Multi-objective optimization of hybrid microgrid for energy trilemma goals using slime mould algorithm
Abstract This study presents a multi-objective optimization of a hybrid microgrid (HMG) targeting the energy trilemma goals—energy security, affordability, and sustainability—using the Slime Mould Algorithm (SMA). The proposed HMG integrates renewable energy sources, diesel generators, and electric...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-15207-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849332719435120640 |
|---|---|
| author | Alok Kumar Shrivastav Soham Dutta |
| author_facet | Alok Kumar Shrivastav Soham Dutta |
| author_sort | Alok Kumar Shrivastav |
| collection | DOAJ |
| description | Abstract This study presents a multi-objective optimization of a hybrid microgrid (HMG) targeting the energy trilemma goals—energy security, affordability, and sustainability—using the Slime Mould Algorithm (SMA). The proposed HMG integrates renewable energy sources, diesel generators, and electric vehicle (EV) batteries as distributed energy resources (DERs) with bidirectional vehicle-to-grid (V2G) capabilities. Compared to conventional metaheuristic such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), the SMA achieves a power loss reduction of 12.3% and a levelized cost of energy (LCOE) improvement of 9.8%. The loss of power supply probability (LPSP) is reduced to 0.012, outperforming benchmark results from HOMER and Salp Swarm Algorithm (SSA), which reported LPSP values of 0.021 and 0.017, respectively. The superior performance of SMA is attributed to its dynamic balance between exploration and exploitation, leading to faster convergence and enhanced computational efficiency. The novel integration of EV batteries as DERs, with explicit modeling of bidirectional V2G operations, distinguishes this work from previous studies that considered only unidirectional or static EV participation. While the proposed approach demonstrates significant improvements, scalability to larger microgrid networks and the computational demands of SMA in real-time applications remain challenges for future research. |
| format | Article |
| id | doaj-art-dfa320f19cfa4024b3c4af494c15927c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-dfa320f19cfa4024b3c4af494c15927c2025-08-20T03:46:07ZengNature PortfolioScientific Reports2045-23222025-08-0115112210.1038/s41598-025-15207-1Multi-objective optimization of hybrid microgrid for energy trilemma goals using slime mould algorithmAlok Kumar Shrivastav0Soham Dutta1Department of Electrical Engineering, JIS College of EngineeringDepartment of Electrical Engineering, JIS College of EngineeringAbstract This study presents a multi-objective optimization of a hybrid microgrid (HMG) targeting the energy trilemma goals—energy security, affordability, and sustainability—using the Slime Mould Algorithm (SMA). The proposed HMG integrates renewable energy sources, diesel generators, and electric vehicle (EV) batteries as distributed energy resources (DERs) with bidirectional vehicle-to-grid (V2G) capabilities. Compared to conventional metaheuristic such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), the SMA achieves a power loss reduction of 12.3% and a levelized cost of energy (LCOE) improvement of 9.8%. The loss of power supply probability (LPSP) is reduced to 0.012, outperforming benchmark results from HOMER and Salp Swarm Algorithm (SSA), which reported LPSP values of 0.021 and 0.017, respectively. The superior performance of SMA is attributed to its dynamic balance between exploration and exploitation, leading to faster convergence and enhanced computational efficiency. The novel integration of EV batteries as DERs, with explicit modeling of bidirectional V2G operations, distinguishes this work from previous studies that considered only unidirectional or static EV participation. While the proposed approach demonstrates significant improvements, scalability to larger microgrid networks and the computational demands of SMA in real-time applications remain challenges for future research.https://doi.org/10.1038/s41598-025-15207-1Carbon emission reductionDistributed generationElectric vehicle integrationEnergy trilemmaIEEE 33-bus systemLevelized cost of energy |
| spellingShingle | Alok Kumar Shrivastav Soham Dutta Multi-objective optimization of hybrid microgrid for energy trilemma goals using slime mould algorithm Scientific Reports Carbon emission reduction Distributed generation Electric vehicle integration Energy trilemma IEEE 33-bus system Levelized cost of energy |
| title | Multi-objective optimization of hybrid microgrid for energy trilemma goals using slime mould algorithm |
| title_full | Multi-objective optimization of hybrid microgrid for energy trilemma goals using slime mould algorithm |
| title_fullStr | Multi-objective optimization of hybrid microgrid for energy trilemma goals using slime mould algorithm |
| title_full_unstemmed | Multi-objective optimization of hybrid microgrid for energy trilemma goals using slime mould algorithm |
| title_short | Multi-objective optimization of hybrid microgrid for energy trilemma goals using slime mould algorithm |
| title_sort | multi objective optimization of hybrid microgrid for energy trilemma goals using slime mould algorithm |
| topic | Carbon emission reduction Distributed generation Electric vehicle integration Energy trilemma IEEE 33-bus system Levelized cost of energy |
| url | https://doi.org/10.1038/s41598-025-15207-1 |
| work_keys_str_mv | AT alokkumarshrivastav multiobjectiveoptimizationofhybridmicrogridforenergytrilemmagoalsusingslimemouldalgorithm AT sohamdutta multiobjectiveoptimizationofhybridmicrogridforenergytrilemmagoalsusingslimemouldalgorithm |