Next-Generation energy Management: Particle Density algorithm for residential microgrid optimization
This study presents an innovative approach to optimize energy management in residential microgrids, in light of the rising demand for energy and mounting environmental concerns. The research underscores the vital role of efficient energy management and responsive load control to improve energy effic...
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| Language: | English |
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Elsevier
2025-03-01
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| Series: | Egyptian Informatics Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866525000039 |
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| author | Liang Wang Nan Sun |
| author_facet | Liang Wang Nan Sun |
| author_sort | Liang Wang |
| collection | DOAJ |
| description | This study presents an innovative approach to optimize energy management in residential microgrids, in light of the rising demand for energy and mounting environmental concerns. The research underscores the vital role of efficient energy management and responsive load control to improve energy efficiency and reduce consumer costs. To this end, a framework is proposed in which a power aggregator operates within a microgrid to manage residential electricity consumption. The primary goal of this framework is to minimize energy costs while considering subscriber preferences and the capacity limitations of the distribution network. The improved particle swarm optimization (IPSO) algorithm is employed to optimize energy management, resolve convergence challenges, and ensure user requirements are effectively prioritized. Integrating emergency, economic, and planned strategies provides cost savings, ensures grid stability, and enhances user satisfaction. The incorporation of Internet of Things (IoT) technology enables seamless communication, precise device control, and data-driven decision-making, empowering households to manage their energy loads more effectively and contribute to grid efficiency. Through scenario analysis, this research demonstrates the IPSO algorithm’s potential for significant cost reductions and improved grid stability. In Scenario 1, focused exclusively on affordability, numerical analyses present the total cost of electricity under different load conditions over three months. Scenario 2, also prioritizing affordability, highlights the impact of economic considerations on electricity expenses. Furthermore, Scenario 3 (80 % emergency + 20 % affordable) and Scenario 4 (50 % emergency + 20 % affordable + 30 % planned) showcase the potential for cost reduction through various priority combinations. These insights reflect the effectiveness of load management strategies facilitated by IoT technology. This comprehensive energy management approach lays a strong foundation for a resilient and adaptable energy infrastructure that meets society’s evolving demands. |
| format | Article |
| id | doaj-art-9982ef2891a04d018787920d2995ec42 |
| institution | Kabale University |
| issn | 1110-8665 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-9982ef2891a04d018787920d2995ec422025-01-12T05:24:23ZengElsevierEgyptian Informatics Journal1110-86652025-03-0129100611Next-Generation energy Management: Particle Density algorithm for residential microgrid optimizationLiang Wang0Nan Sun1School of Mechanical Engineering, Southeast University, Nanjing 211189, Jiangsu, ChinaChangchun Humanities and Sciences College, Changchun 130117, Jilin, China; Corresponding author.This study presents an innovative approach to optimize energy management in residential microgrids, in light of the rising demand for energy and mounting environmental concerns. The research underscores the vital role of efficient energy management and responsive load control to improve energy efficiency and reduce consumer costs. To this end, a framework is proposed in which a power aggregator operates within a microgrid to manage residential electricity consumption. The primary goal of this framework is to minimize energy costs while considering subscriber preferences and the capacity limitations of the distribution network. The improved particle swarm optimization (IPSO) algorithm is employed to optimize energy management, resolve convergence challenges, and ensure user requirements are effectively prioritized. Integrating emergency, economic, and planned strategies provides cost savings, ensures grid stability, and enhances user satisfaction. The incorporation of Internet of Things (IoT) technology enables seamless communication, precise device control, and data-driven decision-making, empowering households to manage their energy loads more effectively and contribute to grid efficiency. Through scenario analysis, this research demonstrates the IPSO algorithm’s potential for significant cost reductions and improved grid stability. In Scenario 1, focused exclusively on affordability, numerical analyses present the total cost of electricity under different load conditions over three months. Scenario 2, also prioritizing affordability, highlights the impact of economic considerations on electricity expenses. Furthermore, Scenario 3 (80 % emergency + 20 % affordable) and Scenario 4 (50 % emergency + 20 % affordable + 30 % planned) showcase the potential for cost reduction through various priority combinations. These insights reflect the effectiveness of load management strategies facilitated by IoT technology. This comprehensive energy management approach lays a strong foundation for a resilient and adaptable energy infrastructure that meets society’s evolving demands.http://www.sciencedirect.com/science/article/pii/S1110866525000039Consumption ManagementUtilization PriorityImproved Particle Swarm Optimization AlgorithmSmart Building |
| spellingShingle | Liang Wang Nan Sun Next-Generation energy Management: Particle Density algorithm for residential microgrid optimization Egyptian Informatics Journal Consumption Management Utilization Priority Improved Particle Swarm Optimization Algorithm Smart Building |
| title | Next-Generation energy Management: Particle Density algorithm for residential microgrid optimization |
| title_full | Next-Generation energy Management: Particle Density algorithm for residential microgrid optimization |
| title_fullStr | Next-Generation energy Management: Particle Density algorithm for residential microgrid optimization |
| title_full_unstemmed | Next-Generation energy Management: Particle Density algorithm for residential microgrid optimization |
| title_short | Next-Generation energy Management: Particle Density algorithm for residential microgrid optimization |
| title_sort | next generation energy management particle density algorithm for residential microgrid optimization |
| topic | Consumption Management Utilization Priority Improved Particle Swarm Optimization Algorithm Smart Building |
| url | http://www.sciencedirect.com/science/article/pii/S1110866525000039 |
| work_keys_str_mv | AT liangwang nextgenerationenergymanagementparticledensityalgorithmforresidentialmicrogridoptimization AT nansun nextgenerationenergymanagementparticledensityalgorithmforresidentialmicrogridoptimization |