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

Managing a multi-reservoir system is complicated, due to conflicting interests among various objectives. This study proposes an optimization-based approach for the operations of a multi-reservoir system. An advanced real-time control technique, Model Predictive Control (MPC), is adopted to control a multi-reservoir system with two control objectives, i.e., flood mitigation and water conservation. The case study area is the Sittaung River basin in Myanmar, where the current reservoir operating rule needs to be improved for a more effective operation. A comparison between an MPC-based operation and the current operation is presented by using performance indicators. The result shows a reduction of the system’s vulnerability by 0.9 percent using MPC. Due to the physical constraint of the reservoirs, it is impossible to completely eliminate the flood risk at Taungoo City during high inflow events. However, the results indicate that the potential flood risk can be mitigated by improving the current operating rule.

Section: Discussionmentioning

confidence: 99%

Flood Mitigation through Optimal Operation of a Multi-Reservoir System by Using Model Predictive Control: A Case Study in Myanmar

Lin

Rutten

Tian

2018

“…Wen et al [29] proposed an improved differential evolution algorithm to solve the optimal operation model of the long-term scheduling of large-scale cascade hydropower stations. Uysal et al [30] used probabilistic streamflow forecasts with a lead time of 48 hours to improve real-time flood control solutions. In the short-term operation of hydropower plants, Ji et al [31] proposed a new progressive optimality algorithm to consider the interactions between two cascaded reservoirs.…”

Section: Optimal Reservoir Operationmentioning

confidence: 99%

Recent Advances in Adaptive Catchment Management and Reservoir Operation

Zhang

2019

This editorial introduces the latest research advances in the special issue on catchment management and reservoir operations. River catchments and reservoirs play a central role in water security, community wellbeing and social-economic prosperity, but their operators and managers are under increasing pressures to meet the challenges from population growth, economic activities and changing climates in many parts of the world. This challenge is tackled from various aspects in the 27 papers included in this special issue. A synthesis of these papers is provided, focusing on four themes: reservoir dynamics and impacts, optimal reservoir operation, climate change impacts, and integrated modelling and management. The contributions are discussed in the broader context of the field and future research directions are identified to achieve sustainable and resilient catchment management.

“…Not all the reasons that explain this increasing variability are brand new. Because of that, there is a consequently strong need to have powerful and reliable analytical methods to build accurate models that reproduce and forecast the future hydrological behavior of a river system [19][20][21][22][23][24][25]. Also, there is a growing necessity to design analytical strategies that allow: (a) an increase of knowledge on temporal Indeed, the annual average rainfall in Spain presents a latitudinal decrease pattern, from wet north-west (around 2000 mm), to dry south-east with less than 200 mm [28,38].…”

Section: Introductionmentioning

confidence: 99%

Causal Reasoning: Towards Dynamic Predictive Models for Runoff Temporal Behavior of High Dependence Rivers

Molina¹,

Zazo

Martín³

2019

Nowadays, a noteworthy temporal alteration of traditional hydrological patterns is being observed, producing a higher variability and more unpredictable extreme events worldwide. This is largely due to global warming, which is generating a growing uncertainty over water system behavior, especially river runoff. Understanding these modifications is a crucial and not trivial challenge that requires new analytical strategies like Causality, addressed by Causal Reasoning. Through Causality over runoff series, the hydrological memory and its logical time-dependency structure have been dynamically/stochastically discovered and characterized. This is done in terms of the runoff dependence strength over time. This has allowed determining and quantifying two opposite temporal-fractions within runoff: Temporally Conditioned/Non-conditioned Runoff (TCR/TNCR). Finally, a successful predictive model is proposed and applied to an unregulated stretch, Mijares river catchment (Jucar river basin, Spain), with a very high time-dependency behavior. This research may have important implications over the knowledge of historical rivers´ behavior and their adaptation. Furthermore, it lays the foundations for reaching an optimum reservoir dimensioning through the building of predictive models of runoff behavior. Regarding reservoir capacity, this research would imply substantial economic/environmental savings. Also, a more sustainable management of river basins through more reliable control reservoirs’ operation is expected to be achieved.