA-II[J]. Int. J. Electric. Power EnergySyst.,2011,33(2):219-228.
[126] L. Furong, J.D. Pilgrim, C. Dabeedin, A. Chebbo, R.K. Aggarwal. Genetic algorithms foroptimal reactive power compensation on the national grid system[J]. IEEE Transactions onPower Systems,2005,20(1):493-500.
[127] L. Zhihuan, L. Yinhong, D. Xianzhong. Non-dominated sorting genetic algorithm-II forrobust multi-objective optimal reactive power dispatch[J]. IET Generation, Transmission&Distribution,2010,4(9),1000-1008.
[128] M.A. Abido, J.M. Bakhashwain. Optimal VAR dispatch using a multiobjective evolutionaryalgorithm[J]. Int. J. Electric. Power Energy Syst.,2005,27(1):13-20.
[129] B.A. de Souza, A.M.F. de Almeida. Multiobjective Optimization and Fuzzy Logic Applied toPlanning of the Volt/Var Problem in Distributions Systems[J]. IEEE Transactions on PowerSystems,2010,25(3):1274-1281.
[130]刘述奎,陈维荣,李奇,戴朝华,段涛.基于自适应聚焦粒子群优化算法的电力系统多目标无功优化[J].电网技术,2009,33(13):48-53.
[131]王云,张伏生,陈建斌,段光辉.电力系统多目标无功优化研究[J].西安交通大学学报,2008,42(2):213-217.
[132] S. Mostaghim, J. Teich. The role of ε-dominance in multi-objective particle swarmoptimization methods [C]. presented at the Proc. of IEEE Swarm Intelligence Symposium,Canberra,2003.
[133]熊虎岗,程浩忠,胡泽春,贾德香.基于混沌免疫混合算法的多目标无功优化[J],电网技术，2007,31(11):33-37.
[134] Z. Junqi, L. Kun, T. Ying, H. Xingui. Random black hole particle swarm optimization and itsapplication [C]. in2008International Conference on Neural Networks and Signal Processing,2008:359-365.
[135] E. Zitzler, K. Deb, L. Thiele. Comparison of multiobjective evolutionary algorithms:empirical results [J]. Evolutionary Computation,2000,8(2):173-195.
[136]韩宵松.快速群智能优化算法的研究[D]吉林大学博士学位论文,2012.
[137]冯春时.群智能优化算法及其应用[D].中国科学技术大学,2009.
[138] M. Jacqueline, C. Richard. Application of particle swarm to multiobjective optimization [H].Department of Computer Science and Software Engineering, Auburn University,1999.
[139] M. Laumanns, L. Thiele, K. Deb, E. Zitzler. Combining Convergence and Diversity inEvolutionary Multiobjective Optimization [J]. Evolutionary Computation,2002,10(3):263-282.
[140] M. Sierra, C. Coello Coello. Improving PSO-Based Multi-objective Optimization UsingCrowding, Mutation and ε-Dominance [C]. Presented at the Int. Conf. on EvolutionaryMulti-Criterion Optimization, Guanajuato,2005.
[141] X.D. Li. Better spread and convergence: particle swarm multi-objective optimization usingthe maximin fitness function[C]. in Proc. of the Genetic and Evolutionary Computation Conf.,Washington,2004:117-128.
[142]黄平,于金杨,元泳泉.一种改进的小生境多目标粒子群优化算法[J].计算机工程,2011,37(18):1-3.
[143]贾树晋,杜斌,岳恒.基于局部搜索与混合多样性策略的多目标粒子群算法[J].控制与决策,2012,27(6):813-818+826.
[144]丛琳,焦李成,沙宇恒.正交免疫克隆粒子群多目标优化算法[J]，电子与信息学报,2008,30(10):2320-2324.
[145] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan. A fast and elitist multiobjective geneticalgorithm: NSGA-II [J]. IEEE Trans. Evol. Comput.,2002,6(2):182-197.
[146]施展,陈庆伟.基于QPSO和拥挤距离排序的多目标量子粒子群优化算法[J].控制与决策,2011,26(4):540-547.
[147] M. Mahfouf, M.Y. Chen, D.A. Linkens. Adaptive weighted particle swarm optimisation formulti-objective optimal design of alloy steels [J]. Lect. Note Comput. Sci.,2004:762-771.
[148]陈民铀,张聪誉,罗辞勇.自适应进化多目标粒子群优化算法[J].2009,24(12):1851-1855+1864.
[149] X.D. Li. A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization[J]. Lecture Notes Compute. Sci.,2003,2723:27-48.
[150] M. Salazar-Lechuga, J.E. Rowe. Particle swarm optimization and fitness sharing to solvemulti-objective optimization problems [C]. in The2005IEEE Congress on EvolutionaryComputation,2005:1204-1211.
[151] S.L. Ho, Y. Shiyou, N. Guangzheng, E.W.C. Lo, H.C. Wong. A particle swarmoptimization-based method for multiobjective design optimizations [J]. IEEE Transactions onMagnetics,2005,41(5):1756-1759.
[152] C.A. Coello Coello, D.A.a. Van Veldhuizen, G.B. Lamont. Evolutionary Algorithms forSolving Multi-Objective Problems [M]. New York: Kluwer Academic Publishers,2002.
[153] L.H. Zhang, S. Hu. A New Approach to Improve Particle Swarm Optimization [J]. LectureNotes in Computer Science,2003,2723:134-139,2003.
[154] W.C. Fuqua, S.C. Winans, E.P. Greenberg. Quorum sensing in bacteria: the LuxR LuxI familyof cell density responsive transcriptional regulators [J]. J. Bacteriol.,1994,176(2):269-275.
[155] A. Eberhard. Inhibition and Activation of Bacterial Luciferase Synthesis [J]. J. Bacteriol.,1972,109(3):1101-1105.
[156] K.H. Nealson, T. Platt, J.W. Hastings. Cellular control of the synthesis and activity of thebacterial luminescent system [J]. J. Bacteriol.,1970,104(1):313-322.
[157] S. Huband, P. Hingston, L. Barone, L. While. A review of multiobjective test problems and ascalable test problem toolkit [J]. IEEE Transactions on Evolutionary Computation,2006,10(5):477-506.
[158] C.A.C. Coello, G.T. Pulido, M.S. Lechuga. Handling multiple objectives with particle swarmoptimization [J]. IEEE Trans. Evol. Comput.,2004,8(3):256-279.
[159]王瑞琪,张承慧,李珂.基于改进混沌优化的多目标遗传算法[J]，控制与决策,2011,26(9):1391-1397.
[160]吴亚丽,徐丽青.一种基于粒子群算法的改进多目标文化算法[J].控制与决策,2012,27(8):1127-1132.
[161] T. Hoff, D.S. Shugar. The value of grid support photovoltaic in reducing distribution systemlosse [J]. IEEE Transactions on Energy Conversion,1995,10(3):569-576.
[162] R. Ramakumar, N.G. Butler, A.P. Rodriguez, S.S. Venkata. Economic aspects of advancedenergy technologies [J]. Proceedings of the IEEE,1993,81(3):318-332.
[163]钱科军,袁越,石晓丹,ZHOU Chengke,鞠平.分布式发电的环境效益分析[J].中国电机工程学报,2008,28(29):11-15.
[164] D.H. Popovic, J.A. Greatbanks, M. Begovic, A. Pregelj. Placement of distributed generatorsand reclosers for distribution network security and reliability [J]. Int. J. Electric. PowerEnergy Syst.,2005,27(5-6):398-408.
[165] M. Chakravorty, D. Das. Voltage stability analysis of radial distribution networks [J]. Int. J.Electric. Power Energy Syst.,2001,23(2):129-135.
[166] J. Liu, P.X. Bi, H.P. Dong, Analysis and optimization of complex distribution networks,1ed.Beijing: China Electric Power Press,2002.
[167]杨咚咚,焦李成,公茂果,余航.求解偏好多目标优化的克隆选择算法[J].软件学报,2010,(01):14-33.
[168]李晶,王素华,谷彩连.基于遗传算法的含分布式发电的配电网无功优化控制研究[J].电力系统保护与控制,2010,38(6):60-63.
[169] G. Celli, E. Ghiani, S. Mocci, F. Pilo. A multiobjective evolutionary algorithm for the sizingand siting of distributed generation [J]. IEEE Trans. Power Syst.,2005,20(2):750-757.
[170] K.V. Kumar, M.P. Selvan. Planning and operation of Distributed Generations in distributionsystems for improved voltage profile [C]. in IEEE/PES2009Power Systems Conference andExposition,2009:1-7.
[171] A. Parizad, A. Khazali, M. Kalantar. Optimal placement of distributed generation withsensitivity factors considering voltage stability and losses indices [C]. in201018th IranianConf. Electrical Engineering,2010:848-855.
[172] M.E. Baran, F.F. Wu. Network Reconfiguration in Distribution Systems for Loss Reductionand Load Balancing [J]. IEEE Power Eng. Rev.,1989,9(4):101-102.
[173] W. Li, Y. Tai-Her, L. We-Jen, C. Zhe. Benefit Evaluation of Wind Turbine Generators in WindFarms Using Capacity-Factor Analysis and Economic-Cost Methods [J]. IEEE Trans. PowerSyst.,2009,24(2):692-704.
[174] I.E. Agency. World Energy Outlook (WEO)[R].2007.
[175] A. Zahedi. Maximizing solar PV energy penetration using energy storage technology [J].Renewable Sustainable Energy Rev.,2011,15(1):866-870.
[176] L. Changling, O. Boon-Teck. Frequency deviation of thermal power plants due to wind farms[J]. IEEE Transactions on Energy Conversion,2006,21(3):708-716.
[177]李国杰,唐志伟,聂宏展,谭靖.钒液流储能电池建模及其平抑风电波动研究[J].电力系统保护与控制,2010,38(22):115-119+125.
[178] Z. Tang. Modelling and Controlling of Vanadium Redox Flow Battery to Smooth Wind PowerFluctuations [D]. Northeast Dianli University (Master),2011.
[179] L. Wei, G. Joos, C. Abbey. Wind Power Impact on System Frequency Deviation and an ESSbased Power Filtering Algorithm Solution [C]. in2006IEEE PES Power Systems Conferenceand Exposition,2006:2077-2084.
[180]朱松燃,张勃然.铅酸蓄电池技术[M].北京:机械工业出版社,1988.
[181]李立伟,邹积岩.蓄电池放电能量并网装置[J].电力系统自动化,2003,27(6):80-83.
[182] L. Wei, G. Joos. Performance Comparison of Aggregated and Distributed Energy StorageSystems in a Wind Farm For Wind Power Fluctuation Suppression [C]. in IEEE PowerEngineering Society General Meeting,2007:1-6.
[183] L. Hui, D. Cartes, M. Steurer, T. Haibin. Control design of STATCOM with superconductivemagnetic energy storage [J]. IEEE Transactions on Applied Superconductivity,2005,15(2):1883-1886.
[184] M. Ross, R. Hidalgo, C. Abbey, G. Joos. Analysis of Energy Storage sizing and technologies[J]. IEEE Electric Power and Energy Conference (EPEC),2010:1-6.
[185]王虹富,曹军,邱家驹,魏新颖.一种用于分布式发电系统的有功功率补偿模型[J].电力系统自动化,2009,33(8):94-98.
[186] IEEE Application Guide for IEEE Std1547[S].2003.
[187] PSERC Final Project Report. Evaluation of distributed electric energy storage andgeneration[R]. Wichita State University,1996.
[188]黄志刚，李林川，杨理，李彬.电力市场环境下的无功优化模型及其求解方法[J]．中国电机工程学报，2003，23(12)：79-83．
[189]张伯明等.高等电力网络分析[M].北京:清华大学出版社,1996.
[190]庄园,贺海,杨晓慧,王磊.含分布式电源优化调度的配电网络重构[J].电网与清洁能源,2012,(11):13-18.
[191] S.F. Adra, P.J. Fleming. Diversity Management in Evolutionary Many-Objective Optimization,IEEE Trans. Evol. Comput.,2011,15(2):183-195.