Combined Aggregated Sampling Stochastic Dynamic Programming and Simulation-Optimization to Derive Operation Rules for Large-Scale Hydropower System

Abstract

Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time.

Mine water cooperative optimal scheduling based on improved genetic algorithm

“…In the first stage, the optimization model is simplified by using a random sampling dynamic programming method. In the second stage, the solution of the first stage is used as the initial population, and the genetic algorithm model is used to solve the optimal problem [14] . Some scholars use genetic algorithms to predict ecological water demand and establish an optimal allocation model for multi-source, multi-user water resources [15] .…”

Long-Term Optimal Dispatching of Hydropower Station Based on Controlling the Frequency of Reservoir Level Fluctuation

“…e solution of the hydropower station optimal operation model belongs to the constrained nonlinear programming problem. Many solving methods have been explored all over the world, including traditional algorithms such as linear programming [3], nonlinear programming [4], progressive optimization algorithm (POA) [5], dynamic programming (DP) [6], and intelligent evolutionary algorithms, such as genetic algorithm [7], particle swarm optimization [8], ant colony optimization [9], and the combination algorithm of the above methods [10][11][12].…”

Multi-Objective Optimizations of Hydroelectric Exploitation Using Dynamic Programs in Citarum Cascade Reservoirs

“…Reservoir operation problems occur due to the complex relationship between physical variables and hydrological uncertainty. Non-linear problems and stochastic variables can be easily solved by dynamic programs [8] in which the usage is limited by the curse of dimensionality for multi-reservoir operations [15,16].…”