Optimization of Multireservoir Systems by Genetic Algorithm

Hınçal, Onur; Altan-Sakarya, A. Burcu; Ger, A. Metin

doi:10.1007/s11269-010-9755-0

Cited by 93 publications

(24 citation statements)

References 30 publications

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“…We do however acknowledge that other algorithms also exist as reviewed by Ahmad et al (2014). For instance, some papers consider a genetic algorithm (Hincal et al 2011;Cheng et al 2008) or a simulated annealing one (Teegavarapu and Simonovic 2002). Some papers also investigate multi-objective optimization rather than one objective function (Liao et al 2014;Kougias et al 2012).…”

Section: Hydropower Plant Managementmentioning

confidence: 99%

Long-term Uncertainty of Hydropower Revenue Due to Climate Change and Electricity Prices

Gaudard

Gabbi²,

Bauder³

et al. 2016

Water Resour Manage

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Hydropower represents the world's largest renewable energy source. As a flexible technology, it enhances reliability and security of the electricity system. However, climate change and market liberalization may hinder investment due to the evolution of water runoffs and electricity prices. Both alter expected revenue and bring uncertainty. It increases risk and deters investment. Our research assesses how climate change and market fluctuation affect annual revenue. But this paper focuses on the uncertainty, rather than on forecasting. This transdisciplinary topic is investigated by means of a mixed method, i.e. both quantitative and qualitative. The quantitative approach uses established models in natural sciences and economics. The uncertainty is accounted for by applying various scenarios and various datasets coming from different models. Based on those results, uncertainty is discussed through an analysis discerning three dimensions of uncertainty. Uncertainty analysis requires the assessment of a large panel of models and data sets. It is therefore rarely carried out. The originality of the paper also lies on the combination of quantitative established models with a qualitative analysis. The results surprisingly show that the greenhouse gas scenarios may in fact represent a low source of uncertainty, unlike electricity prices. Like forecasting, the main uncertainties are actually case study related and depend on the investigated variables. It is also shown that the nature of uncertainty evolves. Runoff uncertainty goes from variability, i.e. inherent randomness, to epistemic, i.e. limitation of science. The reverse situation occurs with the electricity price. The implications for scientists and policy makers are discussed.

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Section: Hydropower Plant Managementmentioning

confidence: 99%

Long-term Uncertainty of Hydropower Revenue Due to Climate Change and Electricity Prices

Gaudard

Gabbi²,

Bauder³

et al. 2016

Water Resour Manage

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“…Parameters are optimised using a genetic algorithm (Hınçal et al, 2011). This heuristic programming method may help avoid getting stuck in local optimums (Labadie, 2004).…”

Section: A Parameterisation-simulation-optimisation Approachmentioning

confidence: 99%

Large-scale water scarcity assessment under global changes: insights from a hydroeconomic framework

Neverre

Dumas

Nassopoulos³

2016

Preprint

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Abstract.Global changes are expected to exacerbate water scarcity issues in the Mediterranean region in the next decades. In this work, we investigate the impacts of reservoirs operation rules based on an economic criterion. We examine whether can they help reduce the costs of water scarcity, and whether they become more relevant under future climatic and socioeconomic conditions.We develop an original hydroeconomic model able to compare water supply and demand on a large scale, while representing 5 river basin heterogeneity.On the supply side, we evaluate the impacts of climate change on water inflows to the reservoirs. On the demand side, we focus on the two main sectors of water use: irrigation and domestic sectors. Demands are projected in terms of both quantity and economic value. Coordinated operating rules of the reservoirs are set up, considering spatial and temporal trade-offs. The objective is the maximisation of water benefits. 10The methodology is applied to Algeria at the 2050 horizon. Our results show that the supply-demand imbalance and its costs will increase in most Algerian basins under future climatic and socioeconomic conditions. Our results suggest that the benefits of operating rules based on economic criteria are not unequivocally increased with global changes. In some basins the positive impact of economic prioritisation is higher in future conditions, but in other basins it is higher in historical conditions. Given its generic nature and low data requirements, the developed framework could be implemented in other regions con-15 cerned with water scarcity, or extended to a global coverage.

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“…has been illustrated (Molina-Cristobal et al 2005;Hanne and Nickel 2005;Osman et al 2005;Farmani et al 2005bFarmani et al , 2006Farmani et al and 2007Murugan et al 2009), there have been limited applications in policy analysis of water resources management (Farmani et al 2009). There are recent applications of EMO algorithms related to other water resources research studies such as optimal design of water distribution systems or reservoirs (Cisty 2010;Nazif et al 2010;Haghighi et al 2011;Hınçal et al 2011;Louati et al 2011), or Conjunctive Use of Surface Water and Groundwater (Safavi et al 2010), or the control of Seawater Intrusion in Coastal Aquifers (Kourakos and Mantoglou 2011; Abd-Elhamid and Javadi 2011; Sedki and Ouazar 2011), or hydrological studies (Dumedah et al 2010;;Hassanzadeh et al 2011;Gorev et al 2011). In this work an evolutionary multi-objective optimization tool (GANetXL 2007) based on NSGAII (Deb et al 2000) is coupled with the OOBN model developed in HUGIN software (2007), and used to assist in the selection of the best compromise management option(s) for participatory decision making.…”

Section: Evolutionary Multiobjective Optimization (Emo)mentioning

confidence: 99%

Aquifers Management through Evolutionary Bayesian Networks: The Altiplano Case Study (SE Spain)

Molina

Farmani

Bromley

2011

Water Resour Manage

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An approach for the integration of Object-Oriented Bayesian Networks (OOBNs) and Evolutionary Multiobjective Optimization (EMO) is proposed for integrated water resource management and decision support. Bayesian Networks (BNs) offer a novel and powerful tool for modelling complex water systems, facilitating the use of hierarchical modelling by improving the efficiency and communication between the different parts of a model. EMO offers a range of non-dominated optimal management solutions on a Pareto front that facilitate the identification of tradeoffs among conflicting criteria regarding stakeholder's preferences. The integrated tool provides new possibilities for undertaking an integrated analysis where stakeholder participation can play an important role. It is used for simultaneously analysing the whole water system, characterising uncertainty as probabilities and evaluating different management options. The tool is applied to an overexploited water system located in Southern Spain that is supplied totally by groundwater. In this study, a complex model based on BNs is designed and used as the core of the study. The transition to Evolutionary Bayesian networks (EOBNs) allows stakeholder involvement to be utilized more effectively for designing and evaluating the model's consistency, and taking into account their conflicting interests.

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Optimization of Multireservoir Systems by Genetic Algorithm

Cited by 93 publications

References 30 publications

Long-term Uncertainty of Hydropower Revenue Due to Climate Change and Electricity Prices

Long-term Uncertainty of Hydropower Revenue Due to Climate Change and Electricity Prices

Large-scale water scarcity assessment under global changes: insights from a hydroeconomic framework

Aquifers Management through Evolutionary Bayesian Networks: The Altiplano Case Study (SE Spain)

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