Genetic algorithm-aided design of a fuzzy logic stabilizer for a superconducting generator

Saleh, R. A. F.; Bolton, H.R.

doi:10.1109/59.898109

Cited by 29 publications

(11 citation statements)

References 12 publications

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“…During the last decade, GA has been successfully applied to diverse power systems problems: optimal design of control systems [25]; unit commitment [26]; generation scheduling and its sub problems [27].…”

Section: G Npvmentioning

confidence: 99%

Optimal Allocation and Economic Analysis of Energy Storage System in Microgrids

Chen

Duan

Cai

et al. 2011

IEEE Trans. Power Electron.

333

175

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This paper presents a methodology for the optimal allocation and economic analysis of energy storage system (ESS) in microgrids (MGs) on the basis of net present value (NPV). As the performance of an MG strongly depends on the allocation and arrangement of its ESS, optimal allocation methods and economic operation strategies of the ESS devices are required for the MG. To optimize the operation strategies and capacities of ESS in MGs, the financial benefit and dynamic models of ESS are discussed. And then, a matrix real-coded genetic algorithm is applied to find maximal NPV, in which each GA chromosome consists of a 2-D real number matrix representing the generation schedule of ESS and distributed generation sources. This paper is to suggest, among those available ESS, the optimal sizes and types of them and their optimal arrangement, such that the total NPV achieved during the system operational lifetime period is maximized. Finally, some computational simulation results are presented to verify the effectiveness of the proposed method.

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Section: G Npvmentioning

confidence: 99%

Optimal Allocation and Economic Analysis of Energy Storage System in Microgrids

Chen

Duan

Cai

et al. 2011

IEEE Trans. Power Electron.

333

175

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“…The essential trait of ACO algorithms is the combination of a priori information about the structure of a promising solution with a posteriori information about the structure of previously obtained good solutions [6,7] . The essential trait of ACO algorithms is the combination of a priori information about the structure of a promising solution with a posteriori information about the structure of previously obtained good solutions [6,7] .…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

Computational Tools for Smart Grid Design

Momoh¹

2012

Smart Grid

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“…3) Repair Operators: The offspring creation process often produces unfeasible solutions due to violations of restrictions described in (4) and (6). To avoid the creation of too many unfeasible solutions, two repair operators have been included:…”

Section: ) Crossover Operatorsmentioning

confidence: 99%

“…A GA is a metaheuristic technique inspired on genetics and evolution theories [4]. During the last decade, it has been successfully applied to diverse power systems problems: optimal design of control systems [5], [6]; load forecasting [7]; OPF in systems with FACTS [8]- [10]; FACTS allocation [11]; networks expansion [12]- [14]; reactive power planning [15]- [17]; maintenance scheduling [18], [19]; economic load dispatch [20], [21]; generation scheduling and its subproblems [22]- [34].…”

Section: Introductionmentioning

confidence: 99%

Short-term hydrothermal generation scheduling model using a genetic algorithm

Gil

Bustos

Rudnick

2003

IEEE Trans. Power Syst.

202

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Abstract-A new model to deal with the short-term generation scheduling problem for hydrothermal systems is proposed. Using genetic algorithms (GAs), the model handles simultaneously the subproblems of short-term hydrothermal coordination, unit commitment, and economic load dispatch. Considering a scheduling horizon period of a week, hourly generation schedules are obtained for each of both hydro and thermal units. Future cost curves of hydro generation, obtained from long and mid-term models, have been used to optimize the amount of hydro energy to be used during the week. In the genetic algorithm (GA) implementation, a new technique to represent candidate solutions is introduced, and a set of expert operators has been incorporated to improve the behavior of the algorithm. Results for a real system are presented and discussed.

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Genetic algorithm-aided design of a fuzzy logic stabilizer for a superconducting generator

Cited by 29 publications

References 12 publications

Optimal Allocation and Economic Analysis of Energy Storage System in Microgrids

Optimal Allocation and Economic Analysis of Energy Storage System in Microgrids

Computational Tools for Smart Grid Design

Short-term hydrothermal generation scheduling model using a genetic algorithm

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