The third-monthly (about 10 days in a time-step) hydropower scheduling, typically a challenging nonlinear optimization, is one of the essential tasks in a power system with operational storage hydropower reservoirs. This work formulates the problem into quadratic programming (QP), which is solved successively, with the linearization updated on the nonlinear constraint of the firm hydropower yield from all the cascaded hydropower reservoirs. Notably, the generating discharge is linearly concaved with two planes, and the hydropower output is defined as a quadratic function of reservoir storage, release, and generating discharge. The application of the model and methods to four cascaded hydropower reservoirs on the Jinsha River reveals several things: the successive quadratic programming (SQP) presented in this work can derive results consistent with those by the dynamic programming (DP), typically with the difference in water level within 0.01m; it has fast convergence and computational time increasing linearly as the number of reservoirs increases, with the most significant improvement in the objective at the second iteration by about 20%; and it is capable of coordinating the cascaded reservoir very well to sequentially maximize the firm hydropower yield and the total hydropower production.

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.

The uncertainty of natural inflows and market behavior challenges ensuring a reliable power balance in hydropower-dominated electricity markets. This study proposes a novel framework integrating hourly load balancing on typical days into a monthly scheduling model solved with Gurobi11.0.1 to evaluate demand-met reliability across storage and inflow states. By employing total storage as a system state to reduce dimensional complexity and simulating future runoff scenarios based on current inflows, the method performs multi-year statistical simulations to assess reliability over the following year. Applied to a system of 39 hydropower reservoirs in China, the case studies of present models and procedures suggest: (1) controlling reservoir storage levels during the dry season is crucial for ensuring the power demand-met rate in the following year, with May being the most critical month; (2) the power demand-met rate does not monotonically increase with higher storage levels—there is an optimal storage level that maximizes the demand-met rate; and (3) June and October offer the greatest flexibility in storage adjustment to achieve the highest demand-met reliability.