A genetic algorithm based approach to thermal unit commitment of electric power systems

In article discusses issues for solving optimization problems based on the use of genetic algorithms. Nowadays, the genetic algorithms for solving various problems. This includes the shortest path search, approximation, data filtering and others. In particular, data is being examined regarding the use of a genetic algorithm to solve problems of optimizing the modes of electric power systems. Imagine an algorithm for developing the development of mathematical models, which includes developing the structure of the chromosome, creating a started population, creating a directing force for the population, etc.

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Structural control based on genetic algorithm and neural network for electric power systems

[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems ◽

10.1109/ann.1993.264295 ◽

2002 ◽

Cited By ~ 1

Author(s):

A. Ishigame ◽

Y. Takagi ◽

S. Kawamoto ◽

T. Taniguchi ◽

H. Tanaka

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Power Systems ◽

Electric Power ◽

Structural Control ◽

Electric Power Systems

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Application of a genetic algorithm to meter allocation in electric power systems

Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan) ◽

10.1109/ijcnn.1993.716910 ◽

2005 ◽

Cited By ~ 6

Author(s):

H. Mori ◽

S. Iida

Keyword(s):

Genetic Algorithm ◽

Power Systems ◽

Electric Power ◽

Electric Power Systems

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Removal of overloads and voltage problems in electric power systems using genetic algorithm and knowledge base

Fifth International Conference on Power System Management and Control ◽

10.1049/cp:20020010 ◽

2002 ◽

Cited By ~ 1

Author(s):

G.A. Bakare

Keyword(s):

Genetic Algorithm ◽

Power Systems ◽

Knowledge Base ◽

Electric Power ◽

Electric Power Systems

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OPTIMIZATION OF MODES OF ELECTR POWER SYSTEMS BY GENETIC ALGOROTHMS

Technical science and innovation ◽

10.51346/tstu-01.19.3.-77-0030 ◽

2019 ◽

Vol 2019 (3) ◽

pp. 224-229

Author(s):

B Pulatov

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Power Systems ◽

Electric Power ◽

Optimization Problems ◽

Block Diagram ◽

Electric Power Systems ◽

Data Filtering ◽

Path Search ◽

Shortest Path Search

In article discusses issues for solving optimization problems based on the use of genetic algorithms. To date, the genetic use algorithm for solving various problems. Which includes the shortest path search, approximation, data filtering and others. In particular, data is being examined regarding the use of a genetic algorithm to solve problems of optimizing the modes of electric power systems. Imagine an algorithm for developing the development of mathematical models, which includes developing the structure of the chromosome, creating a started population, creating a directing force for the population, etc. As well as the presentation, the selected structure should take into account all the features and limitations imposed on the desired solution, as well as the fact that the implementation of crossоver and mutation algorithms directly depends on its choice. To solve optimization problems, a block diagram of the genetic algorithm is given.

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Methodology of optimum unit commitment of energy systems with renewable energy sources

Vestnik IGEU ◽

10.17588/2072-2672.2020.6.025-038 ◽

2020 ◽

pp. 25-38

Author(s):

S.G. Obukhov ◽

G.N. Klimova ◽

A. Ibrahim

Keyword(s):

Renewable Energy ◽

Power Systems ◽

Electric Power ◽

Optimization Problem ◽

Unit Commitment ◽

Renewable Energy Sources ◽

Electric Power Systems ◽

Electric Power System ◽

Energy Sources ◽

Particle Swarm Algorithm

One of the promising ways to improve the reliability and efficiency of power supply for customers in the areas remote from central electrical grid is the use of hybrid power systems with renewable energy sources. The primary task of designing such systems is the unit commitment of the generating equipment that provides the optimal technical and economic indexes of the electric power system. The stochastic nature of generation and nonlinearity of the characteristics of power plants cause a high complexity of solving this problem, which, from a mathematical point of view, is formulated as an optimization problem. An accurate and reliable solution of this optimization problem increases the efficiency of design and operation of hybrid electric power systems with renewable energy sources. And it is a vital task of modern power industry. A probabilistic-statistical methods and models for the analysis of experimental data are used to construct climatic time series and graphs of electrical loads. In addition, to study the operating modes of the electric power system the MatLab system is used for the simulation and modeling, and an evolutionary particle swarm algorithm is used to solve the optimization problem. The original model of solar radiation is used as a part of this methodology. This model provides forecasting the key characteristics of solar radiation in any geographical point of Russia including the areas that have no results of routine actinometric observation. Weibull distribution function is used to forecast daily variations of wind speed. It enhances the validity of forecasting of electricity generation of wind-driven power plant at daily time interval. As a result of the research, a method of optimum unit commitment has been developed for the equipment of electric power systems based on renewable energy sources. The use of the particle swarm algorithm as a part of the methodology provides reliable and accurate determination of the extremum of the objective function, which increases the efficiency of design and operation of hybrid electric power systems with renewable energy sources. The method has been tested on practical examples of optimum unit commitment for the equipment of electric power systems of various configurations and has proven its effectiveness. The technique is implemented as a software application, which ensures the convenience of its practical application. The obtained results can be used by companies involved in the design and operation of electric power systems using renewable energy generating units.

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