Determining Inflow Forecast Horizon for Reservoir Operation

Qiankun Zhao, Ximing Cai, Yu Li

Research output: Contribution to journalArticle

Abstract

A critical study issue to incorporate imperfect forecast in real-time reservoir operation is determining the forecast horizon. In this study, properties for the longest forecast horizon (LFH) and the effective forecast horizon (EFH) are derived from a multistage, deterministic optimization model for the operation of a single water supply reservoir with a concave benefit function. The LFH addresses the question of how long a forecast is sufficient to make an optimal reservoir release decision for the current stage if the effect of forecast uncertainty is not considered. The EFH represents a forecast horizon with the information for decision making as much as allowed by uncertainty effect control, which is set as prescribed decision reliability quantified by the error bound (i.e., the largest difference between the optimal release decisions made under any two inflow scenarios). The properties of LFH and EFH are used to specify the criteria and design the procedures for determining EFH and LFH. A hypothetical but typical case study is used to demonstrate the criteria and procedures. Both theoretical analysis and the case study results show that LFH and EFH are affected by multiple factors such as the reservoir capacity, inflow variability, forecast uncertainty, maximum allowable error bound, and ending storage estimate accuracy. LFH is longer with a larger capacity, smaller inflow variability, and smaller forecast uncertainty, and EFH is longer with smaller forecast uncertainty, larger error bound, and more accurate ending storage estimates.

Original languageEnglish (US)
Pages (from-to)4066-4081
Number of pages16
JournalWater Resources Research
Volume55
Issue number5
DOIs
StatePublished - May 2019

Fingerprint

inflow
forecast

ASJC Scopus subject areas

  • Water Science and Technology

Cite this

Determining Inflow Forecast Horizon for Reservoir Operation. / Zhao, Qiankun; Cai, Ximing; Li, Yu.

In: Water Resources Research, Vol. 55, No. 5, 05.2019, p. 4066-4081.

Research output: Contribution to journalArticle

Zhao, Qiankun ; Cai, Ximing ; Li, Yu. / Determining Inflow Forecast Horizon for Reservoir Operation. In: Water Resources Research. 2019 ; Vol. 55, No. 5. pp. 4066-4081.
@article{7d38af44d0be4eb3aa2371cd15939304,
title = "Determining Inflow Forecast Horizon for Reservoir Operation",
abstract = "A critical study issue to incorporate imperfect forecast in real-time reservoir operation is determining the forecast horizon. In this study, properties for the longest forecast horizon (LFH) and the effective forecast horizon (EFH) are derived from a multistage, deterministic optimization model for the operation of a single water supply reservoir with a concave benefit function. The LFH addresses the question of how long a forecast is sufficient to make an optimal reservoir release decision for the current stage if the effect of forecast uncertainty is not considered. The EFH represents a forecast horizon with the information for decision making as much as allowed by uncertainty effect control, which is set as prescribed decision reliability quantified by the error bound (i.e., the largest difference between the optimal release decisions made under any two inflow scenarios). The properties of LFH and EFH are used to specify the criteria and design the procedures for determining EFH and LFH. A hypothetical but typical case study is used to demonstrate the criteria and procedures. Both theoretical analysis and the case study results show that LFH and EFH are affected by multiple factors such as the reservoir capacity, inflow variability, forecast uncertainty, maximum allowable error bound, and ending storage estimate accuracy. LFH is longer with a larger capacity, smaller inflow variability, and smaller forecast uncertainty, and EFH is longer with smaller forecast uncertainty, larger error bound, and more accurate ending storage estimates.",
author = "Qiankun Zhao and Ximing Cai and Yu Li",
year = "2019",
month = "5",
doi = "10.1029/2019WR025226",
language = "English (US)",
volume = "55",
pages = "4066--4081",
journal = "Water Resources Research",
issn = "0043-1397",
publisher = "American Geophysical Union",
number = "5",

}

TY - JOUR

T1 - Determining Inflow Forecast Horizon for Reservoir Operation

AU - Zhao, Qiankun

AU - Cai, Ximing

AU - Li, Yu

PY - 2019/5

Y1 - 2019/5

N2 - A critical study issue to incorporate imperfect forecast in real-time reservoir operation is determining the forecast horizon. In this study, properties for the longest forecast horizon (LFH) and the effective forecast horizon (EFH) are derived from a multistage, deterministic optimization model for the operation of a single water supply reservoir with a concave benefit function. The LFH addresses the question of how long a forecast is sufficient to make an optimal reservoir release decision for the current stage if the effect of forecast uncertainty is not considered. The EFH represents a forecast horizon with the information for decision making as much as allowed by uncertainty effect control, which is set as prescribed decision reliability quantified by the error bound (i.e., the largest difference between the optimal release decisions made under any two inflow scenarios). The properties of LFH and EFH are used to specify the criteria and design the procedures for determining EFH and LFH. A hypothetical but typical case study is used to demonstrate the criteria and procedures. Both theoretical analysis and the case study results show that LFH and EFH are affected by multiple factors such as the reservoir capacity, inflow variability, forecast uncertainty, maximum allowable error bound, and ending storage estimate accuracy. LFH is longer with a larger capacity, smaller inflow variability, and smaller forecast uncertainty, and EFH is longer with smaller forecast uncertainty, larger error bound, and more accurate ending storage estimates.

AB - A critical study issue to incorporate imperfect forecast in real-time reservoir operation is determining the forecast horizon. In this study, properties for the longest forecast horizon (LFH) and the effective forecast horizon (EFH) are derived from a multistage, deterministic optimization model for the operation of a single water supply reservoir with a concave benefit function. The LFH addresses the question of how long a forecast is sufficient to make an optimal reservoir release decision for the current stage if the effect of forecast uncertainty is not considered. The EFH represents a forecast horizon with the information for decision making as much as allowed by uncertainty effect control, which is set as prescribed decision reliability quantified by the error bound (i.e., the largest difference between the optimal release decisions made under any two inflow scenarios). The properties of LFH and EFH are used to specify the criteria and design the procedures for determining EFH and LFH. A hypothetical but typical case study is used to demonstrate the criteria and procedures. Both theoretical analysis and the case study results show that LFH and EFH are affected by multiple factors such as the reservoir capacity, inflow variability, forecast uncertainty, maximum allowable error bound, and ending storage estimate accuracy. LFH is longer with a larger capacity, smaller inflow variability, and smaller forecast uncertainty, and EFH is longer with smaller forecast uncertainty, larger error bound, and more accurate ending storage estimates.

UR - http://www.scopus.com/inward/record.url?scp=85065994752&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85065994752&partnerID=8YFLogxK

U2 - 10.1029/2019WR025226

DO - 10.1029/2019WR025226

M3 - Article

AN - SCOPUS:85065994752

VL - 55

SP - 4066

EP - 4081

JO - Water Resources Research

JF - Water Resources Research

SN - 0043-1397

IS - 5

ER -