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伴随水电规模的扩大,水电站群优化调度的计算量不断增加,需要探求新的方法。在分析离散微分动态规划(discrete differentiation and dynamic programming,DDDP)算法的基础上,提出了基于分治模式的梯级水电站长期优化调度的细粒度并行离散微分动态规划(parallel discrete differentiation and dynamic programming,PDDDP)方法,并以澜沧江梯级的6个电站系统长期优化调度问题为应用实例,在多核计算环境下进行验证。结果表明,多核环境下的PDDDP方法简便易行,能充分利用闲置计算资源、大幅度提高优化调度的计算效率,是解决大规模复杂水电系统调度的高效和实用方法。
With the expansion of hydropower, the computation of hydropower station group optimization scheduling is increasing, and new methods need to be explored. Based on the analysis of the DDDP algorithm, this paper proposed a parallel discrete differentiation and dynamic programming (PDDDP) scheme for long-term optimal scheduling of cascaded hydropower stations based on divide-and-conquer mode. Method, and the long-term optimal scheduling problem of six power station systems in the Lancangjiang cascade is taken as an example to verify the multi-core computing environment. The results show that the PDDDP method in multi-core environment is simple and easy, which can make full use of idle computing resources and greatly improve the computational efficiency of optimal scheduling. It is an efficient and practical method to solve large-scale complex hydropower system scheduling.