Optimizing profits from hydroelectricity production

Ladurantaye, Daniel De; Gendreau, Michel; Potvin, Jean-Yves

doi:10.1016/j.cor.2007.10.012

Cited by 49 publications

(24 citation statements)

References 64 publications

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“…The economic viability of hydro electricity production plants is demonstrated from the aspect of market analysis [35] [36] and production planning [37], and for electricity energy production [38], using stochastic programming. The concept of integrating efficiency and location allocation analysis, has been presented in the context of health care [39].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal design of the renewable energy map of Greece using weighted goal-programming and data envelopment analysis

Zografidou

Petridis

Arabatzis

et al. 2016

Computers & Operations Research

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Renewable energy forms have been widely used in the past decades highlighting a "green" shift in electricity production. An actual reason behind this turn to renewable energy production is EU directives set the Union's target for energy production from renewable sources, greenhouse gas emissions and increase in energy efficiency. All member countries are obligated to apply harmonized legislation and practices and restructure their energy production networks in order to meet EU targets. Towards the fulfillment of 20-20-20 EU targets, in Greece a specific strategy which promotes the construction of large scale Renewable Energy Source plants is been promoted. In this paper, we present an optimal design of the Greek renewable energy production network applying a 0-1 weighted multiperiod Goal Programming model, considering social, ecological and economic criteria. In the absence of a panel of experts Data Envelopment Analysis (DEA) approach is used in order to filter the best out of the possible network structures, seeking for the maximum technical efficiency. Super-Efficiency DEA model is also used in order to reduce the solutions and find the best out of all the possible. The results showed that in order to achieve maximum efficiency, the social and environmental criteria must be weighted more than the economic one.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal design of the renewable energy map of Greece using weighted goal-programming and data envelopment analysis

Zografidou

Petridis

Arabatzis

et al. 2016

Computers & Operations Research

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“…Other approaches with higher focus on the stochastic representation and less on hydropower details has also been investigated, for instance in [11] and [12]. More detailed models based on SSLP are reported in [13] and [14]. In [13] the increased revenue from using a stochastic model varied within [0;16%] and in [10] it was 1.3%.…”

Section: Short-term Hydropower Schedulingmentioning

confidence: 99%

Applying successive linear programming for stochastic short-term hydropower optimization

Belsnes

Wolfgang

Follestad

et al. 2016

Electric Power Systems Research

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We present a model for operational stochastic short-term hydropower scheduling, taking into account the uncertainty in future prices and inflow, and illustrate how the benefits of using a stochastic rather than a deterministic model can be quantified. The solution method is based on stochastic successive linear programming. The proposed method is tested against the solution of the true non-linear problem in a principal setting. We demonstrate that the applied methodology is a first-order approximation to a formal correct head-of-water optimization and achieve good results in tests. How the concept of stochastic successive linear programming has been implemented in a prototype software for operational short-term hydropower scheduling is also presented, and the model's ability is demonstrated through case studies from Norwegian power industry. From these studies, improvements occurred in terms of the objective function value and decreased risk of spill from reservoirs.

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“…The hydroturbines' power plants convert the water flow energy into electricity. The power output of the hydroturbine corresponds to the water volume at the dam and the water flow going through the unit [31] (16)…”

Section: A Decision Variablesmentioning

confidence: 99%

“…Therefore, two if if (30) changes are strongly recommended to improve the performance of this algorithm. In detail, first, the learning phase is replaced by (31) and then a new mutation method is used to generate as (32) and (33). This new mutation method is explained hereinafter:…”

Section: B Modified Tlamentioning

confidence: 99%

Scenario-Based Multiobjective Volt/Var Control in Distribution Networks Including Renewable Energy Sources

Niknam

Zare

Aghaei

2012

IEEE Trans. Power Delivery

226

160

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This paper proposes a stochastic multiobjective framework for daily volt/var control (VVC), including hydroturbine, fuel cell, wind turbine, and photovoltaic powerplants. The multiple objectives of the VVC problem to be minimized are the electrical energy losses, voltage deviations, total electrical energy costs, and total emissions of renewable energy sources and grid. For this purpose, the uncertainty related to hourly load, wind power, and solar irradiance forecasts are modeled in a scenario-based stochastic framework. A roulette wheel mechanism based on the probability distribution functions of these random variables is considered to generate the scenarios. Consequently, the stochastic multiobjective VVC (SMVVC) problem is converted to a series of equivalent deterministic scenarios. Furthermore, an Evolutionary Algorithm using the Modified Teaching-Learning-Algorithm (MTLA) is proposed to solve the SMVVC in the form of a mixed-integer nonlinear programming problem. In the proposed algorithm, a new mutation method is taken into account in order to enhance the global searching ability and mitigate the premature convergence to local minima. Finally, two distribution test feeders are considered as case studies to demonstrate the effectiveness of the proposed SMVVC.

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Optimizing profits from hydroelectricity production

Cited by 49 publications

References 64 publications

Optimal design of the renewable energy map of Greece using weighted goal-programming and data envelopment analysis

Optimal design of the renewable energy map of Greece using weighted goal-programming and data envelopment analysis

Applying successive linear programming for stochastic short-term hydropower optimization

Scenario-Based Multiobjective Volt/Var Control in Distribution Networks Including Renewable Energy Sources

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