梯级水电系统发电优化调度研究及应用
|
摘要
随着我国电力工业的解制,水电系统从传统的水火电力系统中解放出来,调度目标和约束与传统的水电调度相比发生了很大变化。发电侧调度更多地将注意力投向了提高自身的利益同时兼顾电网的安全、稳定与经济运行。流域梯级电站的逐级开发使得我国的水电规模越来越大,也给水电调度带来了新的挑战,实行流域梯级电站的统一调度,实现梯级的最优开发,提高电网运行效率和效益显得尤为必要。
梯级水电系统由于存在着复杂的水力、电力联系以及受径流不确定性的影响,其优化调度是一个极其复杂的优化问题,依靠调度员的经验来制定发电计划的传统调度方法已无法适应大规模水电系统的联合调度。本文结合“湖南沅水流域梯级电站优化调度系统开发工程”对梯级水电系统发电优化调度的模型和算法进行了研究,取得了一些有意义的研究成果,主要体现在以下几个方面:
(1)首先引出本文的研究领域,回顾国内外水电优化调度领域的现状和研究进展,并对优化算法在水电系统优化调度中的应用进行了综述,在此基础上确立本文研究的重点。
(2)考虑径流的不确定性,将自然径流看作受预报影响的白噪声随机变量,通过延长控制期来消除控制期末水库蓄能对水库运行方式的影响。当控制期足够长时,用于优化计算的约束条件和径流概率分布在各年份中趋于相同,基于此提出了当控制期足够长时水库的最优水位过程会出现周期循环的假设,建立了由年循环模型和实时调度模型组成的随机优化调度的数学模型。年循环模型只被离线求解一次,其结果用作实时调度的边界条件;建立了实时调度模型,其调度期包括了受实时信息影响的过渡期。结合实时信息的影响,水库从每时段初的实际水位出发,保证实时调度期末收敛于年循环最优水位,选取实时调度期第一时段的优化结果来指导水库的实际运行,通过各时段的滚动更新来获得水库运行方式,并给出了模型求解流程。
(3)采用分级优化控制的思想,建立了考虑因素较全面的梯级水电系统短期发电优化调度模型。除了考虑常用的水量和电力平衡、库容和流量的上下限以外,还考虑了抽水蓄能电站的扩展,电站的非线性出力特性,水库出库流量变幅,电站出力变幅,电站运行区域的限制以及电站开停机次数和开停机持续时间的限制。对于非线性约束条件和目标函数采用一阶Taylor展开进行线性化处理,基于P分原理通过分解系统耦合出力约束将问题分解到各个梯级,对带上限的约束进行简化以减少约束矩阵的变量个数,求解过程中对人工变量不为零的情况按照预先定义的约束条件破坏优先顺序对不能满足的约束条件进行逐级破坏,确保优化计算总能得到一个有效解。在水库出库和电站出力确定的基础上,考虑电站开停机次数和持续时间约束,采用动态规划求得电站的运行区域计划。并分别给出了混合分解算法求解水电系统调度和动态规划求解电站运行区域计划的详细流程。
(4)研究了长、短期优化调度的协调机制,由长期调度结果经线性插值得到短期调度的边界条件后从水库当前实时状态出发,对长期和短期调度进行滚动计算,实现整个梯级最优运行。
最后通过对沅水流域梯级电站群联合优化调度的仿真,验证了本文提出的优化算法和建立的模型符合工程实际,在求解梯级水电系统发电优化调度问题上是可行且有效的。
With the deregulation of China electric power industry, hydropower system breaks away from the conventional hydrothermal power system, which leads to significant changes of the operation objective and constraints compared to the traditional hydrothermal scheduling. Power companies pay more attention to their own generation interests while meeting the security, stability and economic operation demand of power grid. The progressive development of cascade hydropower stations makes ever larger scale hydropower systems in China, brings about new challenges for hydropower scheduling, and necessitates the unified scheduling to achieve the optimization development of cascade hydropower systems, and to improve the efficiency and benefits of power grid.
Optimal scheduling of cascade hydropower systems is a complicated optimization problem affected by the hydraulic and electric coupling and the uncertainties of natural inflow, the conventional operation method based on dispatcher's experience has no longer been able to fulfill the requirements of co-scheduling of the ever larger scale hydropower systems. Based on the project "Hydropower Optimal Scheduling System Development for Cascade Hydroplants on Yuan River Basin", the optimization model and solution algorithm for solving cascade hydropower system generation scheduling are studied, the research work mainly includes the following aspects:
(1) First of all, the research field is introduced by reviewing the domestic and international state-of-the-art and advances in hydro scheduling optimization, the comments on optimization algorithms' applications to this field are summarized, then the research focus is established.
(2) Accounting for uncertainty, the natural inflow is described as a stochastic forecast-dependent white noise process. With the influence of the errors in estimating the end-of-study water value diminished by extending the study horizon long enough, the randomness of the standardized natural inflow reaches a stable status, and the constraints and probability distribution of inflow during each time interval tends to be the same every year, the assumption that the optimal reservoir storage trajectory will repeat annually when the study horizon is long enough is proposed. A stochastic model consists of two sub models, an annually cycling model and a real-time operation model, is established for the long-term operation optimization of hydropower system. The optimal annually cycling trajectories that serve as the boundary limits on the real-time operation model are derived for offline use by solving the annually cycling model only once. A real-time period (RTP) that covers a transition period (TP) influenced by real-time information is structured. The real-time operation model is to determine the reservoir storage trajectories, transferring from the observed water level at the beginning of a RTP to the annually cycling trajectories at the end of the RTP. Only the optimal storage derived at the end of the first time interval is used as the target to operate the reservoir, then the operation scheme is obtained to guide the reservoir operation by rolling computation. And the solution method to the model is also presented.
(3) Inspired by the hierarchical optimization control mechanism, a comprehensive model for short-term generation scheduling of hydropower system is established. The involved constraints include the water mass conservation, the power balance, limits on reservoir storage and release, expansion of pumped-storage hydroplant, nonlinear plant-based power generation characteristics, limits on plant-based power and ramp, limits on plant-based discharge, limits on reservoir release ramp, limits on plant-based operational region, limits on plant-based start-up and shutdown frequency and duration. The nonlinear objective and constraints are successively approximated by first order Taylor series expansion, and p-decomposition-based algorithm is used to decompose the original problem into several sub ones by decomposing the coupling power balance, constraints with upper bounds are simplified so as to decrease the variable number in the coefficient matrix, a constraint violation priority is defined to deal with the situation that the artificial variables associated with the constraints are non-zero to ensure a feasible active solution is always derived. When the reservoir release and plant-based power are determined, the plant-based operation region schedule is obtained by solving a recursive Dynamic Programming process constrained by the requirements for the plant-based start-up and shutdown frequency and duration. The specific solution algorithm is described as well.
(4) The coordination between long-term and short-term operation is studied. The storage boundary limits of the short-term optimal scheduling are derived from the long-term scheduling results by linear interpolation, then transferring from the observed water level, the coordination is implemented by rolling computation to attain the optimization operation of the cascade hydropower system.
Finally, with application to the optimal co-scheduling of cascade hydroplants on Yuan River basin in Hunan, the models and methods proposed in this dissertation are practically verified to be suitable for generation scheduling of cascade hydropower systems, and the numerical results demonstrate the feasibility and effectiveness of the presented optimization models and solution algorithms.
梯级水电系统由于存在着复杂的水力、电力联系以及受径流不确定性的影响,其优化调度是一个极其复杂的优化问题,依靠调度员的经验来制定发电计划的传统调度方法已无法适应大规模水电系统的联合调度。本文结合“湖南沅水流域梯级电站优化调度系统开发工程”对梯级水电系统发电优化调度的模型和算法进行了研究,取得了一些有意义的研究成果,主要体现在以下几个方面:
(1)首先引出本文的研究领域,回顾国内外水电优化调度领域的现状和研究进展,并对优化算法在水电系统优化调度中的应用进行了综述,在此基础上确立本文研究的重点。
(2)考虑径流的不确定性,将自然径流看作受预报影响的白噪声随机变量,通过延长控制期来消除控制期末水库蓄能对水库运行方式的影响。当控制期足够长时,用于优化计算的约束条件和径流概率分布在各年份中趋于相同,基于此提出了当控制期足够长时水库的最优水位过程会出现周期循环的假设,建立了由年循环模型和实时调度模型组成的随机优化调度的数学模型。年循环模型只被离线求解一次,其结果用作实时调度的边界条件;建立了实时调度模型,其调度期包括了受实时信息影响的过渡期。结合实时信息的影响,水库从每时段初的实际水位出发,保证实时调度期末收敛于年循环最优水位,选取实时调度期第一时段的优化结果来指导水库的实际运行,通过各时段的滚动更新来获得水库运行方式,并给出了模型求解流程。
(3)采用分级优化控制的思想,建立了考虑因素较全面的梯级水电系统短期发电优化调度模型。除了考虑常用的水量和电力平衡、库容和流量的上下限以外,还考虑了抽水蓄能电站的扩展,电站的非线性出力特性,水库出库流量变幅,电站出力变幅,电站运行区域的限制以及电站开停机次数和开停机持续时间的限制。对于非线性约束条件和目标函数采用一阶Taylor展开进行线性化处理,基于P分原理通过分解系统耦合出力约束将问题分解到各个梯级,对带上限的约束进行简化以减少约束矩阵的变量个数,求解过程中对人工变量不为零的情况按照预先定义的约束条件破坏优先顺序对不能满足的约束条件进行逐级破坏,确保优化计算总能得到一个有效解。在水库出库和电站出力确定的基础上,考虑电站开停机次数和持续时间约束,采用动态规划求得电站的运行区域计划。并分别给出了混合分解算法求解水电系统调度和动态规划求解电站运行区域计划的详细流程。
(4)研究了长、短期优化调度的协调机制,由长期调度结果经线性插值得到短期调度的边界条件后从水库当前实时状态出发,对长期和短期调度进行滚动计算,实现整个梯级最优运行。
最后通过对沅水流域梯级电站群联合优化调度的仿真,验证了本文提出的优化算法和建立的模型符合工程实际,在求解梯级水电系统发电优化调度问题上是可行且有效的。
With the deregulation of China electric power industry, hydropower system breaks away from the conventional hydrothermal power system, which leads to significant changes of the operation objective and constraints compared to the traditional hydrothermal scheduling. Power companies pay more attention to their own generation interests while meeting the security, stability and economic operation demand of power grid. The progressive development of cascade hydropower stations makes ever larger scale hydropower systems in China, brings about new challenges for hydropower scheduling, and necessitates the unified scheduling to achieve the optimization development of cascade hydropower systems, and to improve the efficiency and benefits of power grid.
Optimal scheduling of cascade hydropower systems is a complicated optimization problem affected by the hydraulic and electric coupling and the uncertainties of natural inflow, the conventional operation method based on dispatcher's experience has no longer been able to fulfill the requirements of co-scheduling of the ever larger scale hydropower systems. Based on the project "Hydropower Optimal Scheduling System Development for Cascade Hydroplants on Yuan River Basin", the optimization model and solution algorithm for solving cascade hydropower system generation scheduling are studied, the research work mainly includes the following aspects:
(1) First of all, the research field is introduced by reviewing the domestic and international state-of-the-art and advances in hydro scheduling optimization, the comments on optimization algorithms' applications to this field are summarized, then the research focus is established.
(2) Accounting for uncertainty, the natural inflow is described as a stochastic forecast-dependent white noise process. With the influence of the errors in estimating the end-of-study water value diminished by extending the study horizon long enough, the randomness of the standardized natural inflow reaches a stable status, and the constraints and probability distribution of inflow during each time interval tends to be the same every year, the assumption that the optimal reservoir storage trajectory will repeat annually when the study horizon is long enough is proposed. A stochastic model consists of two sub models, an annually cycling model and a real-time operation model, is established for the long-term operation optimization of hydropower system. The optimal annually cycling trajectories that serve as the boundary limits on the real-time operation model are derived for offline use by solving the annually cycling model only once. A real-time period (RTP) that covers a transition period (TP) influenced by real-time information is structured. The real-time operation model is to determine the reservoir storage trajectories, transferring from the observed water level at the beginning of a RTP to the annually cycling trajectories at the end of the RTP. Only the optimal storage derived at the end of the first time interval is used as the target to operate the reservoir, then the operation scheme is obtained to guide the reservoir operation by rolling computation. And the solution method to the model is also presented.
(3) Inspired by the hierarchical optimization control mechanism, a comprehensive model for short-term generation scheduling of hydropower system is established. The involved constraints include the water mass conservation, the power balance, limits on reservoir storage and release, expansion of pumped-storage hydroplant, nonlinear plant-based power generation characteristics, limits on plant-based power and ramp, limits on plant-based discharge, limits on reservoir release ramp, limits on plant-based operational region, limits on plant-based start-up and shutdown frequency and duration. The nonlinear objective and constraints are successively approximated by first order Taylor series expansion, and p-decomposition-based algorithm is used to decompose the original problem into several sub ones by decomposing the coupling power balance, constraints with upper bounds are simplified so as to decrease the variable number in the coefficient matrix, a constraint violation priority is defined to deal with the situation that the artificial variables associated with the constraints are non-zero to ensure a feasible active solution is always derived. When the reservoir release and plant-based power are determined, the plant-based operation region schedule is obtained by solving a recursive Dynamic Programming process constrained by the requirements for the plant-based start-up and shutdown frequency and duration. The specific solution algorithm is described as well.
(4) The coordination between long-term and short-term operation is studied. The storage boundary limits of the short-term optimal scheduling are derived from the long-term scheduling results by linear interpolation, then transferring from the observed water level, the coordination is implemented by rolling computation to attain the optimization operation of the cascade hydropower system.
Finally, with application to the optimal co-scheduling of cascade hydroplants on Yuan River basin in Hunan, the models and methods proposed in this dissertation are practically verified to be suitable for generation scheduling of cascade hydropower systems, and the numerical results demonstrate the feasibility and effectiveness of the presented optimization models and solution algorithms.
引文
[1]张勇传．水电能优化管理．华中工学院出版社，1986．
[2]张勇传．水电系统最优控制．华中理工大学出版社，1993．
[3]张勇传．水电站经济运行原理．北京：中国水利水电出版社，1998．
[4]李钰心．水电站经济运行．北京：中国电力出版社，1999．
[5]Yeh, W. W.-G. Reservoir Management and Operations Model: A State-of-the-Art Review. Water Resources Research. 1985, 21(12): 1797-1818.
[6]Labadie, J. W. Optimal operation ofmultireservoir systems: State-of-the-art review. Journal of Water Resources Planning and Management. 2004, 130(2): 93-111.
[7]王金文．仿单纯形法及其在短期水电系统发电调度中的应用．[博士学位论文]．华中科技大学，2003．
[8]张高峰．梯级水电系统短期发电优化调度与自动发电控制．[博士学位论文]．华中科技大学，2004．
[9]余炳辉．粒子群优化算法试验研究及扩展研究．[博士学位论文]．华中科技大学，2007．
[10]裴哲义，孙芹芳．水电厂水情自动测报与水库调度自动化．电力系统自动化．2001，25(9)：12-14．
[11]甘应爱，田丰，李维铮，运筹学(修订版)．北京：清华大学出版社，1994．
[12]Naccarino, J. R., Cheung, R. T., Briggs, W., et al. Real-time monitoring, optimization and control of a hydroelectric generation complex. IEEE Transactions on Power Systems. 1988, 3(4): 1769-1783.
[13]Sherkat, V. R., Moslehi, K., Lo, E. O., et al. Modular and flexible software for medium- and short-term hydro-thermal scheduling. IEEE Transactions on Power Systems. 1988, 3(3): 1390-1396.
[14]Habibollahzadeh, H., Luo, G.-X., Semlyen, A. Hydrothermal optimal power flow based on a combined linear and nonlinear programming methodology. IEEE Transactions on Power Systems. 1989, 4(2): 530-537.
[15]Tong, S. K., Shahidehpour, S. M. Innovative approach to generation scheduling in large-scale hydro-thermal power systems with fuel constrained units. IEEE Transactions on Power Systems. 1990, 5(2): 665-673.
[16] Hernandez, H. M, Diaz, J. A., Sanchez, G A., et al. Operations planning of Colombian hydro-thermal interconnected system. IEEE Transactions on Power Systems. 1991, 6(2): 778-786.
[17] Ponnambalam, K., Quintana, V. H., Vannelli, A. A fast algorithm for power system optimization problems using an interior point method. IEEE Transactions on Power Systems. 1992, 7(2): 892-899.
[18] Piekutowski, M. R., Litwinowicz, T., Frowd, R. J. Optimal short-term scheduling for a large-scale cascaded hydro system. IEEE Transactions on Power Systems.1994, 9(2): 805-811.
[19] Christoforidis, M., Aganagic, M., Awobamise, B., et al. Long-term/mid-term resource optimization of a hydro-dominant power system using interior point method. IEEE Transactions on Power Systems. 1996, 1(1): 287-294.
[20] Tufegdzic, N., Frowd, R. J., Stadlin, W. O. A coordinated approach for real-time short term hydro scheduling. IEEE Transactions on Power Systems. 1996, 11(4):1698-1704.
[21] Shawwash, Z. K., Siu, T. K., Russell, S. O. D. The B.C. Hydro short term hydro scheduling optimization model. IEEE Transactions on Power Systems. 2000, 15(3):1125-1131.
[22] Chang, G W., Su, C.-T. A practical mixed integer linear programming-based short-term hydro scheduling, in: Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference. 2002, 3: 1606-1610.
[23] Lidgate, D., Amir, B. H. Optimal operational planning for a hydro-electric generation system. IEE Proceedings C: Generation Transmission and Distribution.1988, 135(3): 169-181.
[24] El-Hawary, M. E., Mbamalu, G A. N. Hydro-thermal stochastic optimal power dispatch. A Newton's based approach. IEE Proceedings C: Generation Transmission and Distribution. 1990, 137(3): 213-224.
[25] Zaghlool, M. F., Trutt, F. C. Efficient methods for optimal scheduling of fixed head hydrothermal power systems. IEEE Transactions on Power Systems. 1988, 3(1):24-30.
[26] Rashid, A. H. A., Nor, K. M. An efficient method for optimal scheduling of fixed head hydro and thermal plants. IEEE Transactions on Power Systems. 1991, 6(2):632-636.
[27] Rashid, A. H. A., Nor, K. M. An algorithm for the optimal scheduling of variable head hydro and thermal plants. IEEE Transactions on Power Systems. 1993, 8(3):1242-1249.
[28] Johannesen, A., Gjelsvik, A., Fosso, O. B., et al. Optimal short term hydro scheduling including security constraints. IEEE Transactions on Power Systems.1991, 6(2): 576-583.
[29] El-Hawary, M. E., Ravindranath, K. M. Hydro-thermal power flow scheduling accounting for head variations. IEEE Transactions on Power Systems. 1992, 7(3):1232-1238.
[30] Ruzic, S., Rajakovic, N., Vuckovic, A. A flexible approach to short-term hydro-thermal coordination. I. Problem formulation and general solution procedure.IEEE Transactions on Power Systems. 1996, 11(3): 1564-1571.
[31] Medina, J., Quintana, V. H., Conejo, A. J., et al. A comparison of interior-point codes for medium-term hydro-thermal coordination. IEEE Transactions on Power Systems. 1998, 13(3): 836-843.
[32] Pursimo, J. M., Antila, H. K., Vilkko, M. K., et al. A short-term scheduling for a hydropower plant chain. International Journal of Electrical Power and Energy System. 1998, 20(8): 525-532.
[33] Alguacil, N., Conejo, A. J. Multiperiod optimal power flow using bendersdecomposition. IEEE Transactions on Power Systems. 2000, 15(1): 196-201.
[34] Hau, W., Sasaki, H., Kubokawa, J., et al. Large scale hydrothermal optimal power flow problems based on interior point nonlinear programming. IEEE Transactions on Power Systems. 2000, 15(1): 396-403.
[35] Peng, C, Buras, N. Dynamic operation of a surface water resources system. Water Resources Research. 2000, 36(9): 2701-2709.
[36] Chen, C. L., Chen, N. Direct search method for solving economic dispatch problem considering transmission capacity constraints. IEEE Transactions on Power Systems. 2001, 16(4): 764-769.
[37] Barros, M. T. L., Tsai, F. T. C, Yang, S.-1., et al. Optimization of large-scale hydropower system operations. Journal of Water Resources Planning and Management. 2003, 129(3): 178-188.
[38] Wang, J., Yuan, X., Zhang, Y. Short-term scheduling of large-scale hydropower systems for energy maximization. Journal of Water Resources Planning and Management. 2004, 130(3): 198-205.
[39] Mariano, S. J. P. S., Catalao, J. P. S., Mendes, V. M. R, et al. Head-dependent maximum power generation in short-term hydro scheduling using nonlinear programming, in: Proceedings of the IASTED International Conference on Energy and Power Systems. 2007: 247-252.
[40] Catalao, J. P. S., Mariano, S. J. P. S., Mendes, V. M. R, et al. Scheduling of head-sensitive cascaded hydro systems: A nonlinear approach. IEEE Transactions on Power Systems. 2009, 24(1): 337-346.
[41] Hreinsson, E. B. Optimal short term operation of a purely hydroelectric system. IEEE Transactions on Power Systems. 1988, 3(3): 1072-1077.
[42] Oliveira, P., McKee, S., Coles, C. Optimal scheduling of a hydro-thermal power generation system. European Journal of Operational Research. 1993, 71(3):334-340.
[43] Nilsson, O., Sjelvgren, D. Mixed-integer programming applied to short-term planning of a hydro-thermal system. IEEE Transactions on Power Systems. 1996, 11(1): 281-286.
[44] Nilsson, O., Sjelvgren, D. Variable splitting applied to modelling of start-up costs in short term hydro generation scheduling. IEEE Transactions on Power Systems. 1997, 12(2): 770-775.
[45] Sheble, G B., Fahd, G. N. Unit commitment literature synopsis. IEEE Transactions on Power Systems. 1994, 9(1): 128-135.
[46] Nilsson, O., Soder, L., Sjelvgren, D. Integer modelling of spinning reserve requirements in short term scheduling of hydro systems. IEEE Transactions on Power Systems. 1998, 13(3): 959-964.
[47] Philpott, A. B., Craddock, M., Waterer, H. Hydro-electric unit commitment subject to uncertain demand. European Journal of Operational Research. 2000, 125(2):410-424.
[48] Yu, Z., Sparrow, F. T., Bowen, B., et al. On convexity issues of short-term hydrothermal scheduling. International Journal of Electrical Power and Energy System. 2000, 22(6): 451-457.
[49] Chang, G W., Aganagic, M., Waight, J. G, et al. Experiences with mixed integer linear programming based approaches on short-term hydro scheduling. IEEE Transactions on Power Systems. 2001, 16(4): 743-749.
[50] Garcia-Gonzalez, J., Castro, G A. Short-term hydro scheduling with cascaded and head-dependent reservoirs based on mixed-integer linear programming, in. 2001,vol.3: 6 pp. vol.3.
[51] Borghetti, A., D'Ambrosio, C, Lodi, A., et al. An MILP approach for short-term hydro scheduling and unit commitment with head-dependent reservoir. IEEE Transactions on Power Systems. 2008, 23(3): 1115-1124.
[52] Brannlund, H., Bubenko, J. A., Sjelvgren, D., et al. Optimal short term operation planning of a large hydrothermal power system based on a nonlinear network flow concept. IEEE Transactions on Power Systems. 1986, PWRS-1(4): 75-82.
[53] Xia, Q., Xiang, N., Wang, S., et al. Optimal daily scheduling of cascaded plants using a new algorithm of nonlinear minimum cost network flow. IEEE Transactions on Power Systems. 1988, 3(3): 929-935.
[54] Luo, G-X., Habibollahzadeh, H., Semlyen, A. Short-term hydro-thermal dispatch detailed model and solutions. IEEE Transactions on Power Systems. 1989, 4(4):1452-1462.
[55] Wang, C, Shahidehpour, S. M. Power generation scheduling for multi-area hydro-thermal systems with tie line constraints, cascaded reservoirs and uncertain data. IEEE Transactions on Power Systems. 1993, 8(3): 1333-1340.
[56] Heredia, F. J., Nabona, N. Optimum short-term hydrothermal scheduling with spinning reserve through network flows. IEEE Transactions on Power Systems. 1995,10(3): 1642-1651.
[57] Lamond, B. R, Sobel, M. J. Exact and approximate solutions of affine reservoir models. Operations Research. 1995,43(5): 771-780.
[58] Oliveira, G G, Soares, S. A second-order network flow algorithm for hydrothermal scheduling. IEEE Transactions on Power Systems. 1995, 10(3): 1635-1641.
[59] Ohishi, T., Soares, S., de Carvalho, M. R A short term hydrothermal scheduling approach for dominantly hydro systems. IEEE Transactions on Power Systems.1991, 6(2): 637-643.
[60] Brannlund, H., Sjelvgren, D., Bubenko, J. A. Short term generation scheduling with security constraints. IEEE Transactions on Power Systems. 1988, 3(1): 310-316.
[61] Habibollahzadeh, H., Frances, D., Sui, U. A new generation scheduling program at Ontario Hydro. IEEE Transactions on Power Systems. 1990, 5(1): 65-73.
[62] Wei, H., Sasaki, H., Kubokawa, J. A decoupled solution of hydro-thermal optimal power flow problem by means of interior point method and network programming. IEEE Transactions on Power Systems. 1998, 13(2): 286-293.
[63] Li, C.-a., Jap, P. J., Streiffert, D. L. Implementation of network flow programming to the hydrothermal coordination in an energy management system. IEEE Transactions on Power Systems. 1993, 8(3): 1045-1053.
[64] Franco, P. E. C, Carvalho, M. R, Soares, S. A network flow model for short-term hydro-dominated hydrothermal scheduling problems. IEEE Transactions on Power Systems. 1994, 9(2): 1016-1022.
[65] Bellman, R. Dynamic programming. Science. 1966, 153(3731): 34-37.
[66] Stedinger, J., Sule, B., Loucks, D. Stochastic dynamic programming models for reservoir operation optimization. Water Resources Research. 1984, 20(11):1499-1505.
[67] Piccardi, C, Soncini-Sessa, R. Stochastic dynamic programming for reservoir optimal control: dense discretization and inflow correlation assumption made possible by parallel computing. Water Resources Research. 1991, 27(5): 729-741.
[68] Gorenstin, B. G, Campodonico, N. M., da Costa, J. P., et al. Stochastic optimization of a hydro-thermal system including network constraints. IEEE Transactions on Power Systems. 1992, 7(2): 791-797.
[69]Guan, X., Luh, P. B., Zhang, L. Nonlinear approximation method in Lagrangian relaxation-based algorithms for hydrothermal scheduling. IEEE Transactions on Power Systems. 1995, 10(2): 772-778.
[70]Dantzig, G. B., Infanger, G. Intelligent control and optimization under uncertainty with application to hydro power. European Journal of Operational Research. 1997, 97(2): 396-407.
[71]Guan, X., Ni, E., Li, R., et al. An optimization-based algorithm for scheduling hydrothermal power systems with cascaded reservoirs and discrete hydro constraints. IEEE Transactions on Power Systems. 1997, 12(4): 1775-1780.
[72]Guan, X., Svoboda, A., Li, C.-a. Scheduling hydro power systems with restricted operating zones and discharge ramping constraints. IEEE Transactions on Power Systems. 1999, 14(1): 126-131.
[73]Arce, A., Ohishi, T., Soares, S. Optimal dispatch of generating units of the Itaipu hydroelectric plant. IEEE Transactions on Power Systems. 2002, 17(1 ): 154-158.
[74]董子敖．水库群调度与规划的优化理论和应用．济南：山东科学技术出版社， 1989．
[75]Chang, S.-C., Chen, C.-H., Fong, I. K., et al. Hydroelectric generation scheduling with an effective differential dynamic programming algorithm. IEEE Transactions on Power Systems. 1990, 5(3): 737-743.
[76]Turgeon, A. Optimal short-term hydro scheduling from the principle of progressive optimality. Water Resources Research. 1981, 17(3): 481-486.
[77]Amado, S. M., Ribeiro, C. C. Short-term generation scheduling of hydraulic multi-reservoir multi-area interconnected systems. IEEE Transactions on Power Systems. 1987, PWRS-2(3): 758-763.
[78]Lee, F. N. A method to eliminate solution trapping in applying progressive optimality principle to short-term hydrothermal scheduling. IEEE Transactions on Power Systems. 1989, 4(3): 935-942.
[79]Yang, J.-S., Chen, N. Short term hydrothermal coordination using multi-pass dynamic programming. IEEE Transactions on Power Systems. 1989, 4(3): 1050-1056.
[80] Lee, T.-Y., Chen, N. The effect of pumped storage and battery energy storage systems on hydrothermal generation coordination. IEEE Transactions on Energy Conversion. 1992, 7(4): 631-637.
[81] Guan, X., Luh, P. B., Yan, H., et al. An optimization-based method for unit commitment. International Journal of Electrical Power & Energy Systems. 1992,14(1): 9-17.
[82] Guan, X., Luh, P. B., Yan, H., et al. Optimization-based scheduling of hydrothermal power systems with pumped-storage units. IEEE Transactions on Power Systems.1994,9(2): 1023-1029.
[83] Rakic, M. V., Markovic, Z. M. Short term operation and power exchange planning of hydro-thermal power systems. IEEE Transactions on Power Systems. 1994, 9(1):359-365.
[84] Tang, J., Luh, P. B. Hydrothermal scheduling via extended differential dynamic programming and mixed coordination. IEEE Transactions on Power Systems. 1995,10(4): 2021-2028.
[85] Ruzic, S., Vuckovic, A., Rajakovic, N. A flexible approach to short-term hydro-thermal coordination. II. Dual problem solution procedure. IEEE Transactions on Power Systems. 1996, 11(3): 1572-1578.
[86] Li, C.-a., Hsu, E., Svoboda, A. J., et al. Hydro unit commitment in hydro-thermal optimization. IEEE Transactions on Power Systems. 1997, 12(2): 764-769.
[87] Al-Shakarchi, M. R. G, Al-Hassany, H. D. H. Short-term hydrothermal power system unit commitment: A comparative study. Electric Machines and Power Systems. 2000, 28(7): 955-969.
[88] Siu, T. K., Nash, G. A., Shawwash, Z. K. A practical hydro, dynamic unit commitment and loading model. IEEE Transactions on Power Systems. 2001, 16(2):301-306.
[89] Chuan-Ping, C, Chih-Wen, L., Chun-Chang, L. Unit commitment by Lagrangian relaxation and genetic algorithms. IEEE Transactions on Power Systems. 2000, 15(2): 707-714.
[90] Salam, M. S., Nor, K. M., Hamdam, A. R. Hydrothermal scheduling based Lagrangian relaxation approach to hydrothermal coordination. IEEE Transactions on Power Systems. 1998, 13(1): 226-235.
[91] Ni, E., Guan, X., Li, R. Scheduling hydrothermal power systems with cascaded and head-dependent reservoirs. IEEE Transactions on Power Systems. 1999, 14(3):1127-1132.
[92] Zhuang, F., Galiana, F. D. Towards a more rigorous and practical unit commitment by Lagrangian relaxation. IEEE Transactions on Power Systems. 1988, 3(2):763-773.
[93] Prasannan, B., Luh, P. B., Yan, H., et al. Optimization-based sale transactions and hydrothermal scheduling. IEEE Transactions on Power Systems. 1996, 11(2):654-660.
[94] Yan, H., Luh, P. B., Guan, X., et al. Scheduling of hydrothermal power systems.IEEE Transactions on Power Systems. 1993, 8(3): 1358-1365.
[95] Baldick, R. The generalized unit commitment problem. IEEE Transactions on Power Systems. 1995, 10(1): 465-475.
[96] Al-Agtash, S., Su, R. Augmented Lagrangian approach to hydro-thermal scheduling.IEEE Transactions on Power Systems. 1998, 13(4): 1392-1400.
[97] Ruzic, S., Rajakovic, N. Optimal distance method for Lagrangian multipliers updating in short-term hydro-thermal coordination. IEEE Transactions on Power Systems. 1998, 13(4): 1439-1444.
[98] Redondo, N. J., Conejo, A. J. Short-term hydro-thermal coordination by Lagrangian relaxation: Solution of the dual problem. IEEE Transactions on Power Systems.1999, 14(1): 89-95.
[99] Ngundam, J. M., Kenfack, F., Tatietse, T. T. Optimal scheduling of large-scale hydrothermal power systems using the Lagrangian relaxation technique.International Journal of Electrical Power and Energy System. 2000, 22(4): 237-245.
[100] Nowak, M. P., Romisch, W. Stochastic Lagrangian relaxation applied to power scheduling in a hydro-thermal system under uncertainty. Annals of Operations Research. 2000, 100: 251-272.
[101]A1-Agtash, S. Hydrothermal scheduling by augmented Lagrangian: Consideration of transmission constraints and pumped-storage units. IEEE Transactions on Power Systems. 2001, 16(4): 750-756.
[102]Ahmed, M. M., Ghani, M. R. A., Hassan, I. Fast and optimized hydro-thermal coordination scheduling. in: IEEE/PES Transmission and Distribution Conference and Exhibition (Cat. No.02CH37377). 2002, vol.1 : 261-266.
[103]Aganagic, M., Mokhtari, S. Security constrained economic dispatch using nonlinear Dantzig-Wolfe decomposition. IEEE Transactions on Power Systems. 1997, 12(1): 105-112.
[104]Park, J. H., Kim, Y. S., Lee, K. Y. Economic load dispatch for piecewise quadratic cost function using Hopfield neural network. IEEE Transactions on Power Systems. 1993, 8(3): 1030-1038.
[105]Liang, R. H., Hsu, Y. Y. Scheduling of hydroelectric generations using artificial neural networks. IEE Proceedings: Generation, Transmission and Distribution. 1994, 141(5): 452-458.
[106]Liang, R. H., Hsu, Y. Y. Short-term hydro-scheduling using Hopfield neural network. IEE Proceedings: Generation, Transmission and Distribution. 1996, 143(3): 269-275.
[107]韦柳涛，梁年生．神经网络理论在梯级水电厂短期优化调度中的应用．水电能源科学．1992，10(3)：145-151．
[108]朱敏，王定一．基于人工神经网络的梯级水电厂日优化运行．电力系统自动化．1999，23(10)：35-40．
[109]Naresh, R., Sharma, J. Short term hydro scheduling using two-phase neural network. International Journal of Electrical Power and Energy Systems. 2002, 24(7): 583-590.
[110]Naresh, R., Sharma, J. Two-phase neural network based solution technique for short term hydrothermal scheduling. IEE Proceedings: Generation, Transmission and Distribution. 1999, 146(6): 657-663.
[111 ]Naresh, R. Hydro system scheduling using ANN approach. IEEE Transactions on Power Systems. 2000, 15(1): 388-395.
[112] Glover, F. Future paths for integer programming and links to artificial intelligence. Computers & Operations Research. 1986, 13(5): 533-549.
[113] Glover, F. Tabu search. I. ORSA Journal on Computing. 1989, 1(3): 190-206.
[114] Glover, F. Tabu search. II. ORSA Journal on Computing. 1990, 2(1): 4-32.
[115] Borghetti, A., Frangioni, A., Lacalandra, F, et al. Lagrangian relaxation and Tabu Search approaches for the unit commitment problem, in: 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502). 2001, vol.3: 7 pp. vol.3.
[116] Rajan, C. C. A., Mohan, M. R. An Evolutionary Programming-Based Tabu Search Method For Solving The Unit Commitment Problem. IEEE Transactions on Power Systems. 2004, 19(1): 577-585.
[117] Mitani, T., Mishima, Y, Satoh, T., et al. Security constraints unit commitment by lagrangian decomposition and tabu search, in: Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, ISAP'05. 2005,2005: 440-445.
[118] Victoire, T. A. A., Jeyakumar, A. E. Unit commitment by a tabu-search-based hybrid-optimisation technique. IEE Proceedings: Generation, Transmission and Distribution. 2005, 152(4): 563-574.
[119] Bai, X., Shahidehpour, S. M. Hydro-thermal scheduling by tabu search and decomposition method. IEEE Transactions on Power Systems. 1996, 11(2):968-974.
[120] Mantawy, A. H., Soliman, S. A., El-Hawary, M. E. The long-term hydro-scheduling problem - A new algorithm. Electric Power Systems Research. 2003, 64(1): 67-72.
[121] Orero, S. O., Irving, M. R. Combination of the genetic algorithm and Lagrangian relaxation decomposition techniques for the generation unit commitment problem.Electric Power Systems Research. 1997, 43(3): 149-156.
[122] Mantawy, A. H., Abdel-Magid, Y. L., Selim, S. Z. New genetic-based tabu search algorithm for unit commitment problem. Electric Power Systems Research. 1999, 49(2): 71-78.
[123] Leite, P. T., Carneiro, A. A. d. F. M., De Carvalho, A. C. d. P. L. F. Energetic operation planning using genetic algorithms. IEEE Transactions on Power Systems.2002, 17(1): 173-179.
[124] Yuan, X., Yuan, Y, Zhang, Y. A hybrid chaotic genetic algorithm for short-term hydro system scheduling. Mathematics and Computers in Simulation. 2002, 59(4):319-327.
[125] Orero, S. O., Irving, M. R. A genetic algorithm modelling framework and solution technique for short term optimal hydrothermal scheduling. IEEE Transactions on Power Systems. 1998, 13(2): 501-516.
[126] Wu, Y.-G, Ho, C.-Y, Wang, D.-Y. Diploid genetic approach to short-term scheduling of hydro-thermal system. IEEE Transactions on Power Systems. 2000,15(4): 1268-1274.
[127] Yuan, X., Zhang, Y, Yuan, Y. Improved self-adaptive chaotic genetic algorithm for hydrogeneration scheduling. Journal of Water Resources Planning and Management.2008, 134(4): 319-325.
[128] Storn, R., Price, K. Differential Evolution-A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. Journal of Global Optimization. 1997,11(4): 341-359.
[129] Perez-Guerrero, R. E., Cedeno-Maldonado, J. R. Differential evolution based economic environmental power dispatch, in: Proceedings of the 37th Annual North American Power Symposium, 2005. 2005, 2005: 191-197.
[130]Coelho, L. d. S., Mariani, V. C. Improved differential evolution algorithms for handling economic dispatch optimization with generator constraints. Energy Conversion and Management. 2007, 48(5): 1631-1639.
[131] Yuan, X., Wang, L., Yuan, Y, et al. A modified differential evolution approach for dynamic economic dispatch with valve-point effects. Energy Conversion and Management. 2008, 49(12): 3447-3453.
[132] Yuan, X., Cao, B., Yang, B., et al. Hydrothermal scheduling using chaotic hybrid differential evolution. Energy Conversion and Management. 2008, 49(12):3627-3633.
[133] Lakshminarasimman, L., Subramanian, S. Short-term scheduling of hydrothermal power system with cascaded reservoirs by using modified differential evolution. IEE Proceedings: Generation, Transmission and Distribution. 2006, 153(6): 693-700.
[134]Thitithamrongchai, C., Eua-Arporn, B. Economic load dispatch for piecewise quadratic cost function using hybrid self-adaptive differential evolution with augmented lagrange multiplier method. in: Proceedings of 2006 International Conference on Power System Technology. 2007: 1-8.
[135]Balamurugan, R., Subramanian, S. Hybrid integer coded differential evolution - dynamic programming approach for economic load dispatch with multiple fuel options. Energy Conversion and Management. 2008, 49(4): 608-614.
[136]Yuan, X., Wang, L., Zhang, Y., et al. A hybrid differential evolution method for dynamic economic dispatch with valve-point effects. Expert Systems with Applications. 2009, 36(2 PART 2): 4042-4048.
[137]Kennedy, J., Eberhart, R. Particle swarm optimization. in: Proceedings of the 1995 IEEE International Conference on Neural Networks. 1995, 4:1942-1948.
[138]杨维，李歧强．粒子群优化算法综述．中国工程科学．2004，6(5)：87-94．
[139]余炳辉，王金文，权先璋等．求解水火电力系统短期发电计划的粒子群优化算法研究．水电能源科学．2005，23(6)：84-88．
[140]张双虎，黄强，吴洪寿等．水电站水库优化调度的改进粒子群算法．水力发电学报．2007，26(1)．
[141]李安强，王丽萍，蔺伟民等．免疫粒子群算法在梯级电站短期优化调度中的应用．水利学报．2008，39(4)：426-432．
[142]杨道辉，马光文，过夏明等．粒子群算法在水电站优化调度中的应用．水力发电学报．2006，．25(5)：5-7．
[143]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．水力发电学报．2006，25(2)：94-98．
[144]武新宇，程春田，廖胜利等．两阶段粒子群算法在水电站群优化调度中的应用．电网技术．2006，30(20)：25-28．
[145]Yu, B., Yuan, X., Wang, J. Short-term hydro-thermal scheduling using particle swarm optimization method. Energy Conversion and Management. 2007, 48(7): 1902-1908.
[146]Yuan, X.,Wang, L., Yuan, Y. Application of enhanced PSOapproachto optimal scheduling of hydro system. Energy ConversionandManagement. 2008, 49(11): 2966-2972.
[147]李晓磊，邵之江，钱积新．一种基于动物自治体的寻优模式：鱼群算法．系统工程理论与实践．2002，(11)：32-38．
[148]Eusuff, M. M., Lansey, K. E. Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of Water Resources Planning and Management. 2003, 129(3): 210-225.
[149]王正初，周慕逊，李军等．基于人工鱼群算法的水库优化调度研究．继电器． 2007，35(2 1)：43-46．
[150]白小勇，苏华英，舒凯等．人工鱼群算法在水库优化调度中的应用．水电能源科学．2008，26(5)：51-53．
[151 ]Li, Y., Zhou, J., Yang, J., et al. The Chaos-based shuffled frog leaping algorithm and its application. in: Proceedings of 4th International Conference on Natural Computation, ICNC 2008.2008, 1:481-485.
[152]Cervellera, C., Chen, V. C. P., Wen, A. Optimization of a large-scale water reservoir network by stochastic dynamic programming with efficient state space discretization. European Journal of Operational Research. 2006, 171 (3): 1139-1151.
[153]Foufoula-Georgiou, E., Kitanidis, P. Gradient dynamic programming for stochastic optimal control of multidimensional water resources systems. Water resources research. 1988, 24(8): 1345-1359.
[154]Bessa, M., Ponnambalam, K., Bogobowicz, A. Multi-reservoir systems optimization by the aggregation/decomposition methodology. in. 1994:192-196.
[155]Turgeon, A., Charbonneau, R. An aggregation-disaggregation approach to long-term reservoir management. Water Resources Research. 1998, 34(12): 3585-3594.
[156]Archibald, T., McKinnon, K., Thomas, L. An aggregate stochastic dynamic programming model of multireservoir systems. Water Resources Research. 1997, 33(2): 333-340.
[157]Martinez, L., Soares, S. Comparison between closed-loop and partial open-loop feedback control policies in long term hydrothermal scheduling. IEEE Transactions on Power Systems. 2002, 17(2): 330-336.
[158]Philbrick Jr, C. R., Kitanidis, P. K. Limitations of deterministic optimization applied to reservoir operations. Journal of Water Resources Planning and Management. 1999, 125(3): 135-142.
[159]Sivapragasam, C., Vasudevan, G., Vincent, P. Effect of inflow forecast accuracy and operating time horizon in optimizing irrigation releases. Water Resources Management. 2007, 21 (6): 933-945.
[160]曾勇红．仿射尺度算法及其在水电系统优化调度中的应用研究．[博士学位论文]．华中科技大学，2004．
[161]Rux, L. M. Incremental economic dispatch method for cascaded hydroelectric powerplants. IEEE Transactions on Power Systems. 1993, 8(3): 1266-1273.
[162]李彩林．水电站中长期优化调度与风险研究．[博士学位论文]．华中科技大学， 2007．
[163]Frank, M., Wolfe, P. An algorithm for quadratic programming. Naval Research Logistics Quarterly. 1956, 3 (1-2): 95-110.
[164]俞玉森，杨林锡，邓成梁等．数学规划的原理和方法．(第二版)．武汉：华中理工大学出版社，1993．
[165]Dantzig, G. B. Linear programming and extensions. Princeton Univ Pr, 1963.
[166]Dantzig, G. B., Wolfe, P. Decomposition principle for linear programs. Operations Research. 1960, 8(1): 101-111.
[167]刘治理，马光文，戴露．基于三层B／S结构的梯级水电厂中长期优化调度．计算机工程．2006，32(6)：240-242．
[168]涂扬举，马光文，刘治理等．基于三层B／S结构的水电站中长期优化调度系统．水力发电学报．2006，25(2)：90-93．
[169]付洁廉．水电系统长期优化调度及系统应用软件研究．[硕士学位论文]．华中科技大学，2006
[170]Aganagic, M., Mokhtari, S. Coordination between short-term and long-term resource scheduling. in: Proceedings of the American Power Conference. 1995, 57-2: 1719-1722.
[171]Marwali, M. K. C, Shahidehpour, S. M. Coordination between long-term and short-term generation scheduling with network constraints. IEEE Transactions on Power Systems. 2000, 15(3): 1161-1167.
[172] Dong, X., Zhao, Y., Yuan, J., et al. Coupling the long-term and short-term optimization models for reservoir operation, in: Proceedings of the International Symposium on Dams in the Societies of the 21st Century, ICOLD-SPANCOLD-Dams and Reservoirs, Societies and Environment in the 21st Century. 2006, 1:277-282.
[173] Batista, A., Suzuki, K., Kadota, A. Integrating long- and short-term reservoir operation models via stochastic and deterministic optimization: Case study in Japan. Journal of Water Resources Planning and Management. 2008, 134(5): 440-448.
[2]张勇传．水电系统最优控制．华中理工大学出版社，1993．
[3]张勇传．水电站经济运行原理．北京：中国水利水电出版社，1998．
[4]李钰心．水电站经济运行．北京：中国电力出版社，1999．
[5]Yeh, W. W.-G. Reservoir Management and Operations Model: A State-of-the-Art Review. Water Resources Research. 1985, 21(12): 1797-1818.
[6]Labadie, J. W. Optimal operation ofmultireservoir systems: State-of-the-art review. Journal of Water Resources Planning and Management. 2004, 130(2): 93-111.
[7]王金文．仿单纯形法及其在短期水电系统发电调度中的应用．[博士学位论文]．华中科技大学，2003．
[8]张高峰．梯级水电系统短期发电优化调度与自动发电控制．[博士学位论文]．华中科技大学，2004．
[9]余炳辉．粒子群优化算法试验研究及扩展研究．[博士学位论文]．华中科技大学，2007．
[10]裴哲义，孙芹芳．水电厂水情自动测报与水库调度自动化．电力系统自动化．2001，25(9)：12-14．
[11]甘应爱，田丰，李维铮，运筹学(修订版)．北京：清华大学出版社，1994．
[12]Naccarino, J. R., Cheung, R. T., Briggs, W., et al. Real-time monitoring, optimization and control of a hydroelectric generation complex. IEEE Transactions on Power Systems. 1988, 3(4): 1769-1783.
[13]Sherkat, V. R., Moslehi, K., Lo, E. O., et al. Modular and flexible software for medium- and short-term hydro-thermal scheduling. IEEE Transactions on Power Systems. 1988, 3(3): 1390-1396.
[14]Habibollahzadeh, H., Luo, G.-X., Semlyen, A. Hydrothermal optimal power flow based on a combined linear and nonlinear programming methodology. IEEE Transactions on Power Systems. 1989, 4(2): 530-537.
[15]Tong, S. K., Shahidehpour, S. M. Innovative approach to generation scheduling in large-scale hydro-thermal power systems with fuel constrained units. IEEE Transactions on Power Systems. 1990, 5(2): 665-673.
[16] Hernandez, H. M, Diaz, J. A., Sanchez, G A., et al. Operations planning of Colombian hydro-thermal interconnected system. IEEE Transactions on Power Systems. 1991, 6(2): 778-786.
[17] Ponnambalam, K., Quintana, V. H., Vannelli, A. A fast algorithm for power system optimization problems using an interior point method. IEEE Transactions on Power Systems. 1992, 7(2): 892-899.
[18] Piekutowski, M. R., Litwinowicz, T., Frowd, R. J. Optimal short-term scheduling for a large-scale cascaded hydro system. IEEE Transactions on Power Systems.1994, 9(2): 805-811.
[19] Christoforidis, M., Aganagic, M., Awobamise, B., et al. Long-term/mid-term resource optimization of a hydro-dominant power system using interior point method. IEEE Transactions on Power Systems. 1996, 1(1): 287-294.
[20] Tufegdzic, N., Frowd, R. J., Stadlin, W. O. A coordinated approach for real-time short term hydro scheduling. IEEE Transactions on Power Systems. 1996, 11(4):1698-1704.
[21] Shawwash, Z. K., Siu, T. K., Russell, S. O. D. The B.C. Hydro short term hydro scheduling optimization model. IEEE Transactions on Power Systems. 2000, 15(3):1125-1131.
[22] Chang, G W., Su, C.-T. A practical mixed integer linear programming-based short-term hydro scheduling, in: Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference. 2002, 3: 1606-1610.
[23] Lidgate, D., Amir, B. H. Optimal operational planning for a hydro-electric generation system. IEE Proceedings C: Generation Transmission and Distribution.1988, 135(3): 169-181.
[24] El-Hawary, M. E., Mbamalu, G A. N. Hydro-thermal stochastic optimal power dispatch. A Newton's based approach. IEE Proceedings C: Generation Transmission and Distribution. 1990, 137(3): 213-224.
[25] Zaghlool, M. F., Trutt, F. C. Efficient methods for optimal scheduling of fixed head hydrothermal power systems. IEEE Transactions on Power Systems. 1988, 3(1):24-30.
[26] Rashid, A. H. A., Nor, K. M. An efficient method for optimal scheduling of fixed head hydro and thermal plants. IEEE Transactions on Power Systems. 1991, 6(2):632-636.
[27] Rashid, A. H. A., Nor, K. M. An algorithm for the optimal scheduling of variable head hydro and thermal plants. IEEE Transactions on Power Systems. 1993, 8(3):1242-1249.
[28] Johannesen, A., Gjelsvik, A., Fosso, O. B., et al. Optimal short term hydro scheduling including security constraints. IEEE Transactions on Power Systems.1991, 6(2): 576-583.
[29] El-Hawary, M. E., Ravindranath, K. M. Hydro-thermal power flow scheduling accounting for head variations. IEEE Transactions on Power Systems. 1992, 7(3):1232-1238.
[30] Ruzic, S., Rajakovic, N., Vuckovic, A. A flexible approach to short-term hydro-thermal coordination. I. Problem formulation and general solution procedure.IEEE Transactions on Power Systems. 1996, 11(3): 1564-1571.
[31] Medina, J., Quintana, V. H., Conejo, A. J., et al. A comparison of interior-point codes for medium-term hydro-thermal coordination. IEEE Transactions on Power Systems. 1998, 13(3): 836-843.
[32] Pursimo, J. M., Antila, H. K., Vilkko, M. K., et al. A short-term scheduling for a hydropower plant chain. International Journal of Electrical Power and Energy System. 1998, 20(8): 525-532.
[33] Alguacil, N., Conejo, A. J. Multiperiod optimal power flow using bendersdecomposition. IEEE Transactions on Power Systems. 2000, 15(1): 196-201.
[34] Hau, W., Sasaki, H., Kubokawa, J., et al. Large scale hydrothermal optimal power flow problems based on interior point nonlinear programming. IEEE Transactions on Power Systems. 2000, 15(1): 396-403.
[35] Peng, C, Buras, N. Dynamic operation of a surface water resources system. Water Resources Research. 2000, 36(9): 2701-2709.
[36] Chen, C. L., Chen, N. Direct search method for solving economic dispatch problem considering transmission capacity constraints. IEEE Transactions on Power Systems. 2001, 16(4): 764-769.
[37] Barros, M. T. L., Tsai, F. T. C, Yang, S.-1., et al. Optimization of large-scale hydropower system operations. Journal of Water Resources Planning and Management. 2003, 129(3): 178-188.
[38] Wang, J., Yuan, X., Zhang, Y. Short-term scheduling of large-scale hydropower systems for energy maximization. Journal of Water Resources Planning and Management. 2004, 130(3): 198-205.
[39] Mariano, S. J. P. S., Catalao, J. P. S., Mendes, V. M. R, et al. Head-dependent maximum power generation in short-term hydro scheduling using nonlinear programming, in: Proceedings of the IASTED International Conference on Energy and Power Systems. 2007: 247-252.
[40] Catalao, J. P. S., Mariano, S. J. P. S., Mendes, V. M. R, et al. Scheduling of head-sensitive cascaded hydro systems: A nonlinear approach. IEEE Transactions on Power Systems. 2009, 24(1): 337-346.
[41] Hreinsson, E. B. Optimal short term operation of a purely hydroelectric system. IEEE Transactions on Power Systems. 1988, 3(3): 1072-1077.
[42] Oliveira, P., McKee, S., Coles, C. Optimal scheduling of a hydro-thermal power generation system. European Journal of Operational Research. 1993, 71(3):334-340.
[43] Nilsson, O., Sjelvgren, D. Mixed-integer programming applied to short-term planning of a hydro-thermal system. IEEE Transactions on Power Systems. 1996, 11(1): 281-286.
[44] Nilsson, O., Sjelvgren, D. Variable splitting applied to modelling of start-up costs in short term hydro generation scheduling. IEEE Transactions on Power Systems. 1997, 12(2): 770-775.
[45] Sheble, G B., Fahd, G. N. Unit commitment literature synopsis. IEEE Transactions on Power Systems. 1994, 9(1): 128-135.
[46] Nilsson, O., Soder, L., Sjelvgren, D. Integer modelling of spinning reserve requirements in short term scheduling of hydro systems. IEEE Transactions on Power Systems. 1998, 13(3): 959-964.
[47] Philpott, A. B., Craddock, M., Waterer, H. Hydro-electric unit commitment subject to uncertain demand. European Journal of Operational Research. 2000, 125(2):410-424.
[48] Yu, Z., Sparrow, F. T., Bowen, B., et al. On convexity issues of short-term hydrothermal scheduling. International Journal of Electrical Power and Energy System. 2000, 22(6): 451-457.
[49] Chang, G W., Aganagic, M., Waight, J. G, et al. Experiences with mixed integer linear programming based approaches on short-term hydro scheduling. IEEE Transactions on Power Systems. 2001, 16(4): 743-749.
[50] Garcia-Gonzalez, J., Castro, G A. Short-term hydro scheduling with cascaded and head-dependent reservoirs based on mixed-integer linear programming, in. 2001,vol.3: 6 pp. vol.3.
[51] Borghetti, A., D'Ambrosio, C, Lodi, A., et al. An MILP approach for short-term hydro scheduling and unit commitment with head-dependent reservoir. IEEE Transactions on Power Systems. 2008, 23(3): 1115-1124.
[52] Brannlund, H., Bubenko, J. A., Sjelvgren, D., et al. Optimal short term operation planning of a large hydrothermal power system based on a nonlinear network flow concept. IEEE Transactions on Power Systems. 1986, PWRS-1(4): 75-82.
[53] Xia, Q., Xiang, N., Wang, S., et al. Optimal daily scheduling of cascaded plants using a new algorithm of nonlinear minimum cost network flow. IEEE Transactions on Power Systems. 1988, 3(3): 929-935.
[54] Luo, G-X., Habibollahzadeh, H., Semlyen, A. Short-term hydro-thermal dispatch detailed model and solutions. IEEE Transactions on Power Systems. 1989, 4(4):1452-1462.
[55] Wang, C, Shahidehpour, S. M. Power generation scheduling for multi-area hydro-thermal systems with tie line constraints, cascaded reservoirs and uncertain data. IEEE Transactions on Power Systems. 1993, 8(3): 1333-1340.
[56] Heredia, F. J., Nabona, N. Optimum short-term hydrothermal scheduling with spinning reserve through network flows. IEEE Transactions on Power Systems. 1995,10(3): 1642-1651.
[57] Lamond, B. R, Sobel, M. J. Exact and approximate solutions of affine reservoir models. Operations Research. 1995,43(5): 771-780.
[58] Oliveira, G G, Soares, S. A second-order network flow algorithm for hydrothermal scheduling. IEEE Transactions on Power Systems. 1995, 10(3): 1635-1641.
[59] Ohishi, T., Soares, S., de Carvalho, M. R A short term hydrothermal scheduling approach for dominantly hydro systems. IEEE Transactions on Power Systems.1991, 6(2): 637-643.
[60] Brannlund, H., Sjelvgren, D., Bubenko, J. A. Short term generation scheduling with security constraints. IEEE Transactions on Power Systems. 1988, 3(1): 310-316.
[61] Habibollahzadeh, H., Frances, D., Sui, U. A new generation scheduling program at Ontario Hydro. IEEE Transactions on Power Systems. 1990, 5(1): 65-73.
[62] Wei, H., Sasaki, H., Kubokawa, J. A decoupled solution of hydro-thermal optimal power flow problem by means of interior point method and network programming. IEEE Transactions on Power Systems. 1998, 13(2): 286-293.
[63] Li, C.-a., Jap, P. J., Streiffert, D. L. Implementation of network flow programming to the hydrothermal coordination in an energy management system. IEEE Transactions on Power Systems. 1993, 8(3): 1045-1053.
[64] Franco, P. E. C, Carvalho, M. R, Soares, S. A network flow model for short-term hydro-dominated hydrothermal scheduling problems. IEEE Transactions on Power Systems. 1994, 9(2): 1016-1022.
[65] Bellman, R. Dynamic programming. Science. 1966, 153(3731): 34-37.
[66] Stedinger, J., Sule, B., Loucks, D. Stochastic dynamic programming models for reservoir operation optimization. Water Resources Research. 1984, 20(11):1499-1505.
[67] Piccardi, C, Soncini-Sessa, R. Stochastic dynamic programming for reservoir optimal control: dense discretization and inflow correlation assumption made possible by parallel computing. Water Resources Research. 1991, 27(5): 729-741.
[68] Gorenstin, B. G, Campodonico, N. M., da Costa, J. P., et al. Stochastic optimization of a hydro-thermal system including network constraints. IEEE Transactions on Power Systems. 1992, 7(2): 791-797.
[69]Guan, X., Luh, P. B., Zhang, L. Nonlinear approximation method in Lagrangian relaxation-based algorithms for hydrothermal scheduling. IEEE Transactions on Power Systems. 1995, 10(2): 772-778.
[70]Dantzig, G. B., Infanger, G. Intelligent control and optimization under uncertainty with application to hydro power. European Journal of Operational Research. 1997, 97(2): 396-407.
[71]Guan, X., Ni, E., Li, R., et al. An optimization-based algorithm for scheduling hydrothermal power systems with cascaded reservoirs and discrete hydro constraints. IEEE Transactions on Power Systems. 1997, 12(4): 1775-1780.
[72]Guan, X., Svoboda, A., Li, C.-a. Scheduling hydro power systems with restricted operating zones and discharge ramping constraints. IEEE Transactions on Power Systems. 1999, 14(1): 126-131.
[73]Arce, A., Ohishi, T., Soares, S. Optimal dispatch of generating units of the Itaipu hydroelectric plant. IEEE Transactions on Power Systems. 2002, 17(1 ): 154-158.
[74]董子敖．水库群调度与规划的优化理论和应用．济南：山东科学技术出版社， 1989．
[75]Chang, S.-C., Chen, C.-H., Fong, I. K., et al. Hydroelectric generation scheduling with an effective differential dynamic programming algorithm. IEEE Transactions on Power Systems. 1990, 5(3): 737-743.
[76]Turgeon, A. Optimal short-term hydro scheduling from the principle of progressive optimality. Water Resources Research. 1981, 17(3): 481-486.
[77]Amado, S. M., Ribeiro, C. C. Short-term generation scheduling of hydraulic multi-reservoir multi-area interconnected systems. IEEE Transactions on Power Systems. 1987, PWRS-2(3): 758-763.
[78]Lee, F. N. A method to eliminate solution trapping in applying progressive optimality principle to short-term hydrothermal scheduling. IEEE Transactions on Power Systems. 1989, 4(3): 935-942.
[79]Yang, J.-S., Chen, N. Short term hydrothermal coordination using multi-pass dynamic programming. IEEE Transactions on Power Systems. 1989, 4(3): 1050-1056.
[80] Lee, T.-Y., Chen, N. The effect of pumped storage and battery energy storage systems on hydrothermal generation coordination. IEEE Transactions on Energy Conversion. 1992, 7(4): 631-637.
[81] Guan, X., Luh, P. B., Yan, H., et al. An optimization-based method for unit commitment. International Journal of Electrical Power & Energy Systems. 1992,14(1): 9-17.
[82] Guan, X., Luh, P. B., Yan, H., et al. Optimization-based scheduling of hydrothermal power systems with pumped-storage units. IEEE Transactions on Power Systems.1994,9(2): 1023-1029.
[83] Rakic, M. V., Markovic, Z. M. Short term operation and power exchange planning of hydro-thermal power systems. IEEE Transactions on Power Systems. 1994, 9(1):359-365.
[84] Tang, J., Luh, P. B. Hydrothermal scheduling via extended differential dynamic programming and mixed coordination. IEEE Transactions on Power Systems. 1995,10(4): 2021-2028.
[85] Ruzic, S., Vuckovic, A., Rajakovic, N. A flexible approach to short-term hydro-thermal coordination. II. Dual problem solution procedure. IEEE Transactions on Power Systems. 1996, 11(3): 1572-1578.
[86] Li, C.-a., Hsu, E., Svoboda, A. J., et al. Hydro unit commitment in hydro-thermal optimization. IEEE Transactions on Power Systems. 1997, 12(2): 764-769.
[87] Al-Shakarchi, M. R. G, Al-Hassany, H. D. H. Short-term hydrothermal power system unit commitment: A comparative study. Electric Machines and Power Systems. 2000, 28(7): 955-969.
[88] Siu, T. K., Nash, G. A., Shawwash, Z. K. A practical hydro, dynamic unit commitment and loading model. IEEE Transactions on Power Systems. 2001, 16(2):301-306.
[89] Chuan-Ping, C, Chih-Wen, L., Chun-Chang, L. Unit commitment by Lagrangian relaxation and genetic algorithms. IEEE Transactions on Power Systems. 2000, 15(2): 707-714.
[90] Salam, M. S., Nor, K. M., Hamdam, A. R. Hydrothermal scheduling based Lagrangian relaxation approach to hydrothermal coordination. IEEE Transactions on Power Systems. 1998, 13(1): 226-235.
[91] Ni, E., Guan, X., Li, R. Scheduling hydrothermal power systems with cascaded and head-dependent reservoirs. IEEE Transactions on Power Systems. 1999, 14(3):1127-1132.
[92] Zhuang, F., Galiana, F. D. Towards a more rigorous and practical unit commitment by Lagrangian relaxation. IEEE Transactions on Power Systems. 1988, 3(2):763-773.
[93] Prasannan, B., Luh, P. B., Yan, H., et al. Optimization-based sale transactions and hydrothermal scheduling. IEEE Transactions on Power Systems. 1996, 11(2):654-660.
[94] Yan, H., Luh, P. B., Guan, X., et al. Scheduling of hydrothermal power systems.IEEE Transactions on Power Systems. 1993, 8(3): 1358-1365.
[95] Baldick, R. The generalized unit commitment problem. IEEE Transactions on Power Systems. 1995, 10(1): 465-475.
[96] Al-Agtash, S., Su, R. Augmented Lagrangian approach to hydro-thermal scheduling.IEEE Transactions on Power Systems. 1998, 13(4): 1392-1400.
[97] Ruzic, S., Rajakovic, N. Optimal distance method for Lagrangian multipliers updating in short-term hydro-thermal coordination. IEEE Transactions on Power Systems. 1998, 13(4): 1439-1444.
[98] Redondo, N. J., Conejo, A. J. Short-term hydro-thermal coordination by Lagrangian relaxation: Solution of the dual problem. IEEE Transactions on Power Systems.1999, 14(1): 89-95.
[99] Ngundam, J. M., Kenfack, F., Tatietse, T. T. Optimal scheduling of large-scale hydrothermal power systems using the Lagrangian relaxation technique.International Journal of Electrical Power and Energy System. 2000, 22(4): 237-245.
[100] Nowak, M. P., Romisch, W. Stochastic Lagrangian relaxation applied to power scheduling in a hydro-thermal system under uncertainty. Annals of Operations Research. 2000, 100: 251-272.
[101]A1-Agtash, S. Hydrothermal scheduling by augmented Lagrangian: Consideration of transmission constraints and pumped-storage units. IEEE Transactions on Power Systems. 2001, 16(4): 750-756.
[102]Ahmed, M. M., Ghani, M. R. A., Hassan, I. Fast and optimized hydro-thermal coordination scheduling. in: IEEE/PES Transmission and Distribution Conference and Exhibition (Cat. No.02CH37377). 2002, vol.1 : 261-266.
[103]Aganagic, M., Mokhtari, S. Security constrained economic dispatch using nonlinear Dantzig-Wolfe decomposition. IEEE Transactions on Power Systems. 1997, 12(1): 105-112.
[104]Park, J. H., Kim, Y. S., Lee, K. Y. Economic load dispatch for piecewise quadratic cost function using Hopfield neural network. IEEE Transactions on Power Systems. 1993, 8(3): 1030-1038.
[105]Liang, R. H., Hsu, Y. Y. Scheduling of hydroelectric generations using artificial neural networks. IEE Proceedings: Generation, Transmission and Distribution. 1994, 141(5): 452-458.
[106]Liang, R. H., Hsu, Y. Y. Short-term hydro-scheduling using Hopfield neural network. IEE Proceedings: Generation, Transmission and Distribution. 1996, 143(3): 269-275.
[107]韦柳涛，梁年生．神经网络理论在梯级水电厂短期优化调度中的应用．水电能源科学．1992，10(3)：145-151．
[108]朱敏，王定一．基于人工神经网络的梯级水电厂日优化运行．电力系统自动化．1999，23(10)：35-40．
[109]Naresh, R., Sharma, J. Short term hydro scheduling using two-phase neural network. International Journal of Electrical Power and Energy Systems. 2002, 24(7): 583-590.
[110]Naresh, R., Sharma, J. Two-phase neural network based solution technique for short term hydrothermal scheduling. IEE Proceedings: Generation, Transmission and Distribution. 1999, 146(6): 657-663.
[111 ]Naresh, R. Hydro system scheduling using ANN approach. IEEE Transactions on Power Systems. 2000, 15(1): 388-395.
[112] Glover, F. Future paths for integer programming and links to artificial intelligence. Computers & Operations Research. 1986, 13(5): 533-549.
[113] Glover, F. Tabu search. I. ORSA Journal on Computing. 1989, 1(3): 190-206.
[114] Glover, F. Tabu search. II. ORSA Journal on Computing. 1990, 2(1): 4-32.
[115] Borghetti, A., Frangioni, A., Lacalandra, F, et al. Lagrangian relaxation and Tabu Search approaches for the unit commitment problem, in: 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502). 2001, vol.3: 7 pp. vol.3.
[116] Rajan, C. C. A., Mohan, M. R. An Evolutionary Programming-Based Tabu Search Method For Solving The Unit Commitment Problem. IEEE Transactions on Power Systems. 2004, 19(1): 577-585.
[117] Mitani, T., Mishima, Y, Satoh, T., et al. Security constraints unit commitment by lagrangian decomposition and tabu search, in: Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, ISAP'05. 2005,2005: 440-445.
[118] Victoire, T. A. A., Jeyakumar, A. E. Unit commitment by a tabu-search-based hybrid-optimisation technique. IEE Proceedings: Generation, Transmission and Distribution. 2005, 152(4): 563-574.
[119] Bai, X., Shahidehpour, S. M. Hydro-thermal scheduling by tabu search and decomposition method. IEEE Transactions on Power Systems. 1996, 11(2):968-974.
[120] Mantawy, A. H., Soliman, S. A., El-Hawary, M. E. The long-term hydro-scheduling problem - A new algorithm. Electric Power Systems Research. 2003, 64(1): 67-72.
[121] Orero, S. O., Irving, M. R. Combination of the genetic algorithm and Lagrangian relaxation decomposition techniques for the generation unit commitment problem.Electric Power Systems Research. 1997, 43(3): 149-156.
[122] Mantawy, A. H., Abdel-Magid, Y. L., Selim, S. Z. New genetic-based tabu search algorithm for unit commitment problem. Electric Power Systems Research. 1999, 49(2): 71-78.
[123] Leite, P. T., Carneiro, A. A. d. F. M., De Carvalho, A. C. d. P. L. F. Energetic operation planning using genetic algorithms. IEEE Transactions on Power Systems.2002, 17(1): 173-179.
[124] Yuan, X., Yuan, Y, Zhang, Y. A hybrid chaotic genetic algorithm for short-term hydro system scheduling. Mathematics and Computers in Simulation. 2002, 59(4):319-327.
[125] Orero, S. O., Irving, M. R. A genetic algorithm modelling framework and solution technique for short term optimal hydrothermal scheduling. IEEE Transactions on Power Systems. 1998, 13(2): 501-516.
[126] Wu, Y.-G, Ho, C.-Y, Wang, D.-Y. Diploid genetic approach to short-term scheduling of hydro-thermal system. IEEE Transactions on Power Systems. 2000,15(4): 1268-1274.
[127] Yuan, X., Zhang, Y, Yuan, Y. Improved self-adaptive chaotic genetic algorithm for hydrogeneration scheduling. Journal of Water Resources Planning and Management.2008, 134(4): 319-325.
[128] Storn, R., Price, K. Differential Evolution-A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. Journal of Global Optimization. 1997,11(4): 341-359.
[129] Perez-Guerrero, R. E., Cedeno-Maldonado, J. R. Differential evolution based economic environmental power dispatch, in: Proceedings of the 37th Annual North American Power Symposium, 2005. 2005, 2005: 191-197.
[130]Coelho, L. d. S., Mariani, V. C. Improved differential evolution algorithms for handling economic dispatch optimization with generator constraints. Energy Conversion and Management. 2007, 48(5): 1631-1639.
[131] Yuan, X., Wang, L., Yuan, Y, et al. A modified differential evolution approach for dynamic economic dispatch with valve-point effects. Energy Conversion and Management. 2008, 49(12): 3447-3453.
[132] Yuan, X., Cao, B., Yang, B., et al. Hydrothermal scheduling using chaotic hybrid differential evolution. Energy Conversion and Management. 2008, 49(12):3627-3633.
[133] Lakshminarasimman, L., Subramanian, S. Short-term scheduling of hydrothermal power system with cascaded reservoirs by using modified differential evolution. IEE Proceedings: Generation, Transmission and Distribution. 2006, 153(6): 693-700.
[134]Thitithamrongchai, C., Eua-Arporn, B. Economic load dispatch for piecewise quadratic cost function using hybrid self-adaptive differential evolution with augmented lagrange multiplier method. in: Proceedings of 2006 International Conference on Power System Technology. 2007: 1-8.
[135]Balamurugan, R., Subramanian, S. Hybrid integer coded differential evolution - dynamic programming approach for economic load dispatch with multiple fuel options. Energy Conversion and Management. 2008, 49(4): 608-614.
[136]Yuan, X., Wang, L., Zhang, Y., et al. A hybrid differential evolution method for dynamic economic dispatch with valve-point effects. Expert Systems with Applications. 2009, 36(2 PART 2): 4042-4048.
[137]Kennedy, J., Eberhart, R. Particle swarm optimization. in: Proceedings of the 1995 IEEE International Conference on Neural Networks. 1995, 4:1942-1948.
[138]杨维，李歧强．粒子群优化算法综述．中国工程科学．2004，6(5)：87-94．
[139]余炳辉，王金文，权先璋等．求解水火电力系统短期发电计划的粒子群优化算法研究．水电能源科学．2005，23(6)：84-88．
[140]张双虎，黄强，吴洪寿等．水电站水库优化调度的改进粒子群算法．水力发电学报．2007，26(1)．
[141]李安强，王丽萍，蔺伟民等．免疫粒子群算法在梯级电站短期优化调度中的应用．水利学报．2008，39(4)：426-432．
[142]杨道辉，马光文，过夏明等．粒子群算法在水电站优化调度中的应用．水力发电学报．2006，．25(5)：5-7．
[143]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．水力发电学报．2006，25(2)：94-98．
[144]武新宇，程春田，廖胜利等．两阶段粒子群算法在水电站群优化调度中的应用．电网技术．2006，30(20)：25-28．
[145]Yu, B., Yuan, X., Wang, J. Short-term hydro-thermal scheduling using particle swarm optimization method. Energy Conversion and Management. 2007, 48(7): 1902-1908.
[146]Yuan, X.,Wang, L., Yuan, Y. Application of enhanced PSOapproachto optimal scheduling of hydro system. Energy ConversionandManagement. 2008, 49(11): 2966-2972.
[147]李晓磊，邵之江，钱积新．一种基于动物自治体的寻优模式：鱼群算法．系统工程理论与实践．2002，(11)：32-38．
[148]Eusuff, M. M., Lansey, K. E. Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of Water Resources Planning and Management. 2003, 129(3): 210-225.
[149]王正初，周慕逊，李军等．基于人工鱼群算法的水库优化调度研究．继电器． 2007，35(2 1)：43-46．
[150]白小勇，苏华英，舒凯等．人工鱼群算法在水库优化调度中的应用．水电能源科学．2008，26(5)：51-53．
[151 ]Li, Y., Zhou, J., Yang, J., et al. The Chaos-based shuffled frog leaping algorithm and its application. in: Proceedings of 4th International Conference on Natural Computation, ICNC 2008.2008, 1:481-485.
[152]Cervellera, C., Chen, V. C. P., Wen, A. Optimization of a large-scale water reservoir network by stochastic dynamic programming with efficient state space discretization. European Journal of Operational Research. 2006, 171 (3): 1139-1151.
[153]Foufoula-Georgiou, E., Kitanidis, P. Gradient dynamic programming for stochastic optimal control of multidimensional water resources systems. Water resources research. 1988, 24(8): 1345-1359.
[154]Bessa, M., Ponnambalam, K., Bogobowicz, A. Multi-reservoir systems optimization by the aggregation/decomposition methodology. in. 1994:192-196.
[155]Turgeon, A., Charbonneau, R. An aggregation-disaggregation approach to long-term reservoir management. Water Resources Research. 1998, 34(12): 3585-3594.
[156]Archibald, T., McKinnon, K., Thomas, L. An aggregate stochastic dynamic programming model of multireservoir systems. Water Resources Research. 1997, 33(2): 333-340.
[157]Martinez, L., Soares, S. Comparison between closed-loop and partial open-loop feedback control policies in long term hydrothermal scheduling. IEEE Transactions on Power Systems. 2002, 17(2): 330-336.
[158]Philbrick Jr, C. R., Kitanidis, P. K. Limitations of deterministic optimization applied to reservoir operations. Journal of Water Resources Planning and Management. 1999, 125(3): 135-142.
[159]Sivapragasam, C., Vasudevan, G., Vincent, P. Effect of inflow forecast accuracy and operating time horizon in optimizing irrigation releases. Water Resources Management. 2007, 21 (6): 933-945.
[160]曾勇红．仿射尺度算法及其在水电系统优化调度中的应用研究．[博士学位论文]．华中科技大学，2004．
[161]Rux, L. M. Incremental economic dispatch method for cascaded hydroelectric powerplants. IEEE Transactions on Power Systems. 1993, 8(3): 1266-1273.
[162]李彩林．水电站中长期优化调度与风险研究．[博士学位论文]．华中科技大学， 2007．
[163]Frank, M., Wolfe, P. An algorithm for quadratic programming. Naval Research Logistics Quarterly. 1956, 3 (1-2): 95-110.
[164]俞玉森，杨林锡，邓成梁等．数学规划的原理和方法．(第二版)．武汉：华中理工大学出版社，1993．
[165]Dantzig, G. B. Linear programming and extensions. Princeton Univ Pr, 1963.
[166]Dantzig, G. B., Wolfe, P. Decomposition principle for linear programs. Operations Research. 1960, 8(1): 101-111.
[167]刘治理，马光文，戴露．基于三层B／S结构的梯级水电厂中长期优化调度．计算机工程．2006，32(6)：240-242．
[168]涂扬举，马光文，刘治理等．基于三层B／S结构的水电站中长期优化调度系统．水力发电学报．2006，25(2)：90-93．
[169]付洁廉．水电系统长期优化调度及系统应用软件研究．[硕士学位论文]．华中科技大学，2006
[170]Aganagic, M., Mokhtari, S. Coordination between short-term and long-term resource scheduling. in: Proceedings of the American Power Conference. 1995, 57-2: 1719-1722.
[171]Marwali, M. K. C, Shahidehpour, S. M. Coordination between long-term and short-term generation scheduling with network constraints. IEEE Transactions on Power Systems. 2000, 15(3): 1161-1167.
[172] Dong, X., Zhao, Y., Yuan, J., et al. Coupling the long-term and short-term optimization models for reservoir operation, in: Proceedings of the International Symposium on Dams in the Societies of the 21st Century, ICOLD-SPANCOLD-Dams and Reservoirs, Societies and Environment in the 21st Century. 2006, 1:277-282.
[173] Batista, A., Suzuki, K., Kadota, A. Integrating long- and short-term reservoir operation models via stochastic and deterministic optimization: Case study in Japan. Journal of Water Resources Planning and Management. 2008, 134(5): 440-448.