مشروع البحث: A NOVEL APPROACH FOR INTEGRATING RENEWABLE ENERGY SOURCES INTO THE GRID USING MULTI-STAGE PARTICLE SWARM OPTIMIZATION
| dc.contributor.advisor | PROF. DR. NEYRE TEKBIYIK ERSOY | |
| dc.contributor.advisor | PROF. DR. MUSTAFA DAĞBAŞI | |
| dc.date.accessioned | 2025-10-28T07:42:51Z | |
| dc.date.available | 2025-10-28T07:42:51Z | |
| dc.description | a novel multi-stage PSO technique has been suggested to solve complex UC problems with high and guaranteed rate of convergence. This technique has the capability to solve hybrid UC problems containing thermal, wind, and solar generating units as well as storage batteries. The objective function is optimized to achieve minimum fuel cost as well as minimum gas emissions while respecting the system constraints. | |
| dc.description.abstract | The demand for electrical energy is traditionally met by thermal power generation units. However, these systems have drawbacks of high generation/operational costs and harmful emissions. Therefore, in order to reduce generation and emission costs, a reliable unit commitment strategy is needed to optimally combine different thermal and renewable energy generating units. Unit Commitment requires that generating units operate optimally every hour at varying loads, and under different technical and environmental constraints. Many researchers are still working on improving techniques to solve the unit commitment problem. In recent years, | |
| dc.identifier | 788 | |
| dc.identifier.uri | https://dspace.academy.edu.ly/handle/123456789/1812 | |
| dc.subject | USING MULTI-STAGE PARTICLE SWARM OPTIMIZATION | |
| dc.title | A NOVEL APPROACH FOR INTEGRATING RENEWABLE ENERGY SOURCES INTO THE GRID USING MULTI-STAGE PARTICLE SWARM OPTIMIZATION | |
| dspace.entity.type | Project | |
| project.endDate | 2024 | |
| project.funder.name | هندسة الطاقة البديلة | |
| project.investigator | جاد جمعة ابوعجيله محمد | |
| project.startDate | 2023 | |
| relation.isOrgUnitOfProject | 91d97a25-5e85-4d40-9b5e-d052a7b22d73 | |
| relation.isOrgUnitOfProject.latestForDiscovery | 91d97a25-5e85-4d40-9b5e-d052a7b22d73 |