机会约束规划下的梯级水电站短期优化调度策略  

A New Strategy for Short-term Scheduling Optimization of Cascade Hydro Plants Based on Chance-constrained Programming

作　　者：吴杰康[1] 朱建全[1] 

机构地区：[1]广西大学电气工程学院,广西壮族自治区南宁市530004

出　　处：《中国电机工程学报》2008年第13期41-46,共6页Proceedings of the CSEE

基　　金：国家自然科学基金项目(50767001);国家863高技术基金项目(2007AA04Z100);广西自然科学基金资助项目(桂科自0640028);广西壮族自治区研究生教育创新计划项目(20060808M32);广西高校百名中青年学科带头人资助计划项目(RC20060808002);广西壮族自治区教育厅资助项目(桂教科研[2005]47号)~~

摘　　要：以一定时期内可能实现的总的目标利润最大化为目标,在一定的置信水平的前提下满足约束条件,基于机会约束规划构建了一种新的的梯级水电站短期优化调度策略。模型全面分析了蓄水量、弃水量、前池水位、放水路水位、发电水头之间的关系,并考虑了电价、入库径流量、机组运行状况等不确定因素对梯级水电站短期优化调度问题的影响。利用粒子群算法算简单、鲁棒性好、可操作性强的优势,将其嵌入蒙特卡罗随机模拟对模型进行求解。算例说明了该方法可以根据电站的实际情况协调风险和利润这两个相互矛盾的指标,实现最优化决策。To maximize the possible total objective profit throughout a time period, a novel strategy for short-term scheduling optimization of cascade hydro plants is presented based on chance-constrained programming in which the constraints are met with a specified probability. The detailed representation of cascade hydro plants, which includes water volume, water inflow, water discharge, forebay elevation, tailrace elevation and effective water head, is studied in the proposed strategy. The uncertainties, such as water inflows, electricity prices and unit status are taken into account as well. The model presented is solved using a combination of particle swarm optimization （PSO） and Monte Carlo simulation because of the advantages of PSO such as simple concept, easy implementation and robustness. The results show that the two conflicting targets of profit and risk can be coordinated preferably according to the practical system, and the optimization of power output for cascade hydro plants is established by the developed method.

关 键 词：梯级水电站 短期优化调度 不确定性 机会约束规划 蒙特卡罗仿真 粒子群算法 

分 类 号：TM73[电气工程—电力系统及自动化]