Fuzzy multiobjective models for optimal operation of a hydropower system

Teegavarapu, Ramesh S. V.; Ferreira, André Rodrigues; Simonović́, Slobodan P.

doi:10.1002/wrcr.20224

Cited by 19 publications

(6 citation statements)

References 44 publications

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“…Fuzzy logic was identified as an appropriate tool to address the uncertainty due to imprecision in defining the goals of the stakeholders [29]. In this context, fuzzy water allocation models to address the uncertainty due to imprecision in defining the goals of the reservoir users has been widely used all over the world [46] and in India [47][48][49]. A typical fuzzy optimization model for reservoir operation works on specifying the goals of the users as fuzzy membership functions and the mathematical formulation of a typical reservoir operation as a water quantity control model, following Rehana and Mujumdar [4], which can be expressed as follows:…”

Section: Reservoir Operation and Associated Uncertaintiesmentioning

confidence: 99%

Uncertainty Quantification in Water Resource Systems Modeling: Case Studies from India

Rehana

Rajulapati

Ghosh

et al. 2020

Water

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Regional water resource modelling is important for evaluating system performance by analyzing the reliability, resilience and vulnerability criteria of the system. In water resource systems modelling, several uncertainties abound, including data inadequacy and errors, modeling inaccuracy, lack of knowledge, imprecision, inexactness, randomness of natural phenomena, and operational variability, in addition to challenges such as growing population, increasing water demands, diminishing water sources and climate change. Recent advances in modelling techniques along with high computational capabilities have facilitated rapid progress in this area. In India, several studies have been carried out to understand and quantify uncertainties in various basins, enumerate large temporal and regional mismatches between water availability and demands, and project likely changes due to warming. A comprehensive review of uncertainties in water resource modelling from an Indian perspective is yet to be done. In this work, we aim to appraise the quantification of uncertainties in systems modelling in India and discuss various water resource management and operation models. Basic formulation of models for probabilistic, fuzzy and grey/inexact simulation, optimization, and multi-objective analyses to water resource design, planning and operations are presented. We further discuss challenges in modelling uncertainties, missing links in integrated systems approach, along with directions for future.

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Section: Reservoir Operation and Associated Uncertaintiesmentioning

confidence: 99%

Uncertainty Quantification in Water Resource Systems Modeling: Case Studies from India

Rehana

Rajulapati

Ghosh

et al. 2020

Water

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“…Fontane et al () employed stochastic DP to quantify optimal monthly releases for a 12 month period in terms of hydropower generation, flood control, water supply, and recreational demands. Using a GA, Teegavarapu et al () analyzed the trade‐offs between power generation and downstream water quality using a simplistic one‐dimensional decay process on a daily timescale, Chen et al () performed daily and hourly reservoir system scheduling subject to fish flow and other competing constraints, and Liu et al () incorporated minimization of flood risk on a daily time step. These applications all assumed a well‐mixed system or were performed in one spatial dimension.…”

Section: Reservoir Optimizationmentioning

confidence: 99%

Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High‐Fidelity Hydrodynamics and Water Quality Model

Shaw

Sawyer

LeBoeuf

et al. 2017

Water Resources Research

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Hydropower operations optimization subject to environmental constraints is limited by challenges associated with dimensionality and spatial and temporal resolution. The need for high‐fidelity hydrodynamic and water quality models within optimization schemes is driven by improved computational capabilities, increased requirements to meet specific points of compliance with greater resolution, and the need to optimize operations of not just single reservoirs but systems of reservoirs. This study describes an important advancement for computing hourly power generation schemes for a hydropower reservoir using high‐fidelity models, surrogate modeling techniques, and optimization methods. The predictive power of the high‐fidelity hydrodynamic and water quality model CE‐QUAL‐W2 is successfully emulated by an artificial neural network, then integrated into a genetic algorithm optimization approach to maximize hydropower generation subject to constraints on dam operations and water quality. This methodology is applied to a multipurpose reservoir near Nashville, Tennessee, USA. The model successfully reproduced high‐fidelity reservoir information while enabling 6.8% and 6.6% increases in hydropower production value relative to actual operations for dissolved oxygen (DO) limits of 5 and 6 mg/L, respectively, while witnessing an expected decrease in power generation at more restrictive DO constraints. Exploration of simultaneous temperature and DO constraints revealed capability to address multiple water quality constraints at specified locations. The reduced computational requirements of the new modeling approach demonstrated an ability to provide decision support for reservoir operations scheduling while maintaining high‐fidelity hydrodynamic and water quality information as part of the optimization decision support routines.

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“…By evaluating the fitness of each individual, an optimal one is finally suggested as the solution of the optimization model. In recent years, GAs have been successfully used in numerous reservoir operation and water resource management studies (e.g., Bai et al, 2015;Teegavarapu et al, 2013;Yin and Yang, 2013). Therefore, a GA is chosen as the optimization model solution method in this study.…”

Section: Optimization Model Solutionmentioning

confidence: 99%

“…New multi-objective optimization approaches were developed as a result of multiple reservoir operation targets (Adeyemo, 2011;Kurek and Ostfeld, 2013;Reddy and Kumar, 2007). Some creative operating approaches and rules were proposed for decision makers to resolve the interest conflicts of multiple stakeholders (Kerachian and Karamouz, 2006;Teegavarapu et al, 2013). These studies provided useful tools for directing reservoir operation and planning for downstream water quality protection.…”

Section: Introductionmentioning

confidence: 99%

Hydrological management for improving nutrient assimilative capacity in plant-dominated wetlands: A modelling approach

Yang

Yin

et al. 2016

Journal of Environmental Management

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Wetland eutrophication is a global environmental problem. Besides reducing pollutant emissions, improving nutrient assimilative capacity in wetlands is also significant for preventing eutrophication. Hydrological management can improve nutrient assimilative capacity in wetlands through physical effects on the dilution capacity of water body and ecological effects on wetland nutrient cycles. The ecological effects are significant while were rarely considered in previous research. This study focused on the ecological effects of hydrological management on two crucial nutrient removal processes, plant uptake and biological denitrification, in plant-dominated wetlands. A dual-objective optimization model for hydrological management was developed to improve wetland nitrogen and phosphorus assimilative capacities, using upstream reservoir release as water regulating measure. The model considered the interactions between ecological processes and hydrological cycles in wetlands, and their joint effects on nutrient assimilative capacity. Baiyangdian Wetland, the largest freshwater wetland in northern China, was chosen as a case study. The results found that the annual total assimilative capacity of nitrogen (phosphorus) was 4754 (493) t under the optimal scheme for upstream reservoir operation. The capacity of nutrient removal during the summer season accounted for over 80% of the annual total removal capacity. It was interesting to find that the relationship between water inflow and nutrient assimilative capacity in a plant-dominated wetland satisfied a dose-response relationship commonly describing the response of an organism to an external stressor in the medical field. It illustrates that a plant-dominated wetland shows similar characteristics to an organism. This study offers a useful tool and some fresh implications for future management of wetland eutrophication prevention.

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Fuzzy multiobjective models for optimal operation of a hydropower system

Cited by 19 publications

References 44 publications

Uncertainty Quantification in Water Resource Systems Modeling: Case Studies from India

Uncertainty Quantification in Water Resource Systems Modeling: Case Studies from India

Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High‐Fidelity Hydrodynamics and Water Quality Model

Hydrological management for improving nutrient assimilative capacity in plant-dominated wetlands: A modelling approach

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