Next Article in Journal
Uncertainty Analysis of the Water Scarcity Footprint Based on the AWARE Model Considering Temporal Variations
Next Article in Special Issue
Long-Term Scheduling of Large-Scale Cascade Hydropower Stations Using Improved Differential Evolution Algorithm
Previous Article in Journal
Implementation of a Two-Source Model for Estimating the Spatial Variability of Olive Evapotranspiration Using Satellite Images and Ground-Based Climate Data
Previous Article in Special Issue
Evaluating Regime Change of Sediment Transport in the Jingjiang River Reach, Yangtze River, China
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Water 2018, 10(3), 340; https://doi.org/10.3390/w10030340

Real-Time Flood Control by Tree-Based Model Predictive Control Including Forecast Uncertainty: A Case Study Reservoir in Turkey

1
Institute of Hydraulic Engineering and Water Resources Management, University of Duisburg-Essen, 45141 Essen, Germany
2
Department of Civil Engineering, Anadolu University, 26555 Eskişehir, Turkey
3
Deltares, Operational Water Management, Deltares, Rotterdamseweg 185, 26 MH Delft, The Netherlands
4
KISTERS AG, Business Unit, Pascalstraße, 52076 Aachen, Germany
*
Author to whom correspondence should be addressed.
Received: 18 December 2017 / Revised: 8 March 2018 / Accepted: 9 March 2018 / Published: 19 March 2018
(This article belongs to the Special Issue Adaptive Catchment Management and Reservoir Operation)
Full-Text   |   PDF [7195 KB, uploaded 3 May 2018]   |  

Abstract

Optimal control of reservoirs is a challenging task due to conflicting objectives, complex system structure, and uncertainties in the system. Real time control decisions suffer from streamflow forecast uncertainty. This study aims to use Probabilistic Streamflow Forecasts (PSFs) having a lead-time up to 48 h as input for the recurrent reservoir operation problem. A related technique for decision making is multi-stage stochastic optimization using scenario trees, referred to as Tree-based Model Predictive Control (TB-MPC). Deterministic Streamflow Forecasts (DSFs) are provided by applying random perturbations on perfect data. PSFs are synthetically generated from DSFs by a new approach which explicitly presents dynamic uncertainty evolution. We assessed different variables in the generation of stochasticity and compared the results using different scenarios. The developed real-time hourly flood control was applied to a test case which had limited reservoir storage and restricted downstream condition. According to hindcasting closed-loop experiment results, TB-MPC outperforms the deterministic counterpart in terms of decreased downstream flood risk according to different independent forecast scenarios. TB-MPC was also tested considering different number of tree branches, forecast horizons, and different inflow conditions. We conclude that using synthetic PSFs in TB-MPC can provide more robust solutions against forecast uncertainty by resolution of uncertainty in trees. View Full-Text
Keywords: reservoir operation; multi-stage stochastic optimization; TB-MPC; flood control; real-time control reservoir operation; multi-stage stochastic optimization; TB-MPC; flood control; real-time control
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Uysal, G.; Alvarado-Montero, R.; Schwanenberg, D.; Şensoy, A. Real-Time Flood Control by Tree-Based Model Predictive Control Including Forecast Uncertainty: A Case Study Reservoir in Turkey. Water 2018, 10, 340.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top