Real-time optimal water allocation for daily hydropower generation from the Vanderkloof dam, South Africa

Olofintoye, Oluwatosin; Otieno, Fred; Adeyemo, Josiah

doi:10.1016/j.asoc.2016.05.018

Cited by 20 publications

(7 citation statements)

References 14 publications

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“…Many papers are focused mainly on providing accurate predictions of river flow/inflow parameters focus on its importance to hydropower plants and reservoir operation, and ANN is the most applied tool to achieve this goal [50,[58][59][60]86,88].…”

Section: Results Analysismentioning

confidence: 99%

“…Furthermore, for assessing the effect of periodicity time index is added to the input data (indicate the number of months from 1 to 12). In work presented in [60], the authors perform the reservoir inflow forecast by ANN to feed the multi-objective numerical optimization of hydropower production, solving by the application of a novel combined Pareto multiobjective differential evolution. In the paper [86], the monthly flow of a river is predicted by two recurrent neural networks techniques: Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU).…”

Section: Results Analysismentioning

confidence: 99%

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Hydropower Operation Optimization Using Machine Learning: A Systematic Review

Bernardes

Santos

Abreu

et al. 2022

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The optimal dispatch of hydropower plants consists of the challenge of taking advantage of both available head and river flows. Despite the objective of delivering the maximum power to the grid, some variables are uncertain, dynamic, non-linear, and non-parametric. Nevertheless, some models may help hydropower generating players with computer science evolution, thus maximizing the hydropower plants’ power production. Over the years, several studies have explored Machine Learning (ML) techniques to optimize hydropower plants’ dispatch, being applied in the pre-operation, real-time and post-operation phases. Hence, this work consists of a systematic review to analyze how ML models are being used to optimize energy production from hydropower plants. The analysis focused on criteria that interfere with energy generation forecasts, operating policies, and performance evaluation. Our discussions aimed at ML techniques, schedule forecasts, river systems, and ML applications for hydropower optimization. The results showed that ML techniques have been more applied for river flow forecast and reservoir operation optimization. The long-term scheduling horizon is the most common application in the analyzed studies. Therefore, supervised learning was more applied as ML technique segment. Despite being a widely explored theme, new areas present opportunities for disruptive research, such as real-time schedule forecast, run-of-river system optimization and low-head hydropower plant operation.

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Section: Results Analysismentioning

confidence: 99%

Section: Results Analysismentioning

confidence: 99%

Hydropower Operation Optimization Using Machine Learning: A Systematic Review

Bernardes

Santos

Abreu

et al. 2022

View full text Add to dashboard Cite

show abstract

“…For example, the review in [ 108 ] demonstrated the integration of data-driven techniques with optimisation modelling in streamflow forecasting. A combination of the accurate reservoir inflow forecasting (artificial neural networks) and efficient optimisation (Pareto multi-objective DE) can improve daily hydropower generation at the Vanderkloof Dam [ 54 ]. The studies of blending neural-network and EAs approaches were conducted in reservoir operation optimisation to obtain the best reservoir policy [ 42 ].…”

Section: Reservoir Operation Optimisation Innovation and Techniquesmentioning

confidence: 99%

A Review of Reservoir Operation Optimisations: from Traditional Models to Metaheuristic Algorithms

Lai

Huang

Koo

et al. 2022

Arch Computat Methods Eng

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Reservoir operation optimisation secures benefits, such as optimising energy production while minimising the possibility of flooding, operating costs, and water scarcity, at the lowest possible cost. This paper carries reviews of research on reservoir optimisation models and the consequential challenges of optimally operating reservoir operations. An introductory section is given to the background of reservoir operations and the current concerns on the optimal reservoir operations, for the decision-makers and stakeholders. Next, the review covered the recent ten years (between 2011 and 2021), on the recent research developments in innovation and techniques of reservoir operation optimisation. Further reviews on the conventional techniques that are the traditional methods, linear programming, nonlinear programming, and dynamic programming are discussed. Enhancements to the techniques in improving the drawbacks of the traditional techniques in optimisation of reservoir policies are next explained and evaluated. Recent advances in applying metaheuristics optimisation algorithms beneficial to the reservoir operations are explained, including the advantages and hinderances. A comprehensive tabulated and categorised review according to the classification of reservoir models, evaluation methods, and reservoir systems is given.

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“…Although the potential of DE has not been fully exploited in the area of water demand forecasting, it has however figured prominently in other areas of water resources like river flow forecasting (Piotrowski and Napiorkowski, 2011), reservoir inflow forecasting (Oyebode and Adeyemo, 2014), reservoir optimization (Olofintoye et al., 2016), sediment yield modelling (Kişi, 2010) and optimization of water distribution networks (Suribabu, 2010; Zheng et al., 2012). The meagre application of DE in water demand forecasting studies is evidential to the assertion of Ghalehkhondabi et al.…”

Section: Main Textmentioning

confidence: 99%

“…In a real-time reservoir optimization study, Olofintoye et al. (2016) coupled an ANN model with a novel combined Pareto multi-objective differential evolution (CPMDE) for flow forecasting and mathematical optimization of hydropower generation from the Vanderkloof reservoir, South Africa.…”

Section: Main Textmentioning

confidence: 99%

Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals

Oyebode

Babatunde

Monyei

et al. 2019

Heliyon

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The purpose of this review is to establish and classify the diverse ways in which evolutionary computation (EC) techniques have been employed in water demand modelling and to identify important research challenges and future directions. This review also investigates the potentials of conventional EC techniques in influencing water demand management policies beyond an advisory role while recommending strategies for their use by policy-makers with the sustainable development goals (SDGs) in perspective. This review ultimately proposes a novel integrated water demand and management modelling framework (IWDMMF) that enables water policy-makers to assess the wider impact of water demand management decisions through the principles of egalitarianism, utilitarianism, libertarianism and sufficientarianism. This is necessary to ensure that water policy decisions incorporate equity and justice.

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Real-time optimal water allocation for daily hydropower generation from the Vanderkloof dam, South Africa

Cited by 20 publications

References 14 publications

Hydropower Operation Optimization Using Machine Learning: A Systematic Review

Hydropower Operation Optimization Using Machine Learning: A Systematic Review

A Review of Reservoir Operation Optimisations: from Traditional Models to Metaheuristic Algorithms

Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals

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