This paper presents a multireservoir flood control model that incorporates operating rules to alleviate the risk that results from traditional flood control models. The model is accurately reformulated into one that can be solved with the mixed integer linear programming (MILP) and approximated with a two‐stage linear programming (TSLP) to speed up the solution by excluding all the binary variables. A scroll decision‐making strategy is proposed by assuming only a few days of future inflows being predicted with certainty. With 10 historical and 30 designed floods in different magnitudes, the models and methods are applied to the Yangtze River basin, which includes 11 cascade reservoirs. The results on average show the MILP reduces the rule violation by 17%, while the TSLP is more than 150 times faster in CPU time. Excluding one of the reservoirs from the simulation reveals that the Three Gorges (TG) contributes the most in flood detention, accounting for 76% on average for once‐in‐a‐century floods. Interestingly, with the scroll decision‐making strategy implemented, the change from 3 to 7 days of certain forecasting has the most significant reduction in both the rule violation and flood detention, indicating that the forecasting accuracy of more recent inflows is more valuable for flood control.

This study integrates genetic algorithms with simulation programs, applying the genetic algorithm’s (GA) fitness calculation within the simulation to reduce complexity and significantly improve the efficiency of the optimization process. Additionally, the simulation introduces the concept of “Field Leveling” (FL), utilizing a push–pull strategy to explore more space for absorbing and utilizing unnecessary spillage for energy generation, thereby maximizing electricity production and ensuring optimal reservoir scheduling. Two methods are provided, namely the field-leveling genetic algorithms GAFL1 and GAFL2. GAFL1 involves only pushing and does not include a push–pull process; thus, it cannot optimize spillage. On the other hand, GAFL2 implements a complete push–pull strategy, continuously exploring additional space to absorb and utilize unnecessary spillage. Both GAFL1 and GAFL2 achieved reasonable results; specifically, compared to SQP, GAFL1 improved firm yield by 8.3%, spillage increased by 2.2 times, and total energy decreased by 1.2%. GAFL2, building on the basis of GAFL1, effectively reduces spillage under all hydrological conditions without affecting the highest priority of stable output. However, the impact of reducing spillage on energy generation is not consistent; in wet and dry years, reducing spillage increases energy generation. However, in normal years, a reduction in spillage corresponds with decreased energy generation.

Global climate change and growing water demand exacerbate the imbalances in reservoir resource allocation, necessitating advanced frameworks that move beyond static valuation methods. Traditional valuation methods, constrained by static or homogenized assumptions, fail to capture the spatiotemporal heterogeneity and dynamic trade‐offs inherent in cascade reservoir operations. To address this gap, this study develops a novel marginal value framework based on shadow pricing to assess the marginal values of multidimensional resources—water, storage capacity, and turbine capacity—in weekly hydropower scheduling. The key methodological contribution is overcoming the duality gap challenge in mixed‐integer linear programming (MILP) models by proposing two practical methods: Method‐I fixes integer variables to derive dual multipliers via linear programming, while Method‐II computes shadow prices through perturbation analysis. Validated on 26 cascaded reservoirs in Yunnan, China, both methods yield consistent results, with Method‐I demonstrating superior computational efficiency. Key findings reveal that: (a) irrigation water values in dry seasons and upstream regions exceed wet seasons and downstream by 1.11–7.34 times; (b) Nuozhadu Reservoir's storage capacity shadow price peaks in week 44, signaling flood control‐power generation trade‐offs; (c) Wunonglong and Dachaoshan exhibit the highest marginal turbine capacity values for spillage reduction; and (d) reserve capacity costs surge by 32%–45% in weeks 36–37. This work bridges the fields of resource economics and hydraulic engineering, providing actionable insights for dynamic water pricing, infrastructure investment prioritization, and seasonal ancillary service markets.