Unit commitment in microgrid systems using artificial intelligence techniques
This study, conducted at the University of Jos, Nigeria, investigated methods to optimize unit commitment within microgrid systems in response to rising power supply costs. The research focused on managing the activation of various power sources, including micro-pumped hydro storage, sola...
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Academia.edu Journals
2024-08-01
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| Series: | Academia Green Energy |
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| author | Barnabas I. Gwaivangmin Ganiyu A. Bakare Ya’U S. Haruna Abdullahi L. Amoo |
| author_facet | Barnabas I. Gwaivangmin Ganiyu A. Bakare Ya’U S. Haruna Abdullahi L. Amoo |
| author_sort | Barnabas I. Gwaivangmin |
| collection | DOAJ |
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This study, conducted at the University of Jos, Nigeria, investigated methods to optimize unit commitment within microgrid systems in response to rising power supply costs. The research focused on managing the activation of various power sources, including micro-pumped hydro storage, solar photovoltaic (PV) systems, diesel generators, and public power supply, over a 24-hour period. Artificial intelligence optimization techniques were employed to dynamically control these sources, moving away from fixed operational schedules. The study compared three techniques: particle swarm optimization (PSO), Enhanced hybrid particle swarm optimization–ant colony optimization (E-HPSO–ACO), and Enhanced hybrid particle swarm optimization–simulated annealing (E-HPSO–SA). PSO resulted in a cost of N127,216, E-HPSO–ACO yielded N126,872, and E-HPSO–SA achieved N54,264. The research concluded that E-HPSO–SA significantly outperformed the other two techniques in terms of power generation cost, making it the most suitable method for unit commitment planning. Importantly, E-HPSO–SA demonstrated a substantial cost advantage over PSO and E-HPSO–ACO, exceeding them by approximately 57.4% and 52.5%, respectively. This translates to a significant improvement in cost-efficiency, ensuring a reliable and sustainable power supply while effectively mitigating rising expenses. Implementing E-HPSO–SA has the potential to enhance the economic viability of microgrid systems by meeting load demand requirements while maximizing operational efficiency, particularly in the face of escalating power supply costs. |
| format | Article |
| id | doaj-art-7b1ba143c2fe4a1385598b2ff487084d |
| institution | Kabale University |
| issn | 2998-3665 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Academia.edu Journals |
| record_format | Article |
| series | Academia Green Energy |
| spelling | doaj-art-7b1ba143c2fe4a1385598b2ff487084d2025-02-10T21:39:05ZengAcademia.edu JournalsAcademia Green Energy2998-36652024-08-011210.20935/AcadEnergy7313Unit commitment in microgrid systems using artificial intelligence techniquesBarnabas I. Gwaivangmin0Ganiyu A. Bakare1Ya’U S. Haruna2Abdullahi L. Amoo3Department of Electrical and Electronics Engineering, Abubakar Tafawa Balewa University, Bauchi, 740272, Nigeria.Department of Electrical and Electronics Engineering, Abubakar Tafawa Balewa University, Bauchi, 740272, Nigeria.Department of Electrical and Electronics Engineering, Abubakar Tafawa Balewa University, Bauchi, 740272, Nigeria.Department of Electrical and Electronics Engineering, Abubakar Tafawa Balewa University, Bauchi, 740272, Nigeria. This study, conducted at the University of Jos, Nigeria, investigated methods to optimize unit commitment within microgrid systems in response to rising power supply costs. The research focused on managing the activation of various power sources, including micro-pumped hydro storage, solar photovoltaic (PV) systems, diesel generators, and public power supply, over a 24-hour period. Artificial intelligence optimization techniques were employed to dynamically control these sources, moving away from fixed operational schedules. The study compared three techniques: particle swarm optimization (PSO), Enhanced hybrid particle swarm optimization–ant colony optimization (E-HPSO–ACO), and Enhanced hybrid particle swarm optimization–simulated annealing (E-HPSO–SA). PSO resulted in a cost of N127,216, E-HPSO–ACO yielded N126,872, and E-HPSO–SA achieved N54,264. The research concluded that E-HPSO–SA significantly outperformed the other two techniques in terms of power generation cost, making it the most suitable method for unit commitment planning. Importantly, E-HPSO–SA demonstrated a substantial cost advantage over PSO and E-HPSO–ACO, exceeding them by approximately 57.4% and 52.5%, respectively. This translates to a significant improvement in cost-efficiency, ensuring a reliable and sustainable power supply while effectively mitigating rising expenses. Implementing E-HPSO–SA has the potential to enhance the economic viability of microgrid systems by meeting load demand requirements while maximizing operational efficiency, particularly in the face of escalating power supply costs.https://www.academia.edu/123363645/Unit_commitment_in_microgrid_systems_using_artificial_intelligence_techniques |
| spellingShingle | Barnabas I. Gwaivangmin Ganiyu A. Bakare Ya’U S. Haruna Abdullahi L. Amoo Unit commitment in microgrid systems using artificial intelligence techniques Academia Green Energy |
| title | Unit commitment in microgrid systems using artificial intelligence techniques |
| title_full | Unit commitment in microgrid systems using artificial intelligence techniques |
| title_fullStr | Unit commitment in microgrid systems using artificial intelligence techniques |
| title_full_unstemmed | Unit commitment in microgrid systems using artificial intelligence techniques |
| title_short | Unit commitment in microgrid systems using artificial intelligence techniques |
| title_sort | unit commitment in microgrid systems using artificial intelligence techniques |
| url | https://www.academia.edu/123363645/Unit_commitment_in_microgrid_systems_using_artificial_intelligence_techniques |
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