厂网协调模式下流域梯级电站群短期联合优化调度研究
|
摘要
厂网协调模式下的流域梯级电站群短期联合优化调度受水文径流过程、电网负荷需求、机组状态、下游用水需求等多种影响因素制约,是一类非线性、高维度、多约束的复杂水-机-电耦合优化问题,也是水电能源科学与复杂性科学交叉发展的重要研究领域之一。随着我国大型梯级电站群的相继建成与投运,流域梯级水电能源系统的规模与拓扑结构日趋复杂,流域电站群短期联合优化调度涌现出一系列新问题。水电能源综合利用要求大幅提高,而互联大电网下流域水能资源联合优化调度还缺乏统一有效的管理和协调机制；电力供需矛盾日益突出,易出现负荷峰谷差过大和调峰容量不足的情况；在负荷低谷时段,水电站往往被迫运行于低效率区,导致整个电网经济效益显著降低,因此,亟需协调电网吸纳与电站出力关系,研究并发展新的厂网协调模式下的流域梯级电站群短期联合优化调度理论与方法。本文围绕全国互联大电网背景下流域梯级电站群联合优化运行存在的关键科学问题和技术难题,以水电能源学、系统科学、群智能优化等基础理论为支撑,以三峡梯级电站群为主要研究对象,对厂网协调模式下梯级电站群短期联合优化调度建模和求解方法开展研究,取得了一些有理论意义和工程实用价值的成果。主要研究内容和创新包括：
(1)针对水电站机组检修计划、健康状况、运行状态等对机组稳定运行的影响程度,提出水电机组综合运行状态评价指标与评价模型。结合本文所提出的综合运行状态评价指标,对水电机组在当前面临时段发电优化调度中的综合运行状态进行评价,由此确定可参与运行机组的开机或停机优先次序,为优化调度过程时段间负荷变化引起的增加或减少机组台数提供选择依据；通过对机组母线出力限制、机组气蚀振动区限制与机组出力上下限求取交集,精确描述机组稳定出力区域的边界值,为水电站安全、稳定运行提供可靠的数据支撑。
(2)在水电站机组综合运行评价的基础上,提出基于多种群蚁群优化算法的梯级电站实时自动发电控制方法。针对不同时间尺度,以梯级电站耗水量最小为目标,分别建立单时段梯级电站实时发电控制与当前时段至余留期梯级电站自动发电控制的数学优化模型,提出适用解决电站机组组合问题的多种群蚁群优化算法,在收敛过程中以前期最优解集不断替代进化种群,加速模型求解速度,合理分配梯级各电站所需承担的系统负荷,并根据时段负荷需求在各级电站内制定最优开机机组组合与机组间最优负荷分配方案,有效满足电网实时性要求。
(3)综合分析电网中梯级电站日发电计划编制与厂内经济运行在发电过程中的共性与差异性,提出梯级电站日发电计划编制与厂内经济运行循环嵌套模式。针对二者优化目标不同而产生的电站下泄流量可行域冲突问题,将日发电计划编制优化结果作为厂内经济运行优化计算的输入,对发电计划编制方案进行目标反向演算,二者循环嵌套计算直至满足各项约束条件,同时获得梯级电站最优发电计划与厂内经济运行方案,实现二者的一体化运作,为梯级电站调度中心水调部门与电调部门以及电网调度中心的有机协调运行提供保障,且有效降低发电计划与电网实际发电需求的偏差。
(4)结合流域梯级电站群归口管理层级、隶属电网关系、区域分布特征以及电站间的水力、电力补偿关系,提出厂网协调模式流域梯级电站群分层分区分级短期精细化调度方法。对流域梯级电站群短期发电优化调度进行分层分区分级划分,以流域梯级电站群短期发电量最大或效益最优为目标,以中长期优化调度水位消落过程为指导,建立流域梯级电站群短期发电优化调度模型,给出最优发电流量和有功负荷分配方案,制定流域梯级电站群短期联合最优发电计划,提高流域梯级电站群的短期发电效益。
(5)以流域水文过程、电网负荷要求以及不同部门间用水需求的非线性耦合特性作为水火电力系统优化的建模基础,针对水火电能源丰枯、峰谷补偿特性,研究水火电力系统可描述化网络拓扑及其补偿调节机制,建立水火电力系统短期联合优化调度模型,提出基于实数差分量子进化算法的水火电力系统优化方法与启发式约束处理策略,有效处理系统运行约束,实现水火电力系统运行域边界的精确描述,制定水火电力系统短期优化调度方案,为区域电网的安全、稳定、经济运行提供数量基础和理论依据。
The short-term optimal operation of cascade hydropower stations is a nonlinear, high-dimensional, multi-constraint, complex water-machine-electrical coupling optimization problem, which is constrained by hydrological runoff process, electric load demand, the units state and water demand of downstream. The short-term optimal operation of hydropower under the coordination on power plants and power grid is one of the most important research field mixed by hydropower and complexity science. With large-scale hydropower system are completed and put into operation in China, the requirements of comprehensive utilization for hydropower have been improved. The scale and topology structure of basin hydroelectric cascade energy system has become more and more complicated. The short-term optimal operation of cascaded hydropower stations emerge a series of new problems. The optimal scheduling of basin water resources is lack of effective management of unity and coordination mechanism under the background of interconnected power grid. The contradiction between power supply and demand has become increasingly prominent. The big gap between load peak and valley appears frequently and peak-load regulating capacity is not enough. In low load period, hydropower stations are often forced to reduce output and abandon water, which will cause great loss of power grid economic benefit. Therefore, it is necessary to develop new optimization theories and methods to solve these problems. This dissertation focuses on the key scientific and technical problems of optimal operation for cascade hydropower stations under the background of joint grid all over the country. Supported by the theories of hydropower science, systems science, and swarm intelligent optimization, Three Gorges cascade hydropower stations are set as the main research object. Finally, we study the modeling and the solving method of short-term optimization operation under the coordination on power plants and power grid and get valuable achievements on theories and engineering practice. The main research contents and innovations include:
(1) Aiming at the influence type and extent caused by maintenance plan, health condition and running state of the hydropower unit, the evaluation index and model of hydropower unit are proposed for comprehensive operation state. Based on the suggested operation state evaluation indexes, the comprehensive operation state of hydropower units is evaluated in the current scheduling period. And then the running and stop priorities is selected for increasing or reducing the number of units. Considering the power limits of transformer, the requirements of unit cavitation vibration area and dynamic characteristics of the units, the upper and lower limits of unit stable output are determined, which gives reliable support for security and stability of hydropower station.
(2) After the comprehensive evaluation of hydropower station is presented, real-time automatic generation control strategy of cascade hydropower stations is proposed by the population-based ant colony optimization. Considering minimizing water consumption as the goal in different time scales, the model of real-time generation control of cascade hydropower stations is established in single schedule period and so is the mathematical optimization model in remaining periods. Meanwhile, a multi-population ant colony optimization is proposed in this literature. According to replace the population by optimal solutions obtained in early evolution, the convergence will be accelerated. Finally, distribute the load demand to each plant and develop the schedule scheme of optimal unit combination as well as the load distribution among each unit.
(3) With analyzing the commonalities and the differences between the daily generation scheduling and internal economic operation(DAI) of the cascade hydropower stations in power grid, the seamless nested mode of the the daily generation scheduling and internal economic operation(DAI) is proposed for cascade hydropower stations. Considering the feasible region conflict problem of discharge volume engendered by the different optimization goals of DAI, the optimal results of daily generation scheduling is provided as the input of internal economic operation calculation to conduct objective reverse calculus for the daily generation scheduling. The optimal scheme of DAI is got until circulation nested calculation end with meeting the constraint conditions, and the integration operation of DAI is realized which can ensure organically coordination dispatching for the water and electricity department in the center of cascade hydropower stations as well as effective reduction of the deviation between the generation plan and actual power demand of the power grid.
(4) Combined with regional distribution characteristics, relationship between district power grids, the hydraulics and electric power compensation of cascade hydropower stations, a division grade refinement scheduling method of cascade hydropower stations is proposed for the coordination model of the power plant and grid. With dividing the subregion and classification for short-term generation optimization scheduling of cascade hydropower stations, the short-term generation optimization scheduling models of cascade hydropower stations for energy maximization and benefits maximization are established according the water level fluctuating process of the long-term optimized scheduling. The optimal the optimal generation flow, active load distribution solutions and short-term optimal generation scheme are obtained by the established model to improve the short-term generation benefit of cascade hydropower stations.
(5) The nonlinear coupling characteristics of the hydrologic processes, power load demand and water demands of different departments is the optimization modeling foundation for the hydrothermal power system. According to plentiful-low and peak-valley compensation characteristic of the hydrothermal electricity energy, the short-term combined optimization scheduling model of the hydrothermal power system is established. And the heuristic constraint processing strategy and optimization method of the hydrothermal power system based on the real difference quantum evolutionary algorithm, are put forward to deal with the operation constraints of the system effectively. To provide theoretical basis and number basis for the regional network safety, stability and economic operation, the operation domain boundary of the hydrothermal power system is been described accurately and short-term optimal generation scheme is formulated for the hydrothermal power system.
(1)针对水电站机组检修计划、健康状况、运行状态等对机组稳定运行的影响程度,提出水电机组综合运行状态评价指标与评价模型。结合本文所提出的综合运行状态评价指标,对水电机组在当前面临时段发电优化调度中的综合运行状态进行评价,由此确定可参与运行机组的开机或停机优先次序,为优化调度过程时段间负荷变化引起的增加或减少机组台数提供选择依据；通过对机组母线出力限制、机组气蚀振动区限制与机组出力上下限求取交集,精确描述机组稳定出力区域的边界值,为水电站安全、稳定运行提供可靠的数据支撑。
(2)在水电站机组综合运行评价的基础上,提出基于多种群蚁群优化算法的梯级电站实时自动发电控制方法。针对不同时间尺度,以梯级电站耗水量最小为目标,分别建立单时段梯级电站实时发电控制与当前时段至余留期梯级电站自动发电控制的数学优化模型,提出适用解决电站机组组合问题的多种群蚁群优化算法,在收敛过程中以前期最优解集不断替代进化种群,加速模型求解速度,合理分配梯级各电站所需承担的系统负荷,并根据时段负荷需求在各级电站内制定最优开机机组组合与机组间最优负荷分配方案,有效满足电网实时性要求。
(3)综合分析电网中梯级电站日发电计划编制与厂内经济运行在发电过程中的共性与差异性,提出梯级电站日发电计划编制与厂内经济运行循环嵌套模式。针对二者优化目标不同而产生的电站下泄流量可行域冲突问题,将日发电计划编制优化结果作为厂内经济运行优化计算的输入,对发电计划编制方案进行目标反向演算,二者循环嵌套计算直至满足各项约束条件,同时获得梯级电站最优发电计划与厂内经济运行方案,实现二者的一体化运作,为梯级电站调度中心水调部门与电调部门以及电网调度中心的有机协调运行提供保障,且有效降低发电计划与电网实际发电需求的偏差。
(4)结合流域梯级电站群归口管理层级、隶属电网关系、区域分布特征以及电站间的水力、电力补偿关系,提出厂网协调模式流域梯级电站群分层分区分级短期精细化调度方法。对流域梯级电站群短期发电优化调度进行分层分区分级划分,以流域梯级电站群短期发电量最大或效益最优为目标,以中长期优化调度水位消落过程为指导,建立流域梯级电站群短期发电优化调度模型,给出最优发电流量和有功负荷分配方案,制定流域梯级电站群短期联合最优发电计划,提高流域梯级电站群的短期发电效益。
(5)以流域水文过程、电网负荷要求以及不同部门间用水需求的非线性耦合特性作为水火电力系统优化的建模基础,针对水火电能源丰枯、峰谷补偿特性,研究水火电力系统可描述化网络拓扑及其补偿调节机制,建立水火电力系统短期联合优化调度模型,提出基于实数差分量子进化算法的水火电力系统优化方法与启发式约束处理策略,有效处理系统运行约束,实现水火电力系统运行域边界的精确描述,制定水火电力系统短期优化调度方案,为区域电网的安全、稳定、经济运行提供数量基础和理论依据。
The short-term optimal operation of cascade hydropower stations is a nonlinear, high-dimensional, multi-constraint, complex water-machine-electrical coupling optimization problem, which is constrained by hydrological runoff process, electric load demand, the units state and water demand of downstream. The short-term optimal operation of hydropower under the coordination on power plants and power grid is one of the most important research field mixed by hydropower and complexity science. With large-scale hydropower system are completed and put into operation in China, the requirements of comprehensive utilization for hydropower have been improved. The scale and topology structure of basin hydroelectric cascade energy system has become more and more complicated. The short-term optimal operation of cascaded hydropower stations emerge a series of new problems. The optimal scheduling of basin water resources is lack of effective management of unity and coordination mechanism under the background of interconnected power grid. The contradiction between power supply and demand has become increasingly prominent. The big gap between load peak and valley appears frequently and peak-load regulating capacity is not enough. In low load period, hydropower stations are often forced to reduce output and abandon water, which will cause great loss of power grid economic benefit. Therefore, it is necessary to develop new optimization theories and methods to solve these problems. This dissertation focuses on the key scientific and technical problems of optimal operation for cascade hydropower stations under the background of joint grid all over the country. Supported by the theories of hydropower science, systems science, and swarm intelligent optimization, Three Gorges cascade hydropower stations are set as the main research object. Finally, we study the modeling and the solving method of short-term optimization operation under the coordination on power plants and power grid and get valuable achievements on theories and engineering practice. The main research contents and innovations include:
(1) Aiming at the influence type and extent caused by maintenance plan, health condition and running state of the hydropower unit, the evaluation index and model of hydropower unit are proposed for comprehensive operation state. Based on the suggested operation state evaluation indexes, the comprehensive operation state of hydropower units is evaluated in the current scheduling period. And then the running and stop priorities is selected for increasing or reducing the number of units. Considering the power limits of transformer, the requirements of unit cavitation vibration area and dynamic characteristics of the units, the upper and lower limits of unit stable output are determined, which gives reliable support for security and stability of hydropower station.
(2) After the comprehensive evaluation of hydropower station is presented, real-time automatic generation control strategy of cascade hydropower stations is proposed by the population-based ant colony optimization. Considering minimizing water consumption as the goal in different time scales, the model of real-time generation control of cascade hydropower stations is established in single schedule period and so is the mathematical optimization model in remaining periods. Meanwhile, a multi-population ant colony optimization is proposed in this literature. According to replace the population by optimal solutions obtained in early evolution, the convergence will be accelerated. Finally, distribute the load demand to each plant and develop the schedule scheme of optimal unit combination as well as the load distribution among each unit.
(3) With analyzing the commonalities and the differences between the daily generation scheduling and internal economic operation(DAI) of the cascade hydropower stations in power grid, the seamless nested mode of the the daily generation scheduling and internal economic operation(DAI) is proposed for cascade hydropower stations. Considering the feasible region conflict problem of discharge volume engendered by the different optimization goals of DAI, the optimal results of daily generation scheduling is provided as the input of internal economic operation calculation to conduct objective reverse calculus for the daily generation scheduling. The optimal scheme of DAI is got until circulation nested calculation end with meeting the constraint conditions, and the integration operation of DAI is realized which can ensure organically coordination dispatching for the water and electricity department in the center of cascade hydropower stations as well as effective reduction of the deviation between the generation plan and actual power demand of the power grid.
(4) Combined with regional distribution characteristics, relationship between district power grids, the hydraulics and electric power compensation of cascade hydropower stations, a division grade refinement scheduling method of cascade hydropower stations is proposed for the coordination model of the power plant and grid. With dividing the subregion and classification for short-term generation optimization scheduling of cascade hydropower stations, the short-term generation optimization scheduling models of cascade hydropower stations for energy maximization and benefits maximization are established according the water level fluctuating process of the long-term optimized scheduling. The optimal the optimal generation flow, active load distribution solutions and short-term optimal generation scheme are obtained by the established model to improve the short-term generation benefit of cascade hydropower stations.
(5) The nonlinear coupling characteristics of the hydrologic processes, power load demand and water demands of different departments is the optimization modeling foundation for the hydrothermal power system. According to plentiful-low and peak-valley compensation characteristic of the hydrothermal electricity energy, the short-term combined optimization scheduling model of the hydrothermal power system is established. And the heuristic constraint processing strategy and optimization method of the hydrothermal power system based on the real difference quantum evolutionary algorithm, are put forward to deal with the operation constraints of the system effectively. To provide theoretical basis and number basis for the regional network safety, stability and economic operation, the operation domain boundary of the hydrothermal power system is been described accurately and short-term optimal generation scheme is formulated for the hydrothermal power system.
引文
[1]《节能发电调度办法(试行)》国务院办公厅.2007.8
[2]水利科学与海洋工程学科发展战略研究报告(2011-2015)[R].国家自然科学基金委员会工程与材料科学部,科学出版社有限责任公司,2011.
[3]霍丽文.2012电力工业形势春季报告之二电力结构调整篇.中国电力报,2012.
[4]Masse P., Les Reserves set et la Regulation de 1'Avenir[M], Edition Hermann.1946
[5]Little JDC. The use of storage water in a hydroelectric system[J]. Journal of the Operations Research Society of America,1955,3 (2):187-197.
[6]Tennessee Valley Authory, WRMM Staff. Testing of different optimization methods on a reservoir subsystem[R]. Report B-16, Knoxville, Tennessee.1977.
[7]James E. Giles, Gregory A. Hoff. Techniques for the developments of a multi-objective optimization model[C]. Proceeding ACM-SE 16 proceedings of the 16th annual southeast regional conference. New York. USA,1978
[8]George W. Tauxe, Robert R, Inman, Dean M. Mades. Multiobjective dynamic programing with application to a reservoir[J]. Water reseources research.1979, 15(6):1403-1408.
[9]Sidney Yakowitz. Dynamic programming applications in water resources[J]. Water resources research.1982,18(4):673-696.
[10]Tang J, Huang W V. A decomposition method for generation scheduling of hydro systems with delays and unpredictable changes in natural inflows[J]. Computers & Industrial Engineering.1992,22(2):147-155.
[11]Perez-Diaz J I, Wilhelmi J R, Arevalo L A. Optimal short-term operation schedule of a hydropower plant in a competitive electricity market[J]. Energy Conversion and Management.2010,51(12):2955-2966.
[12]Wolfgang O, Haugstad A, Mo B, et al. Hydro reservoir handling in Norway before and after deregulation [J]. Energy.2009,34(10):1642-1651.
[13]谭维炎,黄守信,刘健民.初期运行水电站的最优年运行计划——动态规划方法的应用[J].水利水电技术.1963(6)：22-26.
[14]张勇传,李福生,熊斯毅,等.水电站水库群优化调度方法的研究[J].水力发电.1981(4)：48-52.
[15]张勇传,李福生,杜裕福,等.水电站水库调度最优化[J].华中工学院学报.1981,9(6)：49-56.
[16]张勇传,李福生,熊斯毅,等.变向探索法及其在水库优化调度中的应用[J].水力发电学报.1982(2)：1-10.
[17]张勇传,李福生,邴凤山,等.水库优化问题中的经济性与可靠性[J].水电能源科学.1983,1(1)：43-50.
[18]张勇传.优化理论在水库调度中的应用[M].长沙：湖南科学技术出版社,1985.
[19]叶秉如.多目标规划的理论、方法及在水资源规划中的应用[J].河海大学科技情报.1990,10(3)：6-18.
[20]叶秉如,余里红.多目标二次规划非劣解集的理论生成及应用[J].水电能源科学.1991,9(2)：102-110.
[21]董增川,叶秉如.水电站库群优化调度的分解方法[J].河海大学学报.1990,18(6)：70-78.
[22]胡振鹏,冯尚友.大系统多目标递阶分析的“分解-聚合”方法[J].系统工程学报.1988(1)：56-64.
[23]黄强,王世定,颜竹丘.应用大系统递阶控制理论解梯级水电站水库群长期最优调度[J].水电能源科学.1993(2)：80-87.
[24]梅亚东.梯级水库优化调度的有后效性动态规划模型及应用[J].水科学进展.2000(2)：194-198.
[25]马光文.大系统随机控制理论在水库群优化调度中的应用[J].系统工程学报. 1991(2)：46-58.
[26]陈洋波,王先甲,冯尚友.考虑发电量与保证出力的水库调度多目标优化方法[J].系统工程理论与实践.1998(4)：96-102.
[27]覃晖,周建中,肖舸,等.梯级水电站多目标发电优化调度[J].水科学进展.2010(3)：377-384.
[28]刘维烈主编.电力系统调频与自动发电控制[M].中国电力出版社,2005：3-8.
[29]杨建东,赵琨,李玲,等.浅析俄罗斯萨扬-舒申斯克水电站7号和9号机组事故原因[J].水力发电学报.2011(4)：226-234.
[30]王桂平.萨扬水电站“8·17”事故及对中国水电站安全运行警示[J].水电厂自动化.2011(3)：51-54.
[31]方辉钦.BBC公司的水力发电厂监控系统[J].电力系统自动化.1982(6)：52-55.
[32]方辉钦.葛洲坝试点与三峡水电厂自动化[J].电力系统自动化.1993(8)：91.
[33]方辉钦.试论无人值班水电厂的设计及技术要求[J].电力系统自动化.1996(10)：1-3.
[34]伍永刚,王定一,魏守平.水电站AGC中负荷调节策略的研究[J].华中理工大学学报.2000(2)：56-57.
[35]王健.大中型水电站参加电网AGC的安全防护措施[J].水电自动化与大坝监测.2004(1)：64-66.
[36]蒋建文,江红军,牟奎.紫坪铺水电站AGC的设计与实现[J].水力发电.2007(2)：73-74.
[37]曾火琼.浅析水电厂AGC与一次调频的配合[J].水电自动化与大坝监测.2008(1)：40-42.
[38]李国怀,王忠强,张再虎,等.小浪底电厂并网实时调度系统更新改造[J].水电能源科学.2011(10)：141-143.
[39]程抱贵,王鹏宇.龙滩水电站一次调频与AGC二次调频间的策略优化[J].水电站机电技术.2011(5)：52-55.
[40]龚传利,黄家志,潘苗苗.三峡右岸电站AGC功能设计及实现方法[J].水电站机电技术.2008(3)：26-28.
[41]方辉钦.现代水电厂计算机监控技术与试验[M].中国电力出版社,2004：124-143.
[42]于尔铿,周京阳.能量管理系统(EMS)第7讲发电计划(2)—水电计划和交换计划[J].电力系统自动化.1997(7)：83-85.
[43]王雁凌,张粒子,鲍海,等.用优化排序法进行日发电计划的计算[J].电力系统及其自动化学报.2000(5)：32-36.
[44]蔡建章,蔡保锐,蔡华祥,等.过渡期电力市场条件下日发电计划编制研究[J].云南电力技术.2003(3)：4-6.
[45]杨俊杰,周建中,刘大鹏.电力系统日发电计划的启发式遗传算法[J].水力发电.2004(1)：7-11.
[46]蒋东荣,刘学军,李群湛.电力市场环境下电网日发电计划的电量经济分配策略[J].中国电机工程学报.2004(7)：94-98.
[47]黄春雷,赵永龙,过夏明.基于日典型负荷的水电站群日计划方式[J].水电自动化与大坝监测.2005(4)：45-47.
[48]黄春雷.基于径流随机特性的水电站逐日电量计划制定方法[J].水电自动化与大坝监测.2006(6)：8-11.
[49]梁志飞,夏清.精细化日发电计划模型与方法[J].电力系统自动化.2008(17)：26-29.
[50]姚跃庭,鲍正风,李鹏,等.葛洲坝水电站日发电计划制作方法探讨[Z].中国甘肃敦煌：2008,96-103.
[51]蔡治国,张艾东,张娟.葛洲坝电站非汛期日优化发电计划编制方法初步研究[J].应用基础与工程科学学报.2010(3)：419-427.
[52]文庭秋.水电站厂内经济运行实时控制系统[J].计算技术与自动化.1984(1)：21-29.
[53]肖翘云,莫秀英.西津水电站开展水库优化调度和厂内经济运行效果显著[J].水力发电.1986(8)：40-42.
[54]梅亚东,左园忠,朱教新.一类含有0-1变量的厂内经济运行模型及解法[J].水电能源科学.1999(3)：20-21.
[55]马跃先.小型水电站厂内经济运行新模式[J].小水电.1999(6)：9-10.
[56]路志宏,魏守平,罗元胜.基于开关控制策略的厂内经济运行模型及应用[J].水电自动化与大坝监测.2003(1)：11-13.
[57]徐晨光,黄强,赵麦换.中小型水电站厂内经济运行准实时系统的设计与实现[J].水力发电学报.2003(1)：15-20.
[58]张祖鹏,陈森林.葛洲坝水电站厂内经济运行二层模型研究[J].水电能源科学.2010(4)：124-126.
[59]权先璋.动态规化原理在水电站厂内经济运行中的应用[J].水电能源科学.1983(1)：95-104.
[60]Chang S C, Chen C H, Fong I K, et al. Hydroelectric generation scheduling with an effective differential dynamic programming algorithm [J]. IEEE Transactions on Power Systems.1990,5(3):737-743.
[61]姚齐国,张士军,蒋传文,等.动态规划法在水电站厂内经济运行中的应用[J].水电能源科学.1999(1)：46-49.
[62]刘胡,高仕春,万俊,等.东江水电站厂内经济运行动态规划算法[J].水电能源科学.2000(4)：14-15.
[63]程春田,唐子田,李刚,等.动态规划和粒子群算法在水电站厂内经济运行中的应用比较研究[J].水力发电学报.2008(6)：27-31.
[64]张勇传.水电站经济运行原理[M].中国水利水电出版社,1998.
[65]田峰巍,颜竹丘,刘恩锡.大系统优化理论在水电站厂内经济运行中的应用[J].水力发电学报.1988(2)：10-20.
[66]万俊.厂内经济运行的POA算法研究[J].武汉水利电力学院学报.1992(4)： 370-375.
[67]田峰巍,黄强,刘恩锡.非线性规划在水电站厂内经济运行中的应用[J].西安理工大学学报.1987(3)：68-73.
[68]姜铁兵,游大海,康玲,等.水电站厂内经济运行基因遗传算法模型[J].华中科技大学学报.1995(7)：78-81.
[69]王黎,马光文.基于遗传算法的水电站厂内经济运行新算法[J].中国电机工程学报.1998(1)：65-67.
[70]袁晓辉,张双全,王金文,等.拟梯度遗传算法在水电厂厂内经济运行中的应用研究[J].电网技术.2000(12)：66-69.
[71]徐晨光,赵麦换,黄强.基于自组织进化规划的径流式水电站厂内经济运行算法[J].水利水电技术.2005(3)：55-57.
[72]李崇浩,纪昌明,李文武.微粒群算法在水电站厂内经济运行中的应用研究[J].水利水电技术.2006(1)：88-91.
[73]申建建,程春田,张俊,等.蜜蜂进化算法在水电站厂内经济运行中的应用[J].水电能源科学.2008(3)：137-140.
[74]李刚,程春田,唐子田,等.结合禁忌搜索思想的粒子群算法在乌江渡水电站厂内经济运行中的应用研究[J].水力发电学报.2009(2)：128-132.
[75]赵雪花,黄强,吴建华.蚁群算法在水电站厂内经济运行中的应用[J].水力发电学报.2009(2)：139-142.
[76]杨鸿锋,张志刚,黄伟军.差分进化算法及其在水电站厂内经济运行中的应用[J].中国农村水利水电.2009(7)：113-115.
[77]张智晟,龚文杰,段晓燕,等.类电磁机制算法在水电站厂内经济运行中的应用研究[J].电工电能新技术.2011(4)：17-20.
[78]王定一等.水电厂计算机监视与控制[M].中国电力出版社,2001.
[79]蒋传文,权先璋,张勇传.水电站厂内经济运行中的一种混沌优化算法[J].华中理工大学学报.1999(12)：39-40.
[80]王永强,周建中,覃晖,等.基于改进二进制粒子群与动态微增率逐次逼近法混合优化算法的水电站机组组合优化[J].电力系统保护与控制.2011(10)：64-69.
[81]刘建华,段虞荣.网络规划法在梯级水电站短期优化调度中的应用[J].重庆大学学报(自然科学版).1991(1)：25-32.
[82]尚金成,张勇传,岳子忠,等.梯级电站短期优化运行的新模型及其最优性条件[J].水电能源科学.1998(3)：2-10.
[83]陈森林,万俊,刘子龙,等.水电系统短期优化调度的一般性准则(1)——基本概念与数学模型[J].武汉水利电力大学学报.1999(3)：35-38.
[84]陈森林,万俊,刘子龙,等.水电系统短期优化调度的一般性准则(2)——优化模型求解方法及实例应用[J].武汉水利电力大学学报.1999(3)：39-43.
[85]梅亚东,朱教新.黄河上游梯级水电站短期优化调度模型及迭代解法[J].水力发电学报.2000(2)：1-7.
[86]梅亚东,朱教新.梯级水电站短期优化调度软件开发及应用介绍[J].人民珠江.2000(1)：42-44.
[87]王仁权,王金文,伍永刚.福建梯级水电站群短期优化调度模型及其算法[J].云南水力发电.2002(1)：52-53.
[88]吴迎新,王金文,伍永刚.福建水电站群短期发电量最大优化调度模型与算法研究[J].电力系统及其自动化学报.2002(6)：35-39.
[89]程春田,武新宇,申建建,等.大规模水电站群短期优化调度方法Ⅰ：总体概述[J].水利学报.2011(9)：1017-1024.
[90]程春田,申建建,武新宇,等.大规模水电站群短期优化调度方法Ⅳ：应用软件系统[J].水利学报.2012(2)：160-167.
[91]申建建,武新宇,程春田,等.大规模水电站群短期优化调度方法Ⅱ：高水头多振动区问题[J].水利学报.2011(10)：1168-1176.
[92]武新宇,程春田,申建建,等.大规模水电站群短期优化调度方法Ⅲ：多电网调 峰问题[J].水利学报.2012(1)：31-42.
[93]Dorman J, Ewing M. Numerical inversion of seismic surface wave dispersion data and crust-mantle structure in the New York-Pennsylvania area[J]. Journal of Geophysical Research.1962,67:5227-5241.
[94]Windsor JS. Optimization model for reservoir flood control[J]. Water Resources Research.1973,9(5):1103-1114.
[95]伍宏中.水电站群补偿径流调节的线性规划模型及其应用[J].水力发电学报.1998(1)：11-23.
[96]王金文,刘双全.短期水电系统发电调度的优化方法综述[J].水电能源科学.2008(6)：137-143.
[97]Lidgate, D., Amir, B. H. Optimal operational planning for a hydro-electric generation system [C]. IEE Proceedings C:Generation Transmission and Distribution.1988,135(3):169-181.
[98]Wang, J., Yuan, X., Zhang, Y. Short-term scheduling of large-scale hydropower systems for energy maximization [J]. Journal of Water Resources Planning and Management.2004,130(3):198-205.
[99]Mariano SJPS., Catalao JPS., Mendes VMF, et al. Head-dependent maximum power generation in short-term hydro scheduling using nonlinear programming [C]. In:Proceedings of the IASTED International Conference on Energy and Power Systems,2007:247-252.
[100]覃晖.流域梯级电站群多目标联合优化调度与多属性风险决策[D].博士论文.华中科技大学,2011.
[101]Tang J, Huang WV. A decomposition method for generation scheduling of hydro systems with delays and unpredictable changes in natural inflows[J]. Computers & Industrial Engineering.1992,22(2):147-155.
[102]万俊,陈惠源.水电站群优化调度分解协调-聚合分解模型研究[J].水力发电学报,1996,(2)：41-50.
[103]李爱玲.水电站水库群系统优化调度的大系统分解协调方法研究[J].水电能源科学.1997(4)：59-64.
[104]李亮,黄强,肖燕,等DPSA和大系统分解协调在梯级水电站短期优化调度中的应用研究[J].西北农林科技大学学报(自然科学版).2005(10)：125-128.
[105]高仕春,万飚,梅亚东,等.三峡梯级和清江梯级水电站群联合调度研究[J].水利学报.2006(4)：504-507.
[106]Li C, Hus E, Svoboda AJ, et al. Hydro unit commitment in hydrothermal optimization[J]. IEEE Transactions on Power Systems.1997,12(2):764-769.
[107]Nilsson O, Sjelvgren D. Mixed-integer programming applied to short-term planning of a hydrothermal system[J]. IEEE Transactions on power systems,1996, 11(1):281-286.
[108]Nillson O, Sjelvgren D. Variable splitting applied to modeling of start-up costs in short term hydrothermal generation scheduling [J]. IEEE Transactions on Power Systems,1997,12(2):770-775.
[109]Salam MS, Nor KM, Hamdan AR. Comprehensive algorithm for hydrothermal coordination[J]. IEEE proceding-Generation Transmission and Distribution,1997, 144(5):482-488.
[110]严婧,孙帆,岳超源.基于拉格朗日松弛法的梯级水电优化调度系统[J].自动化技术与应用.2007(7)：13-15.
[111]李钰心.水电站经济运行[M].中国电力出版社,1999.
[112]刘胡,高仕春,万俊,等.东江水电站厂内经济运行动态规划算法[J].水电能源科学.2000(4)：14-15.
[113]邓晓娟,权先璋,张勇传.三峡梯级水电站厂内经济运行[J].东北电力技术.2002(4)：1-4.
[114]Larson RE, Kecklerv WG. Applications of dynamic programming to control of water resource systems [J]. Int. Fed. Automat. Control,1996,5(1):15-26.
[115]董增川,许静仪.水电站库群优化调度的多次动态线性规划方法[J].河海大学学报,1990,18(6)：63-69.
[116]梅亚东.梯级水库优化调度的有后效性动态规划模型及应用[J].水科学进展.2000(2)：194-198.
[117]Chow VT, Madiment DR., Tauxe GW. Computer time and memory requirements for DP and DDDP in water resources systems analysis [J]. Water Resource Research,1975,11(5):621-682.
[118]宗航,李承军,周建中,等.POA算法在梯级水电站短期优化调度中的应用[J].水电能源科学.2003(1)：46-48.
[119]邹进,张勇传.模糊动态规划在三峡梯级电站短期优化调度中的应用[J].水利水电技术.2004(8)：89-93.
[120]Huang S. Hydroelectric generation scheduling—an application of genetic-embedded fuzzy system approach[J]. Electric Power Systems Research. 1998,48(1):65-72.
[121]Wardlaw R, Sharif M. Evaluation of genetic algorithm for optimal reservoir system operatipon[J]. Journal of Water Resources Planning and Management,1999, 125(1):25-33.
[122]王德意,罗兴锜,张宇峰,等.改进遗传算法在水电站AGC中的应用研究[J].水力发电学报.2004(6)：35-39.
[123]杨俊杰,周建中,喻菁,等.基于电力系统日发电计划的混合智能messy遗传算法[J].电力系统自动化.2004(15)：30-33.
[124]Yuan X, Yuan Y, Zhang Y. A hybrid chaotic genetic algorithm for short-term hydro system scheduling[J]. Mathematics and Computers in Simulation.2002, 59(4):319-327.
[125]孙昌佑,马震岳.基于遗传算法的水电站厂内经济运行模型研究[J].水电能源科学.2004(1)：48-50.
[126]武学毅,陈守伦,郭倩.基于FP遣传算法的梯级水库短期优化调度[J].水力 发电学报.2010(1)：72-75.
[127]袁晓辉,王乘,张勇传,等.粒子群优化算法在电力系统中的应用[J].电网技术.2004(19)：55-57.
[128]Chuanwen J, Bompard E. A self-adaptive chaotic particle swarm algorithm for short term hydroelectric system scheduling in deregulated environment [J]. Energy Conversion and Management.2005,46(17):2689-2696.
[129]杨道辉,马光文,严秉忠,等.粒子群算法在水电站日优化调度中的应用[J].水力发电.2006(3)：73-75.
[130]李崇浩,纪昌明,缪益平.基于微粒群算法的梯级水电厂短期优化调度研究[J].水力发电学报.2006(2)：55-57.
[131]Nagesh Kumar D., Janga Reddy M. Multipurpose Reservoir Operation Using Particle Swarm Optimization[J]. Journal of Water Resources Planning and Management,2007,133(3):192-201.
[132]程春田,唐子田,李刚,等.动态规划和粒子群算法在水电站厂内经济运行中的应用比较研究[J].水力发电学报.2008(6)：24-28.
[133]徐刚,马光文,梁武湖,等.蚁群算法在水库优化调度中的应用[J].水科学进展,2005,16(3)：397-400.
[134]徐刚,马光文.基于蚁群算法的梯级水电站群优化调度[J].水力发电学报,2005,24(5)：7-10.
[135]赵雪花,黄强,吴建华.蚁群算法在水电站厂内经济运行中的应用[J].水力发电学报.2009(2)：139-142.
[136]王永强,周建中,肖文,等.多种群蚁群优化算法在大型水电站自动发电控制机组优化组合中的应用[J].电网技术.2011(9)：66-70.
[137]Yuan X, Zhang Y, Wang L, et al. An enhanced differential evolution algorithm for daily optimal hydro generation scheduling [J]. Computers & amp; Mathematics with Applications.2008,55(11):2458-2468.
[138]黄强,张洪波,原文林,等.基于模拟差分演化算法的梯级水库优化调度图研究[J].水力发电学报.2008(6)：13-17.
[139]覃晖,周建中,肖舸,等.梯级水电站多目标发电优化调度[J].水科学进展.2010(3)：377-384.
[140]Liang RH. A noise annealing neural network for hydroelectric generation scheduling with pumped units[J]. IEEE Transactions on Power Systems,2000, 15(3):1008-1013.
[141]韦柳涛,梁年生,虞锦江.神经网络理论在梯级水电厂短期优化调度中的应用[J].水电能源科学.1992(3).
[142]胡铁松,万永华,冯尚友.水库群优化调度函数的人工神经网络方法研究[J].水科学进展.1995(1)：53-60.
[143]邱林,田景环,段春青,等.混沌优化算法在水库优化调度中的应用[J].中国农村水利水电.2005(7)：17-18.
[144]左幸,马光文,徐刚,等.人工免疫系统在梯级水库群短期优化调度中的应用[J].水科学进展.2007(2)：277-281.
[145]Sherkat VR, Moslehi K, Lo EO, et al. Modular and flexible software for medium and short-term hydrothermal scheduling[J]. IEEE Transactions on Power Systems. 1988,3(3):1390-6.
[146]Ferreira LAFM, Andersson T, Imparato CF, Miller TE, Pang CK, Svoboda A, et al. Short-term resource scheduling in multi-area hydrothermal power sy stems [J]. International Journal of Electrical Power & Energy Systems.1989,11(3):200-12.
[147]Contaxis GC, Kavatza SD. Hydrothermal scheduling of a multireservoir power system with stochastic inflows. IEEE Transactions on Power Systems[J].1990, 5(3):766-73.
[148]Rivera JF, Galdeano CA, Vargas A. Some numerical criteria to measure the validity of hydro-aggregation in hydrothermal systems[J]. International Journal of Electrical Power & Energy Systems.1990,12(l):17-24.
[149]Gonzalez C, Juan J. Reliability evaluation for hydrothermal generating systems: Application to the Spanish case[J], Reliability Engineering & System Safety.1999, 64(1):89-97.
[150]Oliveira GC, Binato S, Pereira MVF. Value-based transmission expansion planning of hydrothermal systems under uncertainty [J]. IEEE Transactions on Power Systems.2007,22(4):1429-35.
[151]陈雪青,陈刚,王世缨,等.水火联合电力系统的优化调度[J].清华大学学报(自然科学版).1985(2)：15-26.
[152]文福栓,韩祯祥.水火电力系统的经济调度——Hopfield连续模型的应用[J].电力系统及其自动化学报.1991(1)：60-67.
[153]袁智强,侯志俭,蒋传文,等.水火电系统古诺模型的均衡分析[J].电力系统自动化.2004(4)：17-21.
[154]韩冬,蔡兴国.综合环境保护及峰谷电价的水火电短期优化调度[J].电网技术.2009(14)：78-83.
[155]覃晖,周建中.基于多目标文化差分进化算法的水火电力系统优化调度[J].电力系统保护与控制.2011(22)：90-97.
[156]卢有麟,周建中,王永强,等.水火电力系统多目标环境经济调度模型及其求解算法研究[J].电力系统保护与控制.2011(23)：93-100.
[157]Kumar S, Sharma J, Ray LM. A nonlinear programming algorithm for hydro-thermal generation scheduling[J]. Computers & Electrical Engineering.1979, 6(3):221-229.
[158]Wang C, Shahidehpour SM. Power generation scheduling multi-area hydro-thermal systems with tie line constraints, cascaded reservoirs and uncertain data[J]. IEEE Transtactions on Power System.1993,5(3):737-743.
[159]Liang R-H, Hsu Y-Y. A hybrid artificial neural network—differential dynamic programming approach for short-term hydro scheduling[J]. Electric Power Systems Research.1995,33(2):77-86.
[160]Salam MS, Nor KM, Hamdam AR. Hydrothermal scheduling based Lagrangian relaxation approach to hydrothermal coordination[J]. IEEE Transactions on Power Systems.1998,13(1):226-235.
[161]Heredia FJ, Nabona N. Optimum short-term hydrothermal scheduling with spinning reserve through network flows[J]. IEEE Transactions on Power Systems, 1995,10(3):1642-1651.
[162]Soares S, Lyra C, Tavares H. Optimal generation scheduling of hydrothermal power systems[J]. IEEE Transactions on Power Systems,1980, PAS-99(3).
[163]Ngundam JM, Kenfack F, Tatietse TT. Optimal scheduling of large-scale hydrothermal power systems using the Lagrangian relaxation technique [J]. International Journal of Electrical Power & Energy Systems.2000,22(4):237-245.
[164]李朝安,杨锐.水库末水位不固定时水火电力系统最优周运行方式的研究和程序开发[J].电力系统自动化.1988(4)：20-25.
[165]朱继忠,徐国禹.用网流法求解水火电力系统有功负荷分配[J].系统工程理论与实践.1995(1)：69-73.
[166]韩学山,吴忠明,宋家骅,等.定火电机组组合方式下水火电系统动态优化调度[J].东北电力学院学报.1997(3)：16-23.
[167]Esteban Gil, Julian Bustos, Hugh Rudnick. Short-term hydrothermal generation scheduling model using a genetic algorithm[J]. IEEE Transactions on Power Systems,2003,18(4):1256-1264.
[168]Basu M. A simulated annealing-based goal-attainment method for economic emission load dispatch of fixed head hydrothermal power systems[J]. International Journal of Electrical Power & Energy Systems.2005,27(2):147-153.
[169]Binghui Yu, Xiaohui Yuan, Jinwen Wang. Short-term hydro-thermal scheduling using particle swarm optimization method[J]. Energy Conversion and Management. 2007,48(7):1902-1908.
[170]Lakshminarasimman L, Subramanian S. Short-term scheduling of hydrothermal power system with cascaded reservoirs by using modified differential evolution. Generation[J], IEE Proceedings-Transmission and Distribution.2006,153(6): 693-700.
[171]Zoumas CE, Bakirtzis AG, Theocharis JB, Petridis V. A genetic algorithm solution approach to the hydrothermal coordination problem [J]. IEEE Transactions on Power Systems.2004,19(3):1356-1364.
[172]马光文,王黎.水火电力系统优化调度的逐步最优遗传算法(英文)[J].水电能源科学.2000(2)：69-72.
[173]袁晓辉,袁艳斌,张勇传.用改进遗传算法求解水火电力系统的有功负荷分配[J].电力系统自动化.2002(23)：33-36.
[174]Youlin Lu, Jianzhong Zhou, Hui Qin, et al. An adaptive chaotic differential evolution for the short-term hydrothermal generation scheduling problem[J]. Energy Conversion and Management.2010,51(7):1481-1490.
[175]李刚,程春田,蔡建章,等.云南电网日调度计划编制系统[J].大连理工大学学报.2006(1)：111-115.
[176]张高峰.梯级水电系统短期优化调度与自动发电控制研究[D].博士论文.华中科技大学,2004.
[177]施冲,余杏林,吴正义,等.水电厂自动发电控制的工程设计与分析[J].电力系统自动化.2001(4)：57-59.
[178]龚传利,黄家志,潘苗苗.三峡右岸电站AVC功能设计及实现方法[J].水电自动化与大坝监测.2009(1)：19-21.
[179]张勇传.系统辨识及其在水电能源中的应用[M].湖北科学出版社,2007.
[180]张铭,丁毅,袁晓辉,李承军.梯级水电站水库群联合发电优化调度[J].华中科技大学学报(自然科学版),2006,34(6)：90-92.
[181]Dorigo M. Maniezzo V, Colorni A. Positive feedback as a search strategy[R]. Technical Report 91-016, Dipartimento di Elettronica, Politecnico di Mialano, Italy,1991.
[182]Dorigo M. Optimization learning and natural algorithms[D]. Dipartimento di Elettronica, Politecnico di Mialano,1992.
[183]StUtzle T. Max-min ant system for the quadratic assignment problem[R]. Technical report AIDA-97-4, FG Intellecktik, FB Informatik, TU Darmstadt, Germany.
[184]Dorigo M, Gambardella L M. Ant colonies for the traveling salesman problem[J], BioSystems,1997,43(2):73-81.
[185]Kennedy J, Eberhart R. Particle swarm optimization[C]//Proceedings of IEEE Conference on Neural Networks. Perth, Australia:IEEE,1995:1942-1948.
[186]Han K-H, Kim J-H. Quantum-inspired evolutionary algorithm for a class of combinatorial optimization[J]. IEEE Transactions on Evolutionary Computation. 2002,6:580-93.
[187]Han K-H, Kim J-H. Quantum-inspired evolutionary algorithms with a new termination criterion, H gate, and two-phase scheme[J]. IEEE Transactions on Evolutionary Computation.2004,8:156-69.
[188]陈森林.电力电量平衡算法及其应用研究[J].水力发电.2004(2)：8-10.
[189]刘芳,李阳阳.量子克隆进化算法[J].电子学报,2003,31(12A)：2066-2070.
[190]覃朝勇,郑建国,朱佳俊.一种实数编码量子进化算法及其收敛性[J].控制与决策,2009,24(6)：854-858.
[191]Storn R. Price K. Differential evoutiona-a simple and efficient adaptive scheme for global optimization over continuous spaces [J]. Technical Report, International Computer Science Institute,1995(8):22-25.
[192]Yongqiang Wang, Jianzhong Zhou, Li Mo, Rui Zhang, Yongchuan Zhang. A modified differential real-coded quantum-inspired evolutionary algorithm for continuous space optimization[J]. Journal of Computational Information Systems, 2012,8(4):1487-1495.
[193]P. Li, S. Li, Quantum-inspired evolutionary algorithm for continuous space optimization based on Bloch coordinates of qubits[J]. Neurocomputing,2008, 72(1-3):81-91.
[194]陈辉,张家树,张超.实数编码混沌量子遗传算法[J].控制与决策,2005,20(11)：1300-1303.
[195]Deb K. An efficient constraint handling method for genetic algorithms [J]. Computer Methods in Applied Mechanics and Engineering.2000;186(2-4):311-38.
[196]Ying W, Zhou J, Chao Z, Yongqiang W, Hui Q, Youlin L. An improved self-adaptive PSO technique for short-term hydrothermal scheduling[J]. Expert Systems with Applications.2012;39(3):2288-95.
[197]Nazari ME, Ardehali MM, Jafari S. Pumped-storage unit commitment with considerations for energy demand, economics, and environmental constraints [J]. Energy.2010;35(10):4092-101.
[198]Swain RK, Barisal AK, Hota PK, Chakrabarti R. Short-term hydrothermal scheduling using clonal selection algorithm[J]. International Journal of Electrical Power & Energy Systems 2011; 33(3):647-56.
[2]水利科学与海洋工程学科发展战略研究报告(2011-2015)[R].国家自然科学基金委员会工程与材料科学部,科学出版社有限责任公司,2011.
[3]霍丽文.2012电力工业形势春季报告之二电力结构调整篇.中国电力报,2012.
[4]Masse P., Les Reserves set et la Regulation de 1'Avenir[M], Edition Hermann.1946
[5]Little JDC. The use of storage water in a hydroelectric system[J]. Journal of the Operations Research Society of America,1955,3 (2):187-197.
[6]Tennessee Valley Authory, WRMM Staff. Testing of different optimization methods on a reservoir subsystem[R]. Report B-16, Knoxville, Tennessee.1977.
[7]James E. Giles, Gregory A. Hoff. Techniques for the developments of a multi-objective optimization model[C]. Proceeding ACM-SE 16 proceedings of the 16th annual southeast regional conference. New York. USA,1978
[8]George W. Tauxe, Robert R, Inman, Dean M. Mades. Multiobjective dynamic programing with application to a reservoir[J]. Water reseources research.1979, 15(6):1403-1408.
[9]Sidney Yakowitz. Dynamic programming applications in water resources[J]. Water resources research.1982,18(4):673-696.
[10]Tang J, Huang W V. A decomposition method for generation scheduling of hydro systems with delays and unpredictable changes in natural inflows[J]. Computers & Industrial Engineering.1992,22(2):147-155.
[11]Perez-Diaz J I, Wilhelmi J R, Arevalo L A. Optimal short-term operation schedule of a hydropower plant in a competitive electricity market[J]. Energy Conversion and Management.2010,51(12):2955-2966.
[12]Wolfgang O, Haugstad A, Mo B, et al. Hydro reservoir handling in Norway before and after deregulation [J]. Energy.2009,34(10):1642-1651.
[13]谭维炎,黄守信,刘健民.初期运行水电站的最优年运行计划——动态规划方法的应用[J].水利水电技术.1963(6)：22-26.
[14]张勇传,李福生,熊斯毅,等.水电站水库群优化调度方法的研究[J].水力发电.1981(4)：48-52.
[15]张勇传,李福生,杜裕福,等.水电站水库调度最优化[J].华中工学院学报.1981,9(6)：49-56.
[16]张勇传,李福生,熊斯毅,等.变向探索法及其在水库优化调度中的应用[J].水力发电学报.1982(2)：1-10.
[17]张勇传,李福生,邴凤山,等.水库优化问题中的经济性与可靠性[J].水电能源科学.1983,1(1)：43-50.
[18]张勇传.优化理论在水库调度中的应用[M].长沙：湖南科学技术出版社,1985.
[19]叶秉如.多目标规划的理论、方法及在水资源规划中的应用[J].河海大学科技情报.1990,10(3)：6-18.
[20]叶秉如,余里红.多目标二次规划非劣解集的理论生成及应用[J].水电能源科学.1991,9(2)：102-110.
[21]董增川,叶秉如.水电站库群优化调度的分解方法[J].河海大学学报.1990,18(6)：70-78.
[22]胡振鹏,冯尚友.大系统多目标递阶分析的“分解-聚合”方法[J].系统工程学报.1988(1)：56-64.
[23]黄强,王世定,颜竹丘.应用大系统递阶控制理论解梯级水电站水库群长期最优调度[J].水电能源科学.1993(2)：80-87.
[24]梅亚东.梯级水库优化调度的有后效性动态规划模型及应用[J].水科学进展.2000(2)：194-198.
[25]马光文.大系统随机控制理论在水库群优化调度中的应用[J].系统工程学报. 1991(2)：46-58.
[26]陈洋波,王先甲,冯尚友.考虑发电量与保证出力的水库调度多目标优化方法[J].系统工程理论与实践.1998(4)：96-102.
[27]覃晖,周建中,肖舸,等.梯级水电站多目标发电优化调度[J].水科学进展.2010(3)：377-384.
[28]刘维烈主编.电力系统调频与自动发电控制[M].中国电力出版社,2005：3-8.
[29]杨建东,赵琨,李玲,等.浅析俄罗斯萨扬-舒申斯克水电站7号和9号机组事故原因[J].水力发电学报.2011(4)：226-234.
[30]王桂平.萨扬水电站“8·17”事故及对中国水电站安全运行警示[J].水电厂自动化.2011(3)：51-54.
[31]方辉钦.BBC公司的水力发电厂监控系统[J].电力系统自动化.1982(6)：52-55.
[32]方辉钦.葛洲坝试点与三峡水电厂自动化[J].电力系统自动化.1993(8)：91.
[33]方辉钦.试论无人值班水电厂的设计及技术要求[J].电力系统自动化.1996(10)：1-3.
[34]伍永刚,王定一,魏守平.水电站AGC中负荷调节策略的研究[J].华中理工大学学报.2000(2)：56-57.
[35]王健.大中型水电站参加电网AGC的安全防护措施[J].水电自动化与大坝监测.2004(1)：64-66.
[36]蒋建文,江红军,牟奎.紫坪铺水电站AGC的设计与实现[J].水力发电.2007(2)：73-74.
[37]曾火琼.浅析水电厂AGC与一次调频的配合[J].水电自动化与大坝监测.2008(1)：40-42.
[38]李国怀,王忠强,张再虎,等.小浪底电厂并网实时调度系统更新改造[J].水电能源科学.2011(10)：141-143.
[39]程抱贵,王鹏宇.龙滩水电站一次调频与AGC二次调频间的策略优化[J].水电站机电技术.2011(5)：52-55.
[40]龚传利,黄家志,潘苗苗.三峡右岸电站AGC功能设计及实现方法[J].水电站机电技术.2008(3)：26-28.
[41]方辉钦.现代水电厂计算机监控技术与试验[M].中国电力出版社,2004：124-143.
[42]于尔铿,周京阳.能量管理系统(EMS)第7讲发电计划(2)—水电计划和交换计划[J].电力系统自动化.1997(7)：83-85.
[43]王雁凌,张粒子,鲍海,等.用优化排序法进行日发电计划的计算[J].电力系统及其自动化学报.2000(5)：32-36.
[44]蔡建章,蔡保锐,蔡华祥,等.过渡期电力市场条件下日发电计划编制研究[J].云南电力技术.2003(3)：4-6.
[45]杨俊杰,周建中,刘大鹏.电力系统日发电计划的启发式遗传算法[J].水力发电.2004(1)：7-11.
[46]蒋东荣,刘学军,李群湛.电力市场环境下电网日发电计划的电量经济分配策略[J].中国电机工程学报.2004(7)：94-98.
[47]黄春雷,赵永龙,过夏明.基于日典型负荷的水电站群日计划方式[J].水电自动化与大坝监测.2005(4)：45-47.
[48]黄春雷.基于径流随机特性的水电站逐日电量计划制定方法[J].水电自动化与大坝监测.2006(6)：8-11.
[49]梁志飞,夏清.精细化日发电计划模型与方法[J].电力系统自动化.2008(17)：26-29.
[50]姚跃庭,鲍正风,李鹏,等.葛洲坝水电站日发电计划制作方法探讨[Z].中国甘肃敦煌：2008,96-103.
[51]蔡治国,张艾东,张娟.葛洲坝电站非汛期日优化发电计划编制方法初步研究[J].应用基础与工程科学学报.2010(3)：419-427.
[52]文庭秋.水电站厂内经济运行实时控制系统[J].计算技术与自动化.1984(1)：21-29.
[53]肖翘云,莫秀英.西津水电站开展水库优化调度和厂内经济运行效果显著[J].水力发电.1986(8)：40-42.
[54]梅亚东,左园忠,朱教新.一类含有0-1变量的厂内经济运行模型及解法[J].水电能源科学.1999(3)：20-21.
[55]马跃先.小型水电站厂内经济运行新模式[J].小水电.1999(6)：9-10.
[56]路志宏,魏守平,罗元胜.基于开关控制策略的厂内经济运行模型及应用[J].水电自动化与大坝监测.2003(1)：11-13.
[57]徐晨光,黄强,赵麦换.中小型水电站厂内经济运行准实时系统的设计与实现[J].水力发电学报.2003(1)：15-20.
[58]张祖鹏,陈森林.葛洲坝水电站厂内经济运行二层模型研究[J].水电能源科学.2010(4)：124-126.
[59]权先璋.动态规化原理在水电站厂内经济运行中的应用[J].水电能源科学.1983(1)：95-104.
[60]Chang S C, Chen C H, Fong I K, et al. Hydroelectric generation scheduling with an effective differential dynamic programming algorithm [J]. IEEE Transactions on Power Systems.1990,5(3):737-743.
[61]姚齐国,张士军,蒋传文,等.动态规划法在水电站厂内经济运行中的应用[J].水电能源科学.1999(1)：46-49.
[62]刘胡,高仕春,万俊,等.东江水电站厂内经济运行动态规划算法[J].水电能源科学.2000(4)：14-15.
[63]程春田,唐子田,李刚,等.动态规划和粒子群算法在水电站厂内经济运行中的应用比较研究[J].水力发电学报.2008(6)：27-31.
[64]张勇传.水电站经济运行原理[M].中国水利水电出版社,1998.
[65]田峰巍,颜竹丘,刘恩锡.大系统优化理论在水电站厂内经济运行中的应用[J].水力发电学报.1988(2)：10-20.
[66]万俊.厂内经济运行的POA算法研究[J].武汉水利电力学院学报.1992(4)： 370-375.
[67]田峰巍,黄强,刘恩锡.非线性规划在水电站厂内经济运行中的应用[J].西安理工大学学报.1987(3)：68-73.
[68]姜铁兵,游大海,康玲,等.水电站厂内经济运行基因遗传算法模型[J].华中科技大学学报.1995(7)：78-81.
[69]王黎,马光文.基于遗传算法的水电站厂内经济运行新算法[J].中国电机工程学报.1998(1)：65-67.
[70]袁晓辉,张双全,王金文,等.拟梯度遗传算法在水电厂厂内经济运行中的应用研究[J].电网技术.2000(12)：66-69.
[71]徐晨光,赵麦换,黄强.基于自组织进化规划的径流式水电站厂内经济运行算法[J].水利水电技术.2005(3)：55-57.
[72]李崇浩,纪昌明,李文武.微粒群算法在水电站厂内经济运行中的应用研究[J].水利水电技术.2006(1)：88-91.
[73]申建建,程春田,张俊,等.蜜蜂进化算法在水电站厂内经济运行中的应用[J].水电能源科学.2008(3)：137-140.
[74]李刚,程春田,唐子田,等.结合禁忌搜索思想的粒子群算法在乌江渡水电站厂内经济运行中的应用研究[J].水力发电学报.2009(2)：128-132.
[75]赵雪花,黄强,吴建华.蚁群算法在水电站厂内经济运行中的应用[J].水力发电学报.2009(2)：139-142.
[76]杨鸿锋,张志刚,黄伟军.差分进化算法及其在水电站厂内经济运行中的应用[J].中国农村水利水电.2009(7)：113-115.
[77]张智晟,龚文杰,段晓燕,等.类电磁机制算法在水电站厂内经济运行中的应用研究[J].电工电能新技术.2011(4)：17-20.
[78]王定一等.水电厂计算机监视与控制[M].中国电力出版社,2001.
[79]蒋传文,权先璋,张勇传.水电站厂内经济运行中的一种混沌优化算法[J].华中理工大学学报.1999(12)：39-40.
[80]王永强,周建中,覃晖,等.基于改进二进制粒子群与动态微增率逐次逼近法混合优化算法的水电站机组组合优化[J].电力系统保护与控制.2011(10)：64-69.
[81]刘建华,段虞荣.网络规划法在梯级水电站短期优化调度中的应用[J].重庆大学学报(自然科学版).1991(1)：25-32.
[82]尚金成,张勇传,岳子忠,等.梯级电站短期优化运行的新模型及其最优性条件[J].水电能源科学.1998(3)：2-10.
[83]陈森林,万俊,刘子龙,等.水电系统短期优化调度的一般性准则(1)——基本概念与数学模型[J].武汉水利电力大学学报.1999(3)：35-38.
[84]陈森林,万俊,刘子龙,等.水电系统短期优化调度的一般性准则(2)——优化模型求解方法及实例应用[J].武汉水利电力大学学报.1999(3)：39-43.
[85]梅亚东,朱教新.黄河上游梯级水电站短期优化调度模型及迭代解法[J].水力发电学报.2000(2)：1-7.
[86]梅亚东,朱教新.梯级水电站短期优化调度软件开发及应用介绍[J].人民珠江.2000(1)：42-44.
[87]王仁权,王金文,伍永刚.福建梯级水电站群短期优化调度模型及其算法[J].云南水力发电.2002(1)：52-53.
[88]吴迎新,王金文,伍永刚.福建水电站群短期发电量最大优化调度模型与算法研究[J].电力系统及其自动化学报.2002(6)：35-39.
[89]程春田,武新宇,申建建,等.大规模水电站群短期优化调度方法Ⅰ：总体概述[J].水利学报.2011(9)：1017-1024.
[90]程春田,申建建,武新宇,等.大规模水电站群短期优化调度方法Ⅳ：应用软件系统[J].水利学报.2012(2)：160-167.
[91]申建建,武新宇,程春田,等.大规模水电站群短期优化调度方法Ⅱ：高水头多振动区问题[J].水利学报.2011(10)：1168-1176.
[92]武新宇,程春田,申建建,等.大规模水电站群短期优化调度方法Ⅲ：多电网调 峰问题[J].水利学报.2012(1)：31-42.
[93]Dorman J, Ewing M. Numerical inversion of seismic surface wave dispersion data and crust-mantle structure in the New York-Pennsylvania area[J]. Journal of Geophysical Research.1962,67:5227-5241.
[94]Windsor JS. Optimization model for reservoir flood control[J]. Water Resources Research.1973,9(5):1103-1114.
[95]伍宏中.水电站群补偿径流调节的线性规划模型及其应用[J].水力发电学报.1998(1)：11-23.
[96]王金文,刘双全.短期水电系统发电调度的优化方法综述[J].水电能源科学.2008(6)：137-143.
[97]Lidgate, D., Amir, B. H. Optimal operational planning for a hydro-electric generation system [C]. IEE Proceedings C:Generation Transmission and Distribution.1988,135(3):169-181.
[98]Wang, J., Yuan, X., Zhang, Y. Short-term scheduling of large-scale hydropower systems for energy maximization [J]. Journal of Water Resources Planning and Management.2004,130(3):198-205.
[99]Mariano SJPS., Catalao JPS., Mendes VMF, et al. Head-dependent maximum power generation in short-term hydro scheduling using nonlinear programming [C]. In:Proceedings of the IASTED International Conference on Energy and Power Systems,2007:247-252.
[100]覃晖.流域梯级电站群多目标联合优化调度与多属性风险决策[D].博士论文.华中科技大学,2011.
[101]Tang J, Huang WV. A decomposition method for generation scheduling of hydro systems with delays and unpredictable changes in natural inflows[J]. Computers & Industrial Engineering.1992,22(2):147-155.
[102]万俊,陈惠源.水电站群优化调度分解协调-聚合分解模型研究[J].水力发电学报,1996,(2)：41-50.
[103]李爱玲.水电站水库群系统优化调度的大系统分解协调方法研究[J].水电能源科学.1997(4)：59-64.
[104]李亮,黄强,肖燕,等DPSA和大系统分解协调在梯级水电站短期优化调度中的应用研究[J].西北农林科技大学学报(自然科学版).2005(10)：125-128.
[105]高仕春,万飚,梅亚东,等.三峡梯级和清江梯级水电站群联合调度研究[J].水利学报.2006(4)：504-507.
[106]Li C, Hus E, Svoboda AJ, et al. Hydro unit commitment in hydrothermal optimization[J]. IEEE Transactions on Power Systems.1997,12(2):764-769.
[107]Nilsson O, Sjelvgren D. Mixed-integer programming applied to short-term planning of a hydrothermal system[J]. IEEE Transactions on power systems,1996, 11(1):281-286.
[108]Nillson O, Sjelvgren D. Variable splitting applied to modeling of start-up costs in short term hydrothermal generation scheduling [J]. IEEE Transactions on Power Systems,1997,12(2):770-775.
[109]Salam MS, Nor KM, Hamdan AR. Comprehensive algorithm for hydrothermal coordination[J]. IEEE proceding-Generation Transmission and Distribution,1997, 144(5):482-488.
[110]严婧,孙帆,岳超源.基于拉格朗日松弛法的梯级水电优化调度系统[J].自动化技术与应用.2007(7)：13-15.
[111]李钰心.水电站经济运行[M].中国电力出版社,1999.
[112]刘胡,高仕春,万俊,等.东江水电站厂内经济运行动态规划算法[J].水电能源科学.2000(4)：14-15.
[113]邓晓娟,权先璋,张勇传.三峡梯级水电站厂内经济运行[J].东北电力技术.2002(4)：1-4.
[114]Larson RE, Kecklerv WG. Applications of dynamic programming to control of water resource systems [J]. Int. Fed. Automat. Control,1996,5(1):15-26.
[115]董增川,许静仪.水电站库群优化调度的多次动态线性规划方法[J].河海大学学报,1990,18(6)：63-69.
[116]梅亚东.梯级水库优化调度的有后效性动态规划模型及应用[J].水科学进展.2000(2)：194-198.
[117]Chow VT, Madiment DR., Tauxe GW. Computer time and memory requirements for DP and DDDP in water resources systems analysis [J]. Water Resource Research,1975,11(5):621-682.
[118]宗航,李承军,周建中,等.POA算法在梯级水电站短期优化调度中的应用[J].水电能源科学.2003(1)：46-48.
[119]邹进,张勇传.模糊动态规划在三峡梯级电站短期优化调度中的应用[J].水利水电技术.2004(8)：89-93.
[120]Huang S. Hydroelectric generation scheduling—an application of genetic-embedded fuzzy system approach[J]. Electric Power Systems Research. 1998,48(1):65-72.
[121]Wardlaw R, Sharif M. Evaluation of genetic algorithm for optimal reservoir system operatipon[J]. Journal of Water Resources Planning and Management,1999, 125(1):25-33.
[122]王德意,罗兴锜,张宇峰,等.改进遗传算法在水电站AGC中的应用研究[J].水力发电学报.2004(6)：35-39.
[123]杨俊杰,周建中,喻菁,等.基于电力系统日发电计划的混合智能messy遗传算法[J].电力系统自动化.2004(15)：30-33.
[124]Yuan X, Yuan Y, Zhang Y. A hybrid chaotic genetic algorithm for short-term hydro system scheduling[J]. Mathematics and Computers in Simulation.2002, 59(4):319-327.
[125]孙昌佑,马震岳.基于遗传算法的水电站厂内经济运行模型研究[J].水电能源科学.2004(1)：48-50.
[126]武学毅,陈守伦,郭倩.基于FP遣传算法的梯级水库短期优化调度[J].水力 发电学报.2010(1)：72-75.
[127]袁晓辉,王乘,张勇传,等.粒子群优化算法在电力系统中的应用[J].电网技术.2004(19)：55-57.
[128]Chuanwen J, Bompard E. A self-adaptive chaotic particle swarm algorithm for short term hydroelectric system scheduling in deregulated environment [J]. Energy Conversion and Management.2005,46(17):2689-2696.
[129]杨道辉,马光文,严秉忠,等.粒子群算法在水电站日优化调度中的应用[J].水力发电.2006(3)：73-75.
[130]李崇浩,纪昌明,缪益平.基于微粒群算法的梯级水电厂短期优化调度研究[J].水力发电学报.2006(2)：55-57.
[131]Nagesh Kumar D., Janga Reddy M. Multipurpose Reservoir Operation Using Particle Swarm Optimization[J]. Journal of Water Resources Planning and Management,2007,133(3):192-201.
[132]程春田,唐子田,李刚,等.动态规划和粒子群算法在水电站厂内经济运行中的应用比较研究[J].水力发电学报.2008(6)：24-28.
[133]徐刚,马光文,梁武湖,等.蚁群算法在水库优化调度中的应用[J].水科学进展,2005,16(3)：397-400.
[134]徐刚,马光文.基于蚁群算法的梯级水电站群优化调度[J].水力发电学报,2005,24(5)：7-10.
[135]赵雪花,黄强,吴建华.蚁群算法在水电站厂内经济运行中的应用[J].水力发电学报.2009(2)：139-142.
[136]王永强,周建中,肖文,等.多种群蚁群优化算法在大型水电站自动发电控制机组优化组合中的应用[J].电网技术.2011(9)：66-70.
[137]Yuan X, Zhang Y, Wang L, et al. An enhanced differential evolution algorithm for daily optimal hydro generation scheduling [J]. Computers & amp; Mathematics with Applications.2008,55(11):2458-2468.
[138]黄强,张洪波,原文林,等.基于模拟差分演化算法的梯级水库优化调度图研究[J].水力发电学报.2008(6)：13-17.
[139]覃晖,周建中,肖舸,等.梯级水电站多目标发电优化调度[J].水科学进展.2010(3)：377-384.
[140]Liang RH. A noise annealing neural network for hydroelectric generation scheduling with pumped units[J]. IEEE Transactions on Power Systems,2000, 15(3):1008-1013.
[141]韦柳涛,梁年生,虞锦江.神经网络理论在梯级水电厂短期优化调度中的应用[J].水电能源科学.1992(3).
[142]胡铁松,万永华,冯尚友.水库群优化调度函数的人工神经网络方法研究[J].水科学进展.1995(1)：53-60.
[143]邱林,田景环,段春青,等.混沌优化算法在水库优化调度中的应用[J].中国农村水利水电.2005(7)：17-18.
[144]左幸,马光文,徐刚,等.人工免疫系统在梯级水库群短期优化调度中的应用[J].水科学进展.2007(2)：277-281.
[145]Sherkat VR, Moslehi K, Lo EO, et al. Modular and flexible software for medium and short-term hydrothermal scheduling[J]. IEEE Transactions on Power Systems. 1988,3(3):1390-6.
[146]Ferreira LAFM, Andersson T, Imparato CF, Miller TE, Pang CK, Svoboda A, et al. Short-term resource scheduling in multi-area hydrothermal power sy stems [J]. International Journal of Electrical Power & Energy Systems.1989,11(3):200-12.
[147]Contaxis GC, Kavatza SD. Hydrothermal scheduling of a multireservoir power system with stochastic inflows. IEEE Transactions on Power Systems[J].1990, 5(3):766-73.
[148]Rivera JF, Galdeano CA, Vargas A. Some numerical criteria to measure the validity of hydro-aggregation in hydrothermal systems[J]. International Journal of Electrical Power & Energy Systems.1990,12(l):17-24.
[149]Gonzalez C, Juan J. Reliability evaluation for hydrothermal generating systems: Application to the Spanish case[J], Reliability Engineering & System Safety.1999, 64(1):89-97.
[150]Oliveira GC, Binato S, Pereira MVF. Value-based transmission expansion planning of hydrothermal systems under uncertainty [J]. IEEE Transactions on Power Systems.2007,22(4):1429-35.
[151]陈雪青,陈刚,王世缨,等.水火联合电力系统的优化调度[J].清华大学学报(自然科学版).1985(2)：15-26.
[152]文福栓,韩祯祥.水火电力系统的经济调度——Hopfield连续模型的应用[J].电力系统及其自动化学报.1991(1)：60-67.
[153]袁智强,侯志俭,蒋传文,等.水火电系统古诺模型的均衡分析[J].电力系统自动化.2004(4)：17-21.
[154]韩冬,蔡兴国.综合环境保护及峰谷电价的水火电短期优化调度[J].电网技术.2009(14)：78-83.
[155]覃晖,周建中.基于多目标文化差分进化算法的水火电力系统优化调度[J].电力系统保护与控制.2011(22)：90-97.
[156]卢有麟,周建中,王永强,等.水火电力系统多目标环境经济调度模型及其求解算法研究[J].电力系统保护与控制.2011(23)：93-100.
[157]Kumar S, Sharma J, Ray LM. A nonlinear programming algorithm for hydro-thermal generation scheduling[J]. Computers & Electrical Engineering.1979, 6(3):221-229.
[158]Wang C, Shahidehpour SM. Power generation scheduling multi-area hydro-thermal systems with tie line constraints, cascaded reservoirs and uncertain data[J]. IEEE Transtactions on Power System.1993,5(3):737-743.
[159]Liang R-H, Hsu Y-Y. A hybrid artificial neural network—differential dynamic programming approach for short-term hydro scheduling[J]. Electric Power Systems Research.1995,33(2):77-86.
[160]Salam MS, Nor KM, Hamdam AR. Hydrothermal scheduling based Lagrangian relaxation approach to hydrothermal coordination[J]. IEEE Transactions on Power Systems.1998,13(1):226-235.
[161]Heredia FJ, Nabona N. Optimum short-term hydrothermal scheduling with spinning reserve through network flows[J]. IEEE Transactions on Power Systems, 1995,10(3):1642-1651.
[162]Soares S, Lyra C, Tavares H. Optimal generation scheduling of hydrothermal power systems[J]. IEEE Transactions on Power Systems,1980, PAS-99(3).
[163]Ngundam JM, Kenfack F, Tatietse TT. Optimal scheduling of large-scale hydrothermal power systems using the Lagrangian relaxation technique [J]. International Journal of Electrical Power & Energy Systems.2000,22(4):237-245.
[164]李朝安,杨锐.水库末水位不固定时水火电力系统最优周运行方式的研究和程序开发[J].电力系统自动化.1988(4)：20-25.
[165]朱继忠,徐国禹.用网流法求解水火电力系统有功负荷分配[J].系统工程理论与实践.1995(1)：69-73.
[166]韩学山,吴忠明,宋家骅,等.定火电机组组合方式下水火电系统动态优化调度[J].东北电力学院学报.1997(3)：16-23.
[167]Esteban Gil, Julian Bustos, Hugh Rudnick. Short-term hydrothermal generation scheduling model using a genetic algorithm[J]. IEEE Transactions on Power Systems,2003,18(4):1256-1264.
[168]Basu M. A simulated annealing-based goal-attainment method for economic emission load dispatch of fixed head hydrothermal power systems[J]. International Journal of Electrical Power & Energy Systems.2005,27(2):147-153.
[169]Binghui Yu, Xiaohui Yuan, Jinwen Wang. Short-term hydro-thermal scheduling using particle swarm optimization method[J]. Energy Conversion and Management. 2007,48(7):1902-1908.
[170]Lakshminarasimman L, Subramanian S. Short-term scheduling of hydrothermal power system with cascaded reservoirs by using modified differential evolution. Generation[J], IEE Proceedings-Transmission and Distribution.2006,153(6): 693-700.
[171]Zoumas CE, Bakirtzis AG, Theocharis JB, Petridis V. A genetic algorithm solution approach to the hydrothermal coordination problem [J]. IEEE Transactions on Power Systems.2004,19(3):1356-1364.
[172]马光文,王黎.水火电力系统优化调度的逐步最优遗传算法(英文)[J].水电能源科学.2000(2)：69-72.
[173]袁晓辉,袁艳斌,张勇传.用改进遗传算法求解水火电力系统的有功负荷分配[J].电力系统自动化.2002(23)：33-36.
[174]Youlin Lu, Jianzhong Zhou, Hui Qin, et al. An adaptive chaotic differential evolution for the short-term hydrothermal generation scheduling problem[J]. Energy Conversion and Management.2010,51(7):1481-1490.
[175]李刚,程春田,蔡建章,等.云南电网日调度计划编制系统[J].大连理工大学学报.2006(1)：111-115.
[176]张高峰.梯级水电系统短期优化调度与自动发电控制研究[D].博士论文.华中科技大学,2004.
[177]施冲,余杏林,吴正义,等.水电厂自动发电控制的工程设计与分析[J].电力系统自动化.2001(4)：57-59.
[178]龚传利,黄家志,潘苗苗.三峡右岸电站AVC功能设计及实现方法[J].水电自动化与大坝监测.2009(1)：19-21.
[179]张勇传.系统辨识及其在水电能源中的应用[M].湖北科学出版社,2007.
[180]张铭,丁毅,袁晓辉,李承军.梯级水电站水库群联合发电优化调度[J].华中科技大学学报(自然科学版),2006,34(6)：90-92.
[181]Dorigo M. Maniezzo V, Colorni A. Positive feedback as a search strategy[R]. Technical Report 91-016, Dipartimento di Elettronica, Politecnico di Mialano, Italy,1991.
[182]Dorigo M. Optimization learning and natural algorithms[D]. Dipartimento di Elettronica, Politecnico di Mialano,1992.
[183]StUtzle T. Max-min ant system for the quadratic assignment problem[R]. Technical report AIDA-97-4, FG Intellecktik, FB Informatik, TU Darmstadt, Germany.
[184]Dorigo M, Gambardella L M. Ant colonies for the traveling salesman problem[J], BioSystems,1997,43(2):73-81.
[185]Kennedy J, Eberhart R. Particle swarm optimization[C]//Proceedings of IEEE Conference on Neural Networks. Perth, Australia:IEEE,1995:1942-1948.
[186]Han K-H, Kim J-H. Quantum-inspired evolutionary algorithm for a class of combinatorial optimization[J]. IEEE Transactions on Evolutionary Computation. 2002,6:580-93.
[187]Han K-H, Kim J-H. Quantum-inspired evolutionary algorithms with a new termination criterion, H gate, and two-phase scheme[J]. IEEE Transactions on Evolutionary Computation.2004,8:156-69.
[188]陈森林.电力电量平衡算法及其应用研究[J].水力发电.2004(2)：8-10.
[189]刘芳,李阳阳.量子克隆进化算法[J].电子学报,2003,31(12A)：2066-2070.
[190]覃朝勇,郑建国,朱佳俊.一种实数编码量子进化算法及其收敛性[J].控制与决策,2009,24(6)：854-858.
[191]Storn R. Price K. Differential evoutiona-a simple and efficient adaptive scheme for global optimization over continuous spaces [J]. Technical Report, International Computer Science Institute,1995(8):22-25.
[192]Yongqiang Wang, Jianzhong Zhou, Li Mo, Rui Zhang, Yongchuan Zhang. A modified differential real-coded quantum-inspired evolutionary algorithm for continuous space optimization[J]. Journal of Computational Information Systems, 2012,8(4):1487-1495.
[193]P. Li, S. Li, Quantum-inspired evolutionary algorithm for continuous space optimization based on Bloch coordinates of qubits[J]. Neurocomputing,2008, 72(1-3):81-91.
[194]陈辉,张家树,张超.实数编码混沌量子遗传算法[J].控制与决策,2005,20(11)：1300-1303.
[195]Deb K. An efficient constraint handling method for genetic algorithms [J]. Computer Methods in Applied Mechanics and Engineering.2000;186(2-4):311-38.
[196]Ying W, Zhou J, Chao Z, Yongqiang W, Hui Q, Youlin L. An improved self-adaptive PSO technique for short-term hydrothermal scheduling[J]. Expert Systems with Applications.2012;39(3):2288-95.
[197]Nazari ME, Ardehali MM, Jafari S. Pumped-storage unit commitment with considerations for energy demand, economics, and environmental constraints [J]. Energy.2010;35(10):4092-101.
[198]Swain RK, Barisal AK, Hota PK, Chakrabarti R. Short-term hydrothermal scheduling using clonal selection algorithm[J]. International Journal of Electrical Power & Energy Systems 2011; 33(3):647-56.