Joint Reservoir Scheduling Model

Reservoir flood control scheduling is a challenging optimization task, particularly due to the complexity of various constraints. This paper proposes an innovative algorithm design approach to

The rapid advancement of computer technology has revolutionized the way reservoir scheduling is managed in traditional water conservation fields. In order to address the complexities posed by large reservoir groups, which can make joint scheduling models difficult to solve and decision-making arduous, this study introduces the information physics system and proposes a novel approach for

To do so, we proposed a multi-stage joint optimization model for a scheduling and allocation model that takes into account key indicators from the water source area and the multi-dimensional target requirements of the receiving area. In particular, we considered the water source and receiving areas of the HTWD project.

1 Introduction. Joint operation is essential in order to improve the operational efficiency or increase the overall revenue from a system of multiple reservoirs Barros et al., 2003 Teegavarapu and Simonovic, 2002 Xu et al., 2012.This is because joint operation takes advantage of the inflow variation in each reservoir and optimizes the inflow complementarity among the reservoirs.

Multi-reservoir joint scheduling coordinates the operation of various reservoirs to optimize water resource allocation and flood management across the entire watershed. This approach more effectively controls flood risks and ensures water security. C. Research on joint impoundment dispatching model for cascade reservoir. Water Resour. Manag

This enables more effective guidance of the agent's learning process. Ind this paper, a joint scheduling algorithm for cascaded hydropower reservoirs based on the combination of SAC and Evolutionary HER EVHER-SAC is proposed to solve the above optimization model. The algorithm is applicable for the joint scheduling of cascade hydropower

A stochastic model of inflow floods and a dynamic capacity flood regulation model for the Three Gorges Reservoir TGR were established, and the most dangerous inflow floods for downstream flood control were generated. 22 reservoirs were included in a joint scheduling scheme in 2020 see Table 1. These reservoirs, which control an area of

In solving the joint optimal operation problem of reservoir groups, traditional optimization methods suffer from the defects of quotdimension disasterquot, premature convergence, and low efficiency. In this paper, an improved dynamic programming DP method is proposed, which reduces the dimension of the DP by using dynamic water level limits and a variable discrete mechanism. This approach

Finally, an offline real-time optimization model is built for the scheduling of cascade hydropower reservoirs. The robustness of the model is evaluated through 300 Monte Carlo simulations. Keywords joint scheduling, cascade reservoir system, deep reinforcement learning, water level control, Soft Actor-Critic algorithm. Suggested Citation

The simulated scheduling experiments involved retrieving hydrological data from the five reservoirs for the period from April 15, 2019 to October 31, 2019. According to the scheduling model, a total of 24 scheduling operations were executed during the flood season, resulting in a cumulative water transfer of 54.38 million m 3.