Hybrid Adaptive Sheep Flock Optimization and Gradient Descent Optimization for Energy Management in a Grid-Connected Microgrid
Distributed generation has emerged as a viable solution to supplement traditional grid problems and lessen their negative effects on the environment worldwide. Nevertheless, distributed generation issues are unpredictable and intermittent and impede the power system’s ability to operate effectively....
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MDPI AG
2025-05-01
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| author | Sri Harish Nandigam Krishna Mohan Reddy Pothireddy K. Nageswara Rao Surender Reddy Salkuti |
| author_facet | Sri Harish Nandigam Krishna Mohan Reddy Pothireddy K. Nageswara Rao Surender Reddy Salkuti |
| author_sort | Sri Harish Nandigam |
| collection | DOAJ |
| description | Distributed generation has emerged as a viable solution to supplement traditional grid problems and lessen their negative effects on the environment worldwide. Nevertheless, distributed generation issues are unpredictable and intermittent and impede the power system’s ability to operate effectively. Moreover, the problems associated with outliers and denial of service (DoS) attacks hinder energy management. Therefore, efficient energy management in grid-connected microgrids is critical to ensure sustainability, cost efficiency, and reliability in the presence of uncertainties, outliers, denial-of-service attacks, and false data injection attacks. This paper proposes a hybrid optimization approach that combines adaptive sheep flock optimization (ASFO) and gradient descent optimization (GDO) to address the challenges of energy dispatch and load balancing in MG. The ASFO algorithm offers robust global search capabilities to explore complex search spaces, while GDO safeguards precise local convergence to optimize the dispatch schedule and energy cost and maximize renewable energy utilization. The hybrid method ASFOGDO leverages the strengths of both algorithms to overcome the limitations of standalone approaches. Results demonstrate the efficiency of the proposed hybrid algorithm, achieving substantial improvements in energy efficiency and cost reduction compared to traditional methods like interior point optimization, gradient descent, branch and bound, and a population-based algorithm named Golden Jackal optimization. In case 1, the overall cost in scenario 1 and scenario 2 was reduced from 1620.4 rupees to 1422.84 rupees, whereas, in case 2, the total cost was reduced from 12,350 rupees to 12,017 rupees with the proposed hybrid ASFOGDO algorithm. Further, a detailed impact of attacks and outliers on scheduling, operational cost, and reliability of supply is presented in case 3. |
| format | Article |
| id | doaj-art-4aaf7042e79d4cbc86d5769191652095 |
| institution | Kabale University |
| issn | 2411-9660 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Designs |
| spelling | doaj-art-4aaf7042e79d4cbc86d57691916520952025-08-20T03:26:56ZengMDPI AGDesigns2411-96602025-05-01936310.3390/designs9030063Hybrid Adaptive Sheep Flock Optimization and Gradient Descent Optimization for Energy Management in a Grid-Connected MicrogridSri Harish Nandigam0Krishna Mohan Reddy Pothireddy1K. Nageswara Rao2Surender Reddy Salkuti3Department of Electrical and Electronics Engineering, Hindustan Institute of Technology and Science, Chennai 603103, Tamilnadu, IndiaDepartment of Electrical Engineering, National Institute of Technology, Tadepalligudem 534101, Andhra Pradesh, IndiaDepartment of Electrical and Electronics Engineering, Hindustan Institute of Technology and Science, Chennai 603103, Tamilnadu, IndiaDepartment of Railroad and Electrical Engineering, Woosong University, Daejeon 34606, Republic of KoreaDistributed generation has emerged as a viable solution to supplement traditional grid problems and lessen their negative effects on the environment worldwide. Nevertheless, distributed generation issues are unpredictable and intermittent and impede the power system’s ability to operate effectively. Moreover, the problems associated with outliers and denial of service (DoS) attacks hinder energy management. Therefore, efficient energy management in grid-connected microgrids is critical to ensure sustainability, cost efficiency, and reliability in the presence of uncertainties, outliers, denial-of-service attacks, and false data injection attacks. This paper proposes a hybrid optimization approach that combines adaptive sheep flock optimization (ASFO) and gradient descent optimization (GDO) to address the challenges of energy dispatch and load balancing in MG. The ASFO algorithm offers robust global search capabilities to explore complex search spaces, while GDO safeguards precise local convergence to optimize the dispatch schedule and energy cost and maximize renewable energy utilization. The hybrid method ASFOGDO leverages the strengths of both algorithms to overcome the limitations of standalone approaches. Results demonstrate the efficiency of the proposed hybrid algorithm, achieving substantial improvements in energy efficiency and cost reduction compared to traditional methods like interior point optimization, gradient descent, branch and bound, and a population-based algorithm named Golden Jackal optimization. In case 1, the overall cost in scenario 1 and scenario 2 was reduced from 1620.4 rupees to 1422.84 rupees, whereas, in case 2, the total cost was reduced from 12,350 rupees to 12,017 rupees with the proposed hybrid ASFOGDO algorithm. Further, a detailed impact of attacks and outliers on scheduling, operational cost, and reliability of supply is presented in case 3.https://www.mdpi.com/2411-9660/9/3/63distributed energy sourcesuncertaintyenergy managementdenial of service attacksfalse data injection attacks |
| spellingShingle | Sri Harish Nandigam Krishna Mohan Reddy Pothireddy K. Nageswara Rao Surender Reddy Salkuti Hybrid Adaptive Sheep Flock Optimization and Gradient Descent Optimization for Energy Management in a Grid-Connected Microgrid Designs distributed energy sources uncertainty energy management denial of service attacks false data injection attacks |
| title | Hybrid Adaptive Sheep Flock Optimization and Gradient Descent Optimization for Energy Management in a Grid-Connected Microgrid |
| title_full | Hybrid Adaptive Sheep Flock Optimization and Gradient Descent Optimization for Energy Management in a Grid-Connected Microgrid |
| title_fullStr | Hybrid Adaptive Sheep Flock Optimization and Gradient Descent Optimization for Energy Management in a Grid-Connected Microgrid |
| title_full_unstemmed | Hybrid Adaptive Sheep Flock Optimization and Gradient Descent Optimization for Energy Management in a Grid-Connected Microgrid |
| title_short | Hybrid Adaptive Sheep Flock Optimization and Gradient Descent Optimization for Energy Management in a Grid-Connected Microgrid |
| title_sort | hybrid adaptive sheep flock optimization and gradient descent optimization for energy management in a grid connected microgrid |
| topic | distributed energy sources uncertainty energy management denial of service attacks false data injection attacks |
| url | https://www.mdpi.com/2411-9660/9/3/63 |
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