Asset Details
Do you wish to reserve the book?
Multi-objective optimization of hybrid microgrid for energy trilemma goals using slime mould algorithm
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Multi-objective optimization of hybrid microgrid for energy trilemma goals using slime mould algorithm
Do you wish to request the book?
Multi-objective optimization of hybrid microgrid for energy trilemma goals using slime mould algorithm
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Multi-objective optimization of hybrid microgrid for energy trilemma goals using slime mould algorithm
2025
Overview
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.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
