Power control of hybrid grid-connected renewable energy system using machine learning
This article addresses the crucial challenge of maintaining a reliable power supply in integrated electric systems that combine solar power and energy storage. It focuses on optimizing key parameters for remote photovoltaic, wind, and battery energy storage within the grid, with a primary goal of su...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723016542 |
| _version_ | 1797364205650706432 |
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| author | M. Karthikeyan D. Manimegalai karthikeyan RajaGopal |
| author_facet | M. Karthikeyan D. Manimegalai karthikeyan RajaGopal |
| author_sort | M. Karthikeyan |
| collection | DOAJ |
| description | This article addresses the crucial challenge of maintaining a reliable power supply in integrated electric systems that combine solar power and energy storage. It focuses on optimizing key parameters for remote photovoltaic, wind, and battery energy storage within the grid, with a primary goal of sustaining 24-hour load demand. The study introduces a comprehensive approach that integrates fuel price dynamics and battery depletion costs into the optimization model for hybrid grids. This approach seeks a delicate balance between sustainable energy usage and cost-effectiveness. To achieve this balance, the research employs genetic algorithms, a robust computational technique. These algorithms play a vital role in calculating discharge-charge cycles and assessing battery health, addressing concerns about battery longevity and performance. Additionally, the study explores a machine learning framework, specifically nested learning, to further enhance the optimization process. Nested learning allows for the development of a sophisticated target function that considers various grid operation parameters and constraints. This machine learning approach enhances the system's ability to dynamically monitor power consumption and generation. Within this machine learning framework, the TD Lambda learning algorithm takes a prominent role in identifying the optimal function. This algorithm is well-known for its efficiency, particularly in non-Markovian scenarios. Its quick convergence enhances the overall performance of the hybrid grid, ensuring efficiency in changing conditions. The research also evaluates the impact of weight factors on various aspects, including battery charging status, algorithmic decisions, and their consequences on optimal energy pricing within the hybrid grid. This comprehensive assessment deepens the understanding of how these weight factors influence grid performance and economic viability. |
| first_indexed | 2024-03-08T16:31:57Z |
| format | Article |
| id | doaj.art-12ab9fead3f44e8db03ae652c5817e55 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-08T16:31:57Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-12ab9fead3f44e8db03ae652c5817e552024-01-06T04:39:04ZengElsevierEnergy Reports2352-48472024-06-011110791087Power control of hybrid grid-connected renewable energy system using machine learningM. Karthikeyan0D. Manimegalai1karthikeyan RajaGopal2Centre for Advanced Wireless Integrated Technology, Chennai Institute of Technology, Chennai, India; Corresponding author.Department of Electrical and Electronics Engineering, Rajalakshmi Engineering College, Chennai, IndiaCentre for Nonlinear system, Chennai Institute of Technology, Chennai, IndiaThis article addresses the crucial challenge of maintaining a reliable power supply in integrated electric systems that combine solar power and energy storage. It focuses on optimizing key parameters for remote photovoltaic, wind, and battery energy storage within the grid, with a primary goal of sustaining 24-hour load demand. The study introduces a comprehensive approach that integrates fuel price dynamics and battery depletion costs into the optimization model for hybrid grids. This approach seeks a delicate balance between sustainable energy usage and cost-effectiveness. To achieve this balance, the research employs genetic algorithms, a robust computational technique. These algorithms play a vital role in calculating discharge-charge cycles and assessing battery health, addressing concerns about battery longevity and performance. Additionally, the study explores a machine learning framework, specifically nested learning, to further enhance the optimization process. Nested learning allows for the development of a sophisticated target function that considers various grid operation parameters and constraints. This machine learning approach enhances the system's ability to dynamically monitor power consumption and generation. Within this machine learning framework, the TD Lambda learning algorithm takes a prominent role in identifying the optimal function. This algorithm is well-known for its efficiency, particularly in non-Markovian scenarios. Its quick convergence enhances the overall performance of the hybrid grid, ensuring efficiency in changing conditions. The research also evaluates the impact of weight factors on various aspects, including battery charging status, algorithmic decisions, and their consequences on optimal energy pricing within the hybrid grid. This comprehensive assessment deepens the understanding of how these weight factors influence grid performance and economic viability.http://www.sciencedirect.com/science/article/pii/S2352484723016542Hybrid grid-connected systemMachine learningBattery storageNested learning algorithm |
| spellingShingle | M. Karthikeyan D. Manimegalai karthikeyan RajaGopal Power control of hybrid grid-connected renewable energy system using machine learning Energy Reports Hybrid grid-connected system Machine learning Battery storage Nested learning algorithm |
| title | Power control of hybrid grid-connected renewable energy system using machine learning |
| title_full | Power control of hybrid grid-connected renewable energy system using machine learning |
| title_fullStr | Power control of hybrid grid-connected renewable energy system using machine learning |
| title_full_unstemmed | Power control of hybrid grid-connected renewable energy system using machine learning |
| title_short | Power control of hybrid grid-connected renewable energy system using machine learning |
| title_sort | power control of hybrid grid connected renewable energy system using machine learning |
| topic | Hybrid grid-connected system Machine learning Battery storage Nested learning algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723016542 |
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