长江上游巨型水库群联合蓄水调度研究

以长江上游30座水库巨型水库群为研究对象,建立提前蓄水多目标联合优化调度模型,采用分区策略、大系统聚合分解、参数模拟优化方法和并行逐次逼近寻优算法求解。研究结果表明:所提模型框架可较好地解决巨型水库群联合蓄水优化调度问题;智能算法对于复杂约束的多目标优化问题可产生大量非劣解;Pareto前沿分布范围均匀且广泛,可供决策者灵活调度。与原设计方案相比,在防洪风险得到控制的前提下,通过水库群提前蓄水联合优化调度,水库总蓄满率由90.40%增加到94.42%,年均增发电量76.5亿kW·h(+3.76%),经济社会效益显著。

Joint impoundment operation of mega reservoir groups imposed of 30 reservoirs in upper Yangtze River

A joint multi-objective reservoir impoundment framework is proposed and optimized through district division strategy, aggregation-decomposition and parameter-simulation-optimization approaches, and parallel progressive optimization algorithm. The mega groups imposed of 30 reservoirs in the upper Yangtze River were selected as case study. Application results demonstrate that the proposed model and framework can solve the complex problem of optimal operation of joint impoundment of large reservoir groups. Intelligent algorithm can produce a large number of non-inferior solutions for multi-objective optimization problems with complex constraints. The distribution of Pareto frontiers is uniform and extensive, providing flexible scheduling for decision makers. Compared with the original impoundment operation rule, the optimal solution can enhance total impoundment efficiency from 90.40% to 94.42% and increase 7.65 billion kWh (3.76%) power generation annually under controllable flood risk.