Adaptive optimal control of prosumer energy storage system with renewable energy sources
The prosumer can use energy storage to enhance the benefits of electricity trading by transferring buy and sell points. Improving energy efficiency is not considered from the standpoint of the entire system, but from an individual prosumer and in conditions of difficult-to-predict wind power gen...
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| Format: | Article |
| Language: | English |
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Omsk State Technical University, Federal State Autonoumos Educational Institution of Higher Education
2020-11-01
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| Series: | Омский научный вестник |
| Subjects: | |
| Online Access: | https://www.omgtu.ru/general_information/media_omgtu/journal_of_omsk_research_journal/files/arhiv/2020/5%20(173)/50-56%20%D0%90%D0%BD%D1%82%D0%BE%D0%BD%D0%B5%D0%BD%D0%BA%D0%BE%D0%B2%20%D0%94.%20%D0%92.,%20%D0%9C%D0%B0%D0%BD%D1%83%D1%81%D0%BE%D0%B2%20%D0%92.%20%D0%97.,%20%D0%9C%D0%B0%D1%82%D1%80%D0%B5%D0%BD%D0%B8%D0%BD%20%D0%9F.%20%D0%92.%20%D0%B8%20%D0%B4%D1%80..pdf |
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| _version_ | 1832573968517693440 |
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| author | D. V. Antonenkov V. Z. Manusov P. V. Matrenin V. R. Kiushkina |
| author_facet | D. V. Antonenkov V. Z. Manusov P. V. Matrenin V. R. Kiushkina |
| author_sort | D. V. Antonenkov |
| collection | DOAJ |
| description | The prosumer can use energy storage to enhance the benefits of
electricity trading by transferring buy and sell points. Improving
energy efficiency is not considered from the standpoint of the
entire system, but from an individual prosumer and in conditions
of difficult-to-predict wind power generation. This work aims
to optimize the prosumer’s electrical complex by developing a
method for adapting the base of heuristic rules of the prosumer
control to its parameters and climatic conditions. A method for
adaptation control rules using swarm intelligence algorithms is proposed. The computer simulation has shown that the use of
swarm algorithms makes it possible to increase the economic
efficiency of managing the prosumer’s energy storage system by
2–4 times compared to the control rules manually constructed by
an expert. It is shown that the proposed method makes it possible
to automate the construction of the base of control rules. |
| format | Article |
| id | doaj-art-59acc68d724a4cd0a10cecefe1d2aaa5 |
| institution | Kabale University |
| issn | 1813-8225 2541-7541 |
| language | English |
| publishDate | 2020-11-01 |
| publisher | Omsk State Technical University, Federal State Autonoumos Educational Institution of Higher Education |
| record_format | Article |
| series | Омский научный вестник |
| spelling | doaj-art-59acc68d724a4cd0a10cecefe1d2aaa52025-02-02T01:41:26ZengOmsk State Technical University, Federal State Autonoumos Educational Institution of Higher EducationОмский научный вестник1813-82252541-75412020-11-015 (173)505610.25206/1813-8225-2020-173-50-56Adaptive optimal control of prosumer energy storage system with renewable energy sourcesD. V. Antonenkov0V. Z. Manusov1https://orcid.org/0000-0001-7799-4830P. V. Matrenin2https://orcid.org/0000-0001-5704-0976V. R. Kiushkina3https://orcid.org/0000-0002-7791-1844Novosibirsk State Technical UniversityNovosibirsk State Technical UniversityNovosibirsk State Technical UniversityTechnical Institute of the M. K. Ammosov North-Eastern Federal UniversityThe prosumer can use energy storage to enhance the benefits of electricity trading by transferring buy and sell points. Improving energy efficiency is not considered from the standpoint of the entire system, but from an individual prosumer and in conditions of difficult-to-predict wind power generation. This work aims to optimize the prosumer’s electrical complex by developing a method for adapting the base of heuristic rules of the prosumer control to its parameters and climatic conditions. A method for adaptation control rules using swarm intelligence algorithms is proposed. The computer simulation has shown that the use of swarm algorithms makes it possible to increase the economic efficiency of managing the prosumer’s energy storage system by 2–4 times compared to the control rules manually constructed by an expert. It is shown that the proposed method makes it possible to automate the construction of the base of control rules.https://www.omgtu.ru/general_information/media_omgtu/journal_of_omsk_research_journal/files/arhiv/2020/5%20(173)/50-56%20%D0%90%D0%BD%D1%82%D0%BE%D0%BD%D0%B5%D0%BD%D0%BA%D0%BE%D0%B2%20%D0%94.%20%D0%92.,%20%D0%9C%D0%B0%D0%BD%D1%83%D1%81%D0%BE%D0%B2%20%D0%92.%20%D0%97.,%20%D0%9C%D0%B0%D1%82%D1%80%D0%B5%D0%BD%D0%B8%D0%BD%20%D0%9F.%20%D0%92.%20%D0%B8%20%D0%B4%D1%80..pdfprosumerdistributed generationoptimal controlsmart gridenergy storage systemswarm intelligence |
| spellingShingle | D. V. Antonenkov V. Z. Manusov P. V. Matrenin V. R. Kiushkina Adaptive optimal control of prosumer energy storage system with renewable energy sources Омский научный вестник prosumer distributed generation optimal control smart grid energy storage system swarm intelligence |
| title | Adaptive optimal control of prosumer energy storage system with renewable energy sources |
| title_full | Adaptive optimal control of prosumer energy storage system with renewable energy sources |
| title_fullStr | Adaptive optimal control of prosumer energy storage system with renewable energy sources |
| title_full_unstemmed | Adaptive optimal control of prosumer energy storage system with renewable energy sources |
| title_short | Adaptive optimal control of prosumer energy storage system with renewable energy sources |
| title_sort | adaptive optimal control of prosumer energy storage system with renewable energy sources |
| topic | prosumer distributed generation optimal control smart grid energy storage system swarm intelligence |
| url | https://www.omgtu.ru/general_information/media_omgtu/journal_of_omsk_research_journal/files/arhiv/2020/5%20(173)/50-56%20%D0%90%D0%BD%D1%82%D0%BE%D0%BD%D0%B5%D0%BD%D0%BA%D0%BE%D0%B2%20%D0%94.%20%D0%92.,%20%D0%9C%D0%B0%D0%BD%D1%83%D1%81%D0%BE%D0%B2%20%D0%92.%20%D0%97.,%20%D0%9C%D0%B0%D1%82%D1%80%D0%B5%D0%BD%D0%B8%D0%BD%20%D0%9F.%20%D0%92.%20%D0%B8%20%D0%B4%D1%80..pdf |
| work_keys_str_mv | AT dvantonenkov adaptiveoptimalcontrolofprosumerenergystoragesystemwithrenewableenergysources AT vzmanusov adaptiveoptimalcontrolofprosumerenergystoragesystemwithrenewableenergysources AT pvmatrenin adaptiveoptimalcontrolofprosumerenergystoragesystemwithrenewableenergysources AT vrkiushkina adaptiveoptimalcontrolofprosumerenergystoragesystemwithrenewableenergysources |