Dynamic Programming (DP) is one of the most classical methods adopted for reservoir operation. It reduces the computational efforts of complex high-dimensional problems by piecewise dimensionality reduction and provides the global optimums of the problems, but it suffers the “curse of dimensionality”. Progressive Optimality Algorithm (POA) has been used repeatedly in reservoir operation studies during last decades because it alleviates the “curse of dimensionality” of DP and has good convergence and extensive applicability. Nonetheless, the POA encounters two difficulties in multireservoir operation applications. One is the transfer interrupt problem that makes the search procedure hard to achieve free allocation of water between two non-adjacent stages, and another is the dimensionality problem that leads to a low convergence rate. In order to overcome these deficiencies, this study made some enhancements to the POA and proposed a hybrid approach combining the Enhanced POA and DP (EPOA-DP) for long-term operation of cascade reservoir systems. In EPOA-DP, the EPOA is employed to improve the quality of the solutions and DP is used to reduce the computational effort of the two-stage problem solution. The proposed approach was tested using a real-world four-reservoir cascade system and a ten-reservoir benchmark test example. The results demonstrated that it outperforms the POA both in computational time and quality of the solution.

动态规划（DP）是储层作业采用的最经典的方法之一。它通过分段降维减少了复杂的高维问题的计算工作量，并提供了问题的全局最优值，但是却遭受了“维数的诅咒”。渐进最优算法（POA）在过去的几十年中被反复用于油藏运营研究中，因为它减轻了DP的“维数诅咒”，并且具有良好的收敛性和广泛的适用性。然而，在多储层操作应用中，POA遇到两个困难。一个是传递中断问题，该问题使搜索过程难以在两个不相邻的阶段之间实现水的自由分配，另一个是导致收敛速度低的维数问题。为了克服这些不足，本研究对POA进行了一些增强，并提出了一种将增强的POA和DP（EPOA-DP）结合起来的混合方法，用于梯级水库系统的长期运行。在EPOA-DP中，EPOA用于提高解决方案的质量，而DP用于减少两阶段问题解决方案的计算量。使用实际的四水库级联系统和十水库基准测试示例对提出的方法进行了测试。结果表明，它在计算时间和解决方案质量上均优于POA。EPOA用于提高解决方案的质量，而DP用于减少两阶段问题解决方案的计算量。使用实际的四水库级联系统和十水库基准测试示例对提出的方法进行了测试。结果表明，它在计算时间和解决方案质量上均优于POA。EPOA用于提高解决方案的质量，而DP用于减少两阶段问题解决方案的计算量。使用实际的四水库级联系统和十水库基准测试示例对提出的方法进行了测试。结果表明，它在计算时间和解决方案质量上均优于POA。