Jomansyah, Muhammad Radif (2026) Quantum Binary Particle Swarm Optimization dalam Rekonfigurasi Jaringan Distribusi Dinamis dengan Penetrasi Photovoltaic dan Turbin Angin untuk Perbaikan Kestabilan Tegangan. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Integrasi sumber energi terbarukan berbasis photovoltaic (PV) dan turbin angin pada jaringan distribusi memberikan manfaat dalam mendukung transisi energi berkelanjutan, namun di sisi lain menimbulkan tantangan operasional akibat karakteristik daya keluaran yang bersifat intermiten. Variasi beban, produksi PV, dan turbin angin yang berubah sepanjang waktu dapat memengaruhi profil tegangan, meningkatkan rugi-rugi daya, serta menurunkan margin kestabilan tegangan sistem. Oleh karena itu, diperlukan strategi operasi yang mampu menjaga kinerja jaringan distribusi secara optimal pada berbagai kondisi operasi harian. Penelitian ini mengusulkan penerapan metode Quantum Binary Particle Swarm Optimization (QBPSO) untuk menyelesaikan permasalahan rekonfigurasi jaringan distribusi dinamis pada sistem radial 39-bus yang merepresentasikan jaringan distribusi di wilayah Ibu Kota Nusantara (IKN). Simulasi dilakukan selama periode operasi 24 jam dengan mempertimbangkan variasi profil beban, keluaran PV, dan turbin angin. Fungsi objektif dirancang untuk meminimalkan rugi-rugi daya sekaligus meningkatkan kestabilan tegangan menggunakan Voltage Stability Index (VSI). Analisis aliran daya dilakukan menggunakan metode Bus Injection to Branch Current–Branch Current to Bus Voltage (BIBC–BCBV). Hasil simulasi menunjukkan bahwa QBPSO mampu menurunkan total rugi-rugi daya harian dari 20.248,69 kW pada kondisi awal menjadi 14.044,33 kW atau sebesar 30,64%. Selain itu, nilai rata-rata tegangan minimum meningkat dari 0,92847 p.u. menjadi 0,95913 p.u., sedangkan rata-rata VSI membaik dari 0,18856 menjadi 0,10711. Pada kondisi beban puncak, tegangan minimum berhasil ditingkatkan dari 0,85172 p.u. menjadi 0,90980 p.u. Hasil tersebut menunjukkan bahwa QBPSO efektif dalam menentukan konfigurasi jaringan yang mampu meningkatkan efisiensi operasi, memperbaiki profil tegangan, dan memperkuat margin kestabilan tegangan pada sistem distribusi dengan penetrasi energi terbarukan.
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The integration of renewable energy sources based on photovoltaic (PV) systems and wind turbines into distribution networks supports the transition toward sustainable energy systems. However, the intermittent nature of renewable generation introduces operational challenges due to continuous variations in load demand, PV output, and wind power generation. These fluctuations may affect voltage profiles, increase power losses, and reduce the voltage stability margin of the network. Therefore, an effective operational strategy is required to maintain optimal distribution system performance under varying daily operating conditions. This study proposes the application of Quantum Binary Particle Swarm Optimization (QBPSO) for dynamic distribution network reconfiguration in a 39-bus radial distribution system representing the electricity distribution network of the Indonesian Capital City (IKN). Simulations are performed over a 24-hour operating horizon by considering time-varying load demand, PV generation, and wind turbine output profiles. The objective function is formulated to minimize power losses while enhancing voltage stability through the Voltage Stability Index (VSI). Power flow analysis is conducted using the Bus Injection to Branch Current–Branch Current to Bus Voltage (BIBC–BCBV) method. Simulation results demonstrate that QBPSO successfully reduces the total daily power loss from 20,248.69 kW in the base case to 14,044.33 kW, corresponding to a reduction of 30.64%. Furthermore, the average minimum voltage improves from 0.92847 p.u. to 0.95913 p.u., while the average VSI improves from 0.18856 to 0.10711. Under peak-load conditions, the minimum voltage is increased from 0.85172 p.u. to 0.90980 p.u. These results confirm that QBPSO is capable of identifying optimal network configurations that improve operational efficiency, enhance voltage profiles, and strengthen voltage stability margins in renewable-integrated distribution systems.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Quantum Binary Particle Swarm Optimization, rekonfigurasi jaringan distribusi, energi terbarukan, photovoltaic, turbin angin, kestabilan tegangan, voltage stability index. Quantum Binary Particle Swarm Optimization, distribution network reconfiguration, renewable energy, photovoltaic, wind turbine, voltage stability, voltage stability index. |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1010 Electric power system stability. Electric filters, Passive. |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20101-(S2) Master Thesis |
| Depositing User: | Muhammad Radif Jomansyah |
| Date Deposited: | 01 Aug 2026 07:08 |
| Last Modified: | 01 Aug 2026 07:08 |
| URI: | http://repository.its.ac.id/id/eprint/141521 |
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