A novel mathematical modeling approach to the electric dispatch problem: Case study using Differential Evolution algorithms

Marcelino, Carolina G.; Wanner, Elizabeth F.; Almeida, Paulo E. M.

doi:10.1109/cec.2013.6557597

Cited by 7 publications

(13 citation statements)

References 8 publications

(12 reference statements)

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“…As a computational simulation test, this paper proposes as test scenario a HPP in Brazil with nominal installed capacity of 396M W . The HPP has in its powerhouse 6 The first experiment aims to compare and to analyse the value found in monoobjective approach proposed by Marcelino [5] (the solution was found via a Differential Evolution algorithm [14]) and results found in proposed multiobjective approach. The second experiment aims to assess Pareto front quality obtained by NSGA-II and SPEA-II algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…Marcelino [5] proposed a new mathematical model to solve HPP energy dispatch problem using DE. He showed that evolutionary strategy DE/best/1/bin is the most efficient for solving the mono-objective version of this problem.…”

Section: State Of the Artmentioning

confidence: 99%

“…This section presents brief summary of the mathematical model that describes the energy dispatch problem HPP, which has been discussed in Marcelino's [5] work. Table 1 describes model parameters.…”

Section: Mathematical Modeling Of Power Productivitymentioning

confidence: 99%

“…Having that in mind and assuming that the model presented in this work is the best representation for a power plant which uses Kaplan generators, as in the case study, power production performed by an hydroelectric unit, in MW, can be calculated by Eq. 3, Table 2 presents coefficients obtained by a multi-variable regression process, representing 99% of accuracy, see [5].…”

Section: Mathematical Modeling Of Power Productivitymentioning

confidence: 99%

“…According to the mathematical model presented so far, the problem multiobjective goals are to maximize the hydroelectric productivity function (4), which is derived from the electric power function (3), and to minimize distance between NMO and OCM in function (5). The optimization variables are water flow rate of each generation unit in the following vector,…”

Section: Multiobjective Optimization Modelmentioning

confidence: 99%

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Application of Evolutionary Multiobjective Algorithms for Solving the Problem of Energy Dispatch in Hydroelectric Power Plants

Marcelino

Carvalho

Almeida

et al. 2015

Lecture Notes in Computer Science

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The Brazilian population increase and the purchase power growth have resulted in a widespread use of electric home appliances.Consequently, the demand for electricity has been growing steadily in an average of 5% a year. In this country, electric demand is supplied predominantly by hydro power. Many of the power plants installed do not operate efficiently from water consumption point of view. Energy Dispatch is defined as the allocation of operational values to each turbine inside a power plant to meet some criteria defined by the power plant owner. In this context, an optimal scheduling criterion could be the provision of the greatest amount of electricity with the lowest possible water consumption, i.e. maximization of water use efficiency. Some power plant operators rely on "Normal Mode of Operation" (NMO) as Energy Dispatch criterion. This criterion consists in equally dividing power demand between available turbines regardless whether the allocation represents an efficient good operation point for each turbine. This work proposes a multiobjective approach to solve electric dispatch problem in which the objective functions considered are maximization of hydroelectric productivity function and minimization of the distance between NMO and "Optimized Control Mode" (OCM). Two well-known Multiobjective Evolutionary Algorithms are used to solve this problem. Practical results have shown water savings in the order of million m 3 /s. In addition, statistical inference has revealed that NSGA-II algorithm is more robust than SPEA-II algorithm to solve this problem.

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Section: Methodsmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: Mathematical Modeling Of Power Productivitymentioning

confidence: 99%

Section: Mathematical Modeling Of Power Productivitymentioning

confidence: 99%

Section: Multiobjective Optimization Modelmentioning

confidence: 99%

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Application of Evolutionary Multiobjective Algorithms for Solving the Problem of Energy Dispatch in Hydroelectric Power Plants

Marcelino

Carvalho

Almeida

et al. 2015

Lecture Notes in Computer Science

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Optimal electromagnetic torque of the induction motor generated automatically with Gmsh/GetDP software

Malagoli

Camacho

Luz

2020

Int Trans Electr Energ Syst

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Summary The main objective of this paper is to optimize the electromagnetic torque of the induction motor automatically using finite elements. In addition, through use of the differential evolution algorithm, an algorithm was created that is adapted for Multi‐objective Optimization Differential Evolution (MODE). In order to validate the developed algorithm, the results were compared with the Non‐dominated Sorting Genetic Algorithm (NSGA‐II). The methodology developed is based on minimizing the cost of materials over the manufacturing process of the three‐phase induction motor, along with maximization of energy efficiency and consequently producing optimal torque. The results for the efficient machine design are divided into three parts: an analytical calculation for the preliminary result; the determination of a geometric vector variable that characterizes the mathematical model presented in an optimization and CAD generator software. Finally, a discussion is presented concerning the limitations and advantages of the project approach developed in this paper.

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Dynamic Optimization of the Tandem Acetylene Hydrogenation Process

Tian

et al. 2016

Ind. Eng. Chem. Res.

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In ethylene production units, the acetylene side product is converted into additional C 2 H 4 over a palladium− carbon fixed bed reactor, in which the catalyst activity is affected by the formation of green oil impurities. We determined the optimal dynamic operating conditions for two reactors in series undergoing catalyst deactivation while maintaining the selectivity and conversion rate. The optimal switching periods for the reactors were investigated with dynamic optimization of the hydrogen input and inlet temperature with the incremental feature. Control vector parametrization was used to transform this dynamic problem into a finite-dimensional nonlinear programming problem. An incremental-encoding differential evolution with constraint ranking-based mutation operator (IEDE-CRMO) was proposed based on the reaction selectivity and catalyst activity variation to meet certain requirements of the control variables and to solve a complex industrial process. The IEDE-CRMO method was applied in an industrial C2 hydrogenation tandem reaction process, and results showed improvement in overall operation efficiency.

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A novel mathematical modeling approach to the electric dispatch problem: Case study using Differential Evolution algorithms

Cited by 7 publications

References 8 publications

Application of Evolutionary Multiobjective Algorithms for Solving the Problem of Energy Dispatch in Hydroelectric Power Plants

Application of Evolutionary Multiobjective Algorithms for Solving the Problem of Energy Dispatch in Hydroelectric Power Plants

Optimal electromagnetic torque of the induction motor generated automatically with Gmsh/GetDP software

Dynamic Optimization of the Tandem Acetylene Hydrogenation Process

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