Enhanced energy management in smart microgrids using hybrid optimization and demand response strategies
This paper presents a groundbreaking optimization model for efficient and resilient energy management in smart microgrids, particularly addressing challenges posed by decentralized renewable energy sources (RES) integration. The proposed model advances microgrid performance by minimizing operational...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061524006458 |
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| author | Lei Yang Lile Wu Genzhu Li |
| author_facet | Lei Yang Lile Wu Genzhu Li |
| author_sort | Lei Yang |
| collection | DOAJ |
| description | This paper presents a groundbreaking optimization model for efficient and resilient energy management in smart microgrids, particularly addressing challenges posed by decentralized renewable energy sources (RES) integration. The proposed model advances microgrid performance by minimizing operational costs, reducing pollution emissions, and maximizing RES utilization. A distinctive feature of this model is its hybrid strategy, which combines Demand Response Plans (DRPs) with an Incline Block Tariff (IBT). This approach engages industrial, commercial, and residential consumers to manage the inherent uncertainty and non-linearity of RES, thereby enhancing operational flexibility and reliability. In addition, a two-stage adjustable robust optimization model is introduced, supporting day-ahead planning that ensures secure and cost-effective energy management even under significant uncertainties in renewable energy production. The model is evaluated through two scenarios—operational cost and pollution emissions, and operational cost and availability—both with and without the DRPS and IBT scheme. Simulation results demonstrate that the model achieves substantial reductions in both costs and emissions while significantly improving system reliability and availability. The hybrid optimization model significantly enhances smart microgrid management. With the combined DRPS and IBT scheme, operational costs dropped by 23.21%, and emissions from wind and solar power decreased by 7.7% and 3.7%, respectively. This model improves cost-effectiveness and availability, optimizing renewable energy integration in microgrids. |
| format | Article |
| id | doaj-art-7125eb438e024ba3b14b9a13a0e2c163 |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-7125eb438e024ba3b14b9a13a0e2c1632025-01-19T06:23:59ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-03-01164110421Enhanced energy management in smart microgrids using hybrid optimization and demand response strategiesLei Yang0Lile Wu1Genzhu Li2Power China Zhongnan Engineering Corporation Limited, Changsha 410014, Hunan, ChinaNorth China University of Water Resources and Electric Power, Zhengzhou 450045, Henan, China; Xi’an Jiaotong University, Xi’an 710049, Shanxi, China; Henan Electric Power Survey & Design Institute, Zhengzhou, China; Corresponding author.School of Economic and Management, North China Electric Power University, Beijing 102206, ChinaThis paper presents a groundbreaking optimization model for efficient and resilient energy management in smart microgrids, particularly addressing challenges posed by decentralized renewable energy sources (RES) integration. The proposed model advances microgrid performance by minimizing operational costs, reducing pollution emissions, and maximizing RES utilization. A distinctive feature of this model is its hybrid strategy, which combines Demand Response Plans (DRPs) with an Incline Block Tariff (IBT). This approach engages industrial, commercial, and residential consumers to manage the inherent uncertainty and non-linearity of RES, thereby enhancing operational flexibility and reliability. In addition, a two-stage adjustable robust optimization model is introduced, supporting day-ahead planning that ensures secure and cost-effective energy management even under significant uncertainties in renewable energy production. The model is evaluated through two scenarios—operational cost and pollution emissions, and operational cost and availability—both with and without the DRPS and IBT scheme. Simulation results demonstrate that the model achieves substantial reductions in both costs and emissions while significantly improving system reliability and availability. The hybrid optimization model significantly enhances smart microgrid management. With the combined DRPS and IBT scheme, operational costs dropped by 23.21%, and emissions from wind and solar power decreased by 7.7% and 3.7%, respectively. This model improves cost-effectiveness and availability, optimizing renewable energy integration in microgrids.http://www.sciencedirect.com/science/article/pii/S0142061524006458Demand response plansHybrid optimizationPollution emissionsRenewable energy sourcesSmart microgrids |
| spellingShingle | Lei Yang Lile Wu Genzhu Li Enhanced energy management in smart microgrids using hybrid optimization and demand response strategies International Journal of Electrical Power & Energy Systems Demand response plans Hybrid optimization Pollution emissions Renewable energy sources Smart microgrids |
| title | Enhanced energy management in smart microgrids using hybrid optimization and demand response strategies |
| title_full | Enhanced energy management in smart microgrids using hybrid optimization and demand response strategies |
| title_fullStr | Enhanced energy management in smart microgrids using hybrid optimization and demand response strategies |
| title_full_unstemmed | Enhanced energy management in smart microgrids using hybrid optimization and demand response strategies |
| title_short | Enhanced energy management in smart microgrids using hybrid optimization and demand response strategies |
| title_sort | enhanced energy management in smart microgrids using hybrid optimization and demand response strategies |
| topic | Demand response plans Hybrid optimization Pollution emissions Renewable energy sources Smart microgrids |
| url | http://www.sciencedirect.com/science/article/pii/S0142061524006458 |
| work_keys_str_mv | AT leiyang enhancedenergymanagementinsmartmicrogridsusinghybridoptimizationanddemandresponsestrategies AT lilewu enhancedenergymanagementinsmartmicrogridsusinghybridoptimizationanddemandresponsestrategies AT genzhuli enhancedenergymanagementinsmartmicrogridsusinghybridoptimizationanddemandresponsestrategies |