Improved wind-driven optimization algorithm for the optimization of hydropower generation from a reservoir

Yin Liu,Shuanghu Zhang,Yunzhong Jiang,Zhongbo Zhang,Dan Wang,Qihao Gu

doi:10.2166/hydro.2021.174

Abstract

Abstract The improvement of reservoir operation optimization (ROO) can lead to comprehensive economic benefits as well as sustainable development of water resources. To achieve this goal, an algorithm named wind-driven optimization (WDO) is first employed for ROO in this paper. An improved WDO(IWDO) is developed by using a dynamic adaptive random mutation mechanism, which can avoid the algorithm stagnation at local optima. Moreover, an adaptive search space reduction (ASSR) strategy that aims at improving the search efficiency of all evolutionary algorithms is proposed. The application results of the Goupitan hydropower station show that IWDO is an effective and viable algorithm for ROO and is capable of obtaining greater power generation compared to the classic WDO. Moreover, it is shown that the ASSR strategy can improve the search efficiency and the quality of scheduling results when coupled with various optimization algorithms such as IWDO, WDO and particle swarm optimization.

Highlights

A major crisis of the 21st century is the huge water demand caused by the large population growth (Srinivasan et al 2012; Kummu et al 2016)
The best value, average value and standard deviation (SD) of power generation are shown in Table 4, from which it can be found that the SDs of wind-driven optimization (WDO) and particle swarm optimization (PSO) are zero, indicating that the standard algorithms are in good robustness; the SD of improved WDO (IWDO) is 0.01 and the SD of adaptive search space reduction (ASSR)-IWDO is 0.001, indicating that random mutation mechanism (RMM) will cause some deviation but ASSR can reduce that
The total power generation obtained by IWDO is higher than WDO, which demonstrates the improvement caused by RMM

Summary

Introduction

A major crisis of the 21st century is the huge water demand caused by the large population growth (Srinivasan et al 2012; Kummu et al 2016). Reservoir operation optimization (ROO) is employed for the regulation and management of water resources, and it can lead to comprehensive economic benefits. It is of great significance for meeting water and energy demands in the future (Tayebiyan et al 2016; Nair & Sasikumar 2019). NLP can solve the nonlinear problems but it has the limitation that the objective function and constraints must be differentiable and some approximations must be made to get the solution. This can lead to local optimum and poor convergence performance of NLP. The computational cost of DP is greatly increased with the

Methods

Results

Conclusion