Transactive energy management for efficient scheduling and storage utilization in a grid-connected renewable energy-based microgrid
This study presents an advanced Transactive Energy Management (TEM) approach employing the Slime Mould Algorithm (SMA) to optimize scheduling and storage utilization in grid-connected renewable energy microgrids. SMA's adaptability enables effective management of renewable variability, maximizi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S277267112500021X |
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| author | Peter Anuoluwapo Gbadega Olufunke Abolaji Balogun |
| author_facet | Peter Anuoluwapo Gbadega Olufunke Abolaji Balogun |
| author_sort | Peter Anuoluwapo Gbadega |
| collection | DOAJ |
| description | This study presents an advanced Transactive Energy Management (TEM) approach employing the Slime Mould Algorithm (SMA) to optimize scheduling and storage utilization in grid-connected renewable energy microgrids. SMA's adaptability enables effective management of renewable variability, maximizing energy efficiency while minimizing operational costs and emissions. The study evaluates SMA's performance through simulations of two scenarios: with and without battery storage. In the non-storage scenario, SMA reduces operational costs by optimizing distributed generation and grid transactions. However, in the storage-integrated scenario, SMA demonstrates substantial advantages, achieving 20–48% cost savings by leveraging optimal charging and discharging cycles. This underscores the critical role of energy storage in stabilizing costs and reducing reliance on grid power during high-price intervals. Additionally, the inclusion of storage contributes to 25–38% emission reductions by enhancing renewable energy utilization and minimizing dependency on fossil-fuel-generated electricity. Comparative analysis reveals that SMA consistently outperforms conventional methods such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in terms of convergence speed and computational efficiency, making it particularly suitable for real-time energy management. SMA achieves faster convergence, ensuring timely decision-making even in dynamic market conditions. This research highlights the critical role of advanced energy management strategies and battery storage in improving economic and operational efficiency in renewable energy microgrids. |
| format | Article |
| id | doaj-art-63d38ac5c5644aa3b33ecd6340577f34 |
| institution | Kabale University |
| issn | 2772-6711 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj-art-63d38ac5c5644aa3b33ecd6340577f342025-01-31T05:12:47ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112025-03-0111100914Transactive energy management for efficient scheduling and storage utilization in a grid-connected renewable energy-based microgridPeter Anuoluwapo Gbadega0Olufunke Abolaji Balogun1Corresponding author.; Department of Electrical and Electronic Engineering Science, Faculty of Engineering and the Built Environment, University of Johannesburg, Auckland Park Kingsway Campus, Johannesburg 2006, South AfricaDepartment of Electrical and Electronic Engineering Science, Faculty of Engineering and the Built Environment, University of Johannesburg, Auckland Park Kingsway Campus, Johannesburg 2006, South AfricaThis study presents an advanced Transactive Energy Management (TEM) approach employing the Slime Mould Algorithm (SMA) to optimize scheduling and storage utilization in grid-connected renewable energy microgrids. SMA's adaptability enables effective management of renewable variability, maximizing energy efficiency while minimizing operational costs and emissions. The study evaluates SMA's performance through simulations of two scenarios: with and without battery storage. In the non-storage scenario, SMA reduces operational costs by optimizing distributed generation and grid transactions. However, in the storage-integrated scenario, SMA demonstrates substantial advantages, achieving 20–48% cost savings by leveraging optimal charging and discharging cycles. This underscores the critical role of energy storage in stabilizing costs and reducing reliance on grid power during high-price intervals. Additionally, the inclusion of storage contributes to 25–38% emission reductions by enhancing renewable energy utilization and minimizing dependency on fossil-fuel-generated electricity. Comparative analysis reveals that SMA consistently outperforms conventional methods such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in terms of convergence speed and computational efficiency, making it particularly suitable for real-time energy management. SMA achieves faster convergence, ensuring timely decision-making even in dynamic market conditions. This research highlights the critical role of advanced energy management strategies and battery storage in improving economic and operational efficiency in renewable energy microgrids.http://www.sciencedirect.com/science/article/pii/S277267112500021XSlime mould algorithmGrid-connected energy schedulingTransactive energy managementRenewable energy-based microgridBattery storage optimization |
| spellingShingle | Peter Anuoluwapo Gbadega Olufunke Abolaji Balogun Transactive energy management for efficient scheduling and storage utilization in a grid-connected renewable energy-based microgrid e-Prime: Advances in Electrical Engineering, Electronics and Energy Slime mould algorithm Grid-connected energy scheduling Transactive energy management Renewable energy-based microgrid Battery storage optimization |
| title | Transactive energy management for efficient scheduling and storage utilization in a grid-connected renewable energy-based microgrid |
| title_full | Transactive energy management for efficient scheduling and storage utilization in a grid-connected renewable energy-based microgrid |
| title_fullStr | Transactive energy management for efficient scheduling and storage utilization in a grid-connected renewable energy-based microgrid |
| title_full_unstemmed | Transactive energy management for efficient scheduling and storage utilization in a grid-connected renewable energy-based microgrid |
| title_short | Transactive energy management for efficient scheduling and storage utilization in a grid-connected renewable energy-based microgrid |
| title_sort | transactive energy management for efficient scheduling and storage utilization in a grid connected renewable energy based microgrid |
| topic | Slime mould algorithm Grid-connected energy scheduling Transactive energy management Renewable energy-based microgrid Battery storage optimization |
| url | http://www.sciencedirect.com/science/article/pii/S277267112500021X |
| work_keys_str_mv | AT peteranuoluwapogbadega transactiveenergymanagementforefficientschedulingandstorageutilizationinagridconnectedrenewableenergybasedmicrogrid AT olufunkeabolajibalogun transactiveenergymanagementforefficientschedulingandstorageutilizationinagridconnectedrenewableenergybasedmicrogrid |