Topology Optimization of the Network with Renewable Energy Sources Generation Based on a Modified Adapted Genetic Algorithm
The article presents an adaptive genetic algorithm developed by the authors, which makes it possible to optimize the topology of a power network with distributed generation. The optimization was based on bioinspired methods. The objects of the study were a 15-node circuit of a power net-work with ph...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | Russian |
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Belarusian National Technical University
2022-08-01
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| Series: | Известия высших учебных заведений и энергетических объединенний СНГ: Энергетика |
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| Online Access: | https://energy.bntu.by/jour/article/view/2180 |
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| author | A. M. Bramm A. I. Khalyasmaa S. A. Eroshenko P. V. Matrenin N. A. Papkova D. A. Sekatski |
| author_facet | A. M. Bramm A. I. Khalyasmaa S. A. Eroshenko P. V. Matrenin N. A. Papkova D. A. Sekatski |
| author_sort | A. M. Bramm |
| collection | DOAJ |
| description | The article presents an adaptive genetic algorithm developed by the authors, which makes it possible to optimize the topology of a power network with distributed generation. The optimization was based on bioinspired methods. The objects of the study were a 15-node circuit of a power net-work with photovoltaic stations and a 14-node IEEE augmented circuit with distributed generation sources (three wind farms and two photovoltaic plants). The simulation of the modes of electric power systems was performed using the Pandapower library for the Python programming language, which is in the public domain. Three types of electric load of consumers were considered, reflecting the natures of electricity consumption in the nodes of real electric power systems, the results of numerical studies were presented. The proposed genetic algorithm used two different functions of interbreeding, the function of mutation, selection of the best individuals and mass mutation (complete population renewal). At the end of each iteration of the algorithm operation, statistical dependencies were de-rived that characterized its work: the best (minimal losses) and average adaptability in the population, a list of the best individuals throughout all iterations, etc. The verification was carried out in comparison with the results obtained by a complete search of possible radial configurations of the system, and it showed that the developed genetic algorithm had fast convergence, high accuracy and was able to work correctly with different configurations of electrical circuits, generation and load structures. The algorithm can be used in conjunction with renewable energy sources generation forecasting systems for the day ahead when planning the operating modes of power units in order to minimize the costs of covering electricity losses and improve the quality of electricity supplied. |
| format | Article |
| id | doaj-art-8e794a248d26400a8c4f82098df5b4c4 |
| institution | Kabale University |
| issn | 1029-7448 2414-0341 |
| language | Russian |
| publishDate | 2022-08-01 |
| publisher | Belarusian National Technical University |
| record_format | Article |
| series | Известия высших учебных заведений и энергетических объединенний СНГ: Энергетика |
| spelling | doaj-art-8e794a248d26400a8c4f82098df5b4c42025-02-03T05:20:02ZrusBelarusian National Technical UniversityИзвестия высших учебных заведений и энергетических объединенний СНГ: Энергетика1029-74482414-03412022-08-0165434135410.21122/1029-7448-2022-65-4-341-3541817Topology Optimization of the Network with Renewable Energy Sources Generation Based on a Modified Adapted Genetic AlgorithmA. M. Bramm0A. I. Khalyasmaa1S. A. Eroshenko2P. V. Matrenin3N. A. Papkova4D. A. Sekatski5Ural Federal University named after the first President of Russia B. N. YeltsinUral Federal University named after the first President of Russia B. N. Yeltsin; Novosibirsk State Technical UniversityUral Federal University named after the first President of Russia B. N. Yeltsin; Novosibirsk State Technical UniversityNovosibirsk State Technical UniversityBelаrusian National Technical UniversityBelаrusian National Technical UniversityThe article presents an adaptive genetic algorithm developed by the authors, which makes it possible to optimize the topology of a power network with distributed generation. The optimization was based on bioinspired methods. The objects of the study were a 15-node circuit of a power net-work with photovoltaic stations and a 14-node IEEE augmented circuit with distributed generation sources (three wind farms and two photovoltaic plants). The simulation of the modes of electric power systems was performed using the Pandapower library for the Python programming language, which is in the public domain. Three types of electric load of consumers were considered, reflecting the natures of electricity consumption in the nodes of real electric power systems, the results of numerical studies were presented. The proposed genetic algorithm used two different functions of interbreeding, the function of mutation, selection of the best individuals and mass mutation (complete population renewal). At the end of each iteration of the algorithm operation, statistical dependencies were de-rived that characterized its work: the best (minimal losses) and average adaptability in the population, a list of the best individuals throughout all iterations, etc. The verification was carried out in comparison with the results obtained by a complete search of possible radial configurations of the system, and it showed that the developed genetic algorithm had fast convergence, high accuracy and was able to work correctly with different configurations of electrical circuits, generation and load structures. The algorithm can be used in conjunction with renewable energy sources generation forecasting systems for the day ahead when planning the operating modes of power units in order to minimize the costs of covering electricity losses and improve the quality of electricity supplied.https://energy.bntu.by/jour/article/view/2180distributed generationmode optimizationgenetic algorithmsolar energymetaheuristic methodsrestructuringdistribution networkpower lossesload curve |
| spellingShingle | A. M. Bramm A. I. Khalyasmaa S. A. Eroshenko P. V. Matrenin N. A. Papkova D. A. Sekatski Topology Optimization of the Network with Renewable Energy Sources Generation Based on a Modified Adapted Genetic Algorithm Известия высших учебных заведений и энергетических объединенний СНГ: Энергетика distributed generation mode optimization genetic algorithm solar energy metaheuristic methods restructuring distribution network power losses load curve |
| title | Topology Optimization of the Network with Renewable Energy Sources Generation Based on a Modified Adapted Genetic Algorithm |
| title_full | Topology Optimization of the Network with Renewable Energy Sources Generation Based on a Modified Adapted Genetic Algorithm |
| title_fullStr | Topology Optimization of the Network with Renewable Energy Sources Generation Based on a Modified Adapted Genetic Algorithm |
| title_full_unstemmed | Topology Optimization of the Network with Renewable Energy Sources Generation Based on a Modified Adapted Genetic Algorithm |
| title_short | Topology Optimization of the Network with Renewable Energy Sources Generation Based on a Modified Adapted Genetic Algorithm |
| title_sort | topology optimization of the network with renewable energy sources generation based on a modified adapted genetic algorithm |
| topic | distributed generation mode optimization genetic algorithm solar energy metaheuristic methods restructuring distribution network power losses load curve |
| url | https://energy.bntu.by/jour/article/view/2180 |
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