气候变化领域集成服务门户(CCPortal): Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management

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DOI	10.5194/hess-23-2735-2019
	Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management
	Arsenault R.; Côté P.
发表日期	2019
ISSN	1027-5606
起始页码	2735
结束页码	2750
卷号	23 期号:6
英文摘要	This paper presents an analysis of the effects of biased extended streamflow prediction (ESP) forecasts on three deterministic optimization techniques implemented in a simulated operational context with a rolling horizon test bed for managing a cascade of hydroelectric reservoirs and generating stations in Québec, Canada. The observed weather data were fed to the hydrological model, and the synthetic streamflow subsequently generated was considered to be a proxy for the observed inflow. A traditional, climatology-based ESP forecast approach was used to generate ensemble streamflow scenarios, which were used by three reservoir management optimization approaches. Both positive and negative biases were then forced into the ensembles by multiplying the streamflow values by constant factors. The optimization method's response to those biases was measured through the evaluation of the average annual energy generation in a forward-rolling simulation test bed in which the entire system is precisely and accurately modelled. The ensemble climate data forecasts, the hydrological modelling and ESP forecast generation, optimization model, and decision-making process are all integrated, as is the simulation model that updates reservoir levels and computes generation at each time step. The study focussed on one hydropower system both with and without minimum baseload constraints. This study finds that the tested deterministic optimization algorithms lack the capacity to compensate for uncertainty in future inflows and therefore place the reservoir levels at greater risk to maximize short-term profit. It is shown that for this particular system, an increase in ESP forecast inflows of approximately 5 % allows managing the reservoirs at optimal levels and producing the most energy on average, effectively negating the deterministic model's tendency to underestimate the risk of spilling. Finally, it is shown that implementing minimum load constraints serves as a de facto control on deterministic bias by forcing the system to draw more water from the reservoirs than what the models consider to be optimal trajectories. © 2019 American Society of Civil Engineers (ASCE). All rights reserved.
语种	英语
scopus关键词	Climate models; Decision making; Electric substations; Equipment testing; Forecasting; Hydroelectric power; Hydrology; Optimization; Reservoirs (water); Stream flow; Decision making process; Deterministic modeling; Deterministic optimization; Electricity production; Hydroelectric reservoirs; Hydrological modelling; Hydropower reservoirs; Streamflow prediction; Reservoir management; decision making; detection method; electricity; ensemble forecasting; hydroelectric power; hydrological modeling; reservoir; small scale hydropower; streamflow; Canada; Quebec [Canada]
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159658
作者单位	Arsenault, R., Department of Construction Engineering, École de Technologie Supérieure, Montréal, H3C 1K3, Canada, Quebec Power Operations, Rio Tinto, Jonquière, G7S 4R5, Canada; Côté, P., Quebec Power Operations, Rio Tinto, Jonquière, G7S 4R5, Canada
推荐引用方式 GB/T 7714	Arsenault R.,Côté P.. Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management[J],2019,23(6).
APA	Arsenault R.,&Côté P..(2019).Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management.Hydrology and Earth System Sciences,23(6).
MLA	Arsenault R.,et al."Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management".Hydrology and Earth System Sciences 23.6(2019).