Reservoir Scheduling Using a Multi-Objective Cuckoo Search Algorithm under Climate Change in Jinsha River, China

Abstract

Changes in rainfall and streamflow due to climate change have an adverse impact on hydropower generation reliability and scheduling of cascade hydropower stations. To estimate the impact of climate change on hydropower, a combination of climate, hydrological, and hydropower scheduling models is needed. Here, we take the Jinsha River as an example to estimate the impact of climate change on total power generation of the cascade hydropower stations and residual load variance of the power grid. These two goals are solved by applying an improved multi-objective cuckoo search algorithm, and a variety of strategies for the optimal dispatch of hydropower stations are adopted to improve the efficiency of the algorithm. Using streamflow prediction results of CMIP5 climate data, in conjunction with the Xinanjiang model, the estimated results for the next 30 years were obtained. The results indicated that the negative correlation between total power generation and residual load variance under the RCP 2.6 scenario was weaker than that under the RCP 8.5. Moreover, the average power generation and the average residual load variance in RCP 2.6 was significantly larger than that in RCP 8.5. Thus, reducing carbon emissions is not only beneficial to ecological sustainability, but also has a positive impact on hydropower generation. Our approaches are also applicable for cascade reservoirs in other river catchments worldwide to estimate impact of climate change on hydropower development.

Robust Scheduling of a Hybrid Hydro/Photovoltaic/Pumped-Storage System for Multiple Grids Peak-Shaving and Congestion Management

“…Research [12] combines simulated annealing with particle swarm optimization to improve the accuracy and efficiency of hourly generation scheduling for cascade hydropower plants with complex hydraulic coupling and head-dependent prohibited operating zones. [13] investigates the impact of climate change on total power generation in cascade hydropower stations. [14] proposes a decentralized monthly generation scheduling model for cascade hydropower plants in multiple time-scale markets.…”

Optimizing hydropower scheduling through accurate power load prediction: A practical case study

“…They compared the results with a classical scheme. Additionally, Yu et al [20] investigated the influence of climate and hydrology on hydropower station performance. They formulated a multi-objective optimization problem that considered the total power output and remaining load changes in the grid for the Jinsha River.…”

Hydropower Scheduling Under Missing Power Load Data: Optimizing Energy Efficiency and Navigation Performance

“…Yu et al addressed the impact of climate and hydrology on the performance of hydropower station by formulating a multi-objective optimization problem that considers the total power output of hydropower station and the remaining load changes in the grid. To solve this problem in the context of Jinsha River, they proposed an improved multi-objective cuckoo search algorithm and validated its performance against five state-of-the-art algorithms [18]. Kong et al introduced a marine predators algorithm to solve and compare with other algorithms with the objective of maximizing power generation and minimizing unsuitable ecology [19].…”