摘要
针对逐月频率生态径流计算法精度较低的不足,通过数据预处理筛选替换历史水文数据中的极限、误差较大的数据,提高小水电群推荐生态径流值的精度和合理性。同时在小水电群生态环境保护中,不同于以往将适宜生态径流值作为优化调度模型的约束条件,引入适宜生态径流值作为惩罚因子,为小水电设置合理的生态罚款,从经济效益的角度有效地减少电站发电放水计划对下游生态所造成的破坏。建立通用优化调度模型,用基于动态邻域结构的PSO算法求解,结果表明新模型配合新适宜生态径流能让发电流量更加的平缓、接近河道自然径流过程,保护生态环境,提高水电群总收益。
This paper presents a method to filter and replace the erroneous extreme data in historical series of hydrological data by data preprocessing and screening to improve the accuracy of monthly frequency calculations for instream ecological flow. We formulate the problem from the economic point of view and treat the ecological flow as a penalty factor in protection of the ecological environment of rural cascaded hydropower stations. This is different from previous studies that take the ecological flow as a constraint or one of the multi-objectives in an optimal scheduling model. We have developed a new general model that is solved by DNMPSO, and applied it to the Lushui River cascaded stations in Jiangxi. The results show that this new model produces schemes that can better protect the downstream through restoration of ecological environment and also improve the overall benefits of the cascaded stations.
出处
《水力发电学报》
EI
CSCD
北大核心
2015年第7期45-56,共12页
Journal of Hydroelectric Engineering
基金
"十二五"国家科技支撑计划(2012BAD10B01)
国家自然科学基金(61379123)
浙江省基金(LQ14F020005)
关键词
小水电群
优化调度
生态惩罚
混联梯级
改进粒子群算法
small hydropower stations
optimal scheduling
ecological punishment
hybrid cascade
improved particle swarm optimization
