A deep reinforcement learning-based approach for cyber resilient demand response optimization
The contemporary smart grid infrastructure, characterized by its bidirectional communication capabilities between prosumers and utility organizations, has revolutionized the efficient execution of fine-grain computational tasks. Ensuring the uninterrupted delivery of power, even in the face of unfor...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1494164/full |
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| author | Ayush Sinha Ranjana Vyas Feras Alasali William Holderbaum O. P. Vyas |
| author_facet | Ayush Sinha Ranjana Vyas Feras Alasali William Holderbaum O. P. Vyas |
| author_sort | Ayush Sinha |
| collection | DOAJ |
| description | The contemporary smart grid infrastructure, characterized by its bidirectional communication capabilities between prosumers and utility organizations, has revolutionized the efficient execution of fine-grain computational tasks. Ensuring the uninterrupted delivery of power, even in the face of unforeseen contingencies, stands as a paramount concern for utility companies. Peak load forecasting, load balancing, and robust cyberattack detection and prevention mechanisms are integral components in achieving grid reliability. This research endeavors to advance peak load forecasting strategies and demand response optimization at the microgrid level, thereby enhancing grid reliability through the application of Deep Reinforcement Learning (DRL) techniques. Additionally, it investigates the ongoing threat of false data injection attacks. By synergizing these two critical investigations and implementing a novel framework and defense mechanism, this paper proposes a comprehensive approach to fortify the smart grid’s reliability and security. The envisioned framework not only refines demand response (DR) optimization but also bolsters the grid’s resilience in the face of the everevolving cyber threat landscape. The research outcomes showcase the practicality and effectiveness of the proposed framework, substantiated through extensive experimentation conducted on IEEE-3, IEEE-9, IEEE-14, and IEEE-33 bus systems. |
| format | Article |
| id | doaj-art-df687e23e81d4e82aac2a44d5f344985 |
| institution | Kabale University |
| issn | 2296-598X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj-art-df687e23e81d4e82aac2a44d5f3449852025-01-30T06:23:06ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2025-01-011210.3389/fenrg.2024.14941641494164A deep reinforcement learning-based approach for cyber resilient demand response optimizationAyush Sinha0Ranjana Vyas1Feras Alasali2William Holderbaum3O. P. Vyas4Department of IT, Indian Institute of Information Technology, Allahabad, IndiaDepartment of IT, Indian Institute of Information Technology, Allahabad, IndiaDepartment of Electrical Engineering, Faculty of Engineering, The Hashemite University, Zarqa, JordanSchool of Science, Engineering Environment, University of Salford, Salford, United KingdomDepartment of IT, Indian Institute of Information Technology, Allahabad, IndiaThe contemporary smart grid infrastructure, characterized by its bidirectional communication capabilities between prosumers and utility organizations, has revolutionized the efficient execution of fine-grain computational tasks. Ensuring the uninterrupted delivery of power, even in the face of unforeseen contingencies, stands as a paramount concern for utility companies. Peak load forecasting, load balancing, and robust cyberattack detection and prevention mechanisms are integral components in achieving grid reliability. This research endeavors to advance peak load forecasting strategies and demand response optimization at the microgrid level, thereby enhancing grid reliability through the application of Deep Reinforcement Learning (DRL) techniques. Additionally, it investigates the ongoing threat of false data injection attacks. By synergizing these two critical investigations and implementing a novel framework and defense mechanism, this paper proposes a comprehensive approach to fortify the smart grid’s reliability and security. The envisioned framework not only refines demand response (DR) optimization but also bolsters the grid’s resilience in the face of the everevolving cyber threat landscape. The research outcomes showcase the practicality and effectiveness of the proposed framework, substantiated through extensive experimentation conducted on IEEE-3, IEEE-9, IEEE-14, and IEEE-33 bus systems.https://www.frontiersin.org/articles/10.3389/fenrg.2024.1494164/fullsmart grid architectureload forecastingdemand responseload profilingsmart grid resilienceFDI attack |
| spellingShingle | Ayush Sinha Ranjana Vyas Feras Alasali William Holderbaum O. P. Vyas A deep reinforcement learning-based approach for cyber resilient demand response optimization Frontiers in Energy Research smart grid architecture load forecasting demand response load profiling smart grid resilience FDI attack |
| title | A deep reinforcement learning-based approach for cyber resilient demand response optimization |
| title_full | A deep reinforcement learning-based approach for cyber resilient demand response optimization |
| title_fullStr | A deep reinforcement learning-based approach for cyber resilient demand response optimization |
| title_full_unstemmed | A deep reinforcement learning-based approach for cyber resilient demand response optimization |
| title_short | A deep reinforcement learning-based approach for cyber resilient demand response optimization |
| title_sort | deep reinforcement learning based approach for cyber resilient demand response optimization |
| topic | smart grid architecture load forecasting demand response load profiling smart grid resilience FDI attack |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1494164/full |
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