Application of Hybrid Particle Swarm Optimization Algorithm in the Optimal Water Operation of Cascade Reservoirs in Dry Season

Sen Wang,Fang Yang,Shanzong Li,Zhipeng Ma,Ye Shi

doi:10.1051/matecconf/201824601064

Abstract

In this paper, a hybrid particle swarm optimization (HPSO) algorithm is proposed to solve the problem of optimal water operation of cascade reservoirs in dry season. Based on the basic particle swarm optimization (PSO) algorithm, chaos algorithm is introduced to traverse the search space to generate the initial population and improve the global searching ability of the algorithm. A self-adaptive inertial weighting method based on optimized inertial weighting coefficient is adopted to improve the ability of particle individual search and avoid local optimum. The proposed algorithm is applied to the optimal water operation in dry season of cascade reservoirs on the mainstream of Xijiang River. The results show that the HPSO algorithm can effectively improve the guarantee degree of ecological flow and suppressing salinity flow in the control reach of Wuzhou station under different typical dry year scenarios.

Highlights

Nonlinear programming (NLP), dynamic programming (DP), progressive optimal algorithm (POA), et al are commonly used in the optimal operation of cascade reservoirs [1,2,3]
These methods are more or less problematic, such as follows: The NLP method needs to simplify the original problem, which will reduce the precision of optimization results; In the process of optimization solution, the precision of the DP method is limited by the number of discrete points
With the rapid development of computer technology, more and more heuristic intelligent algorithms are applied to reservoir optimal operation [4,5], such as genetic algorithm (GA), simulated annealing algorithm (SA) and ant colony optimization algorithm (ACO)

Summary

Introduction

Nonlinear programming (NLP), dynamic programming (DP), progressive optimal algorithm (POA), et al are commonly used in the optimal operation of cascade reservoirs [1,2,3]. These methods are more or less problematic, such as follows: The NLP method needs to simplify the original problem, which will reduce the precision of optimization results; In the process of optimization solution, the precision of the DP method is limited by the number of discrete points. The accuracy of the results obtained from the case analysis is satisfactory

Objective function

Basic PSO algorithm

Algorithm flow for HPSO

Case study

Findings

Conclusions