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All Journal Jurnal Teknologi dan Manajemen Industri Terapan Jurnal Teknik Indonesia
Herry Nugraha
Institut Teknologi PLN
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Transportation

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Optimasi Unit Commitment PLTMG Duri Menggunakan Binary Particle Swarm Optimization Dengan Mempertimbangkan Kendala Operasi Rendy Abdullah; Herry Nugraha; Rudy Setiabudy
Jurnal Teknik Indonesia Vol. 5 No. 3 (2026): Jurnal Teknik Indonesia
Publisher : Publica Scientific Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58860/jti.v5i3.937

Abstract

Penelitian ini bertujuan mengembangkan model Unit Commitment (UC) berbasis Binary Particle Swarm Optimization (BPSO) yang mengintegrasikan karakteristik heat rate dengan kendala operasi aktual PLTMG Duri, yaitu inefisiensi start-up, derating temporer, dan gas trip, serta membandingkan hasil Unit Commitment yang diperoleh dengan pola Unit Commitment eksisting. Fungsi biaya bahan bakar tujuh unit dibentuk melalui curve fitting data heat rate, kemudian dipadukan dengan tiga komponen ekspektasi biaya kendala operasi ke dalam fungsi objektif. Selanjutnya, algoritma BPSO diterapkan dengan mekanisme repair untuk memperbaiki solusi yang melanggar batasan operasi, seperti batas kapasitas unit, jadwal pemeliharaan, dan keseimbangan daya, sehingga dihasilkan jadwal Unit Commitment berupa status ON/OFF unit yang optimal selama satu minggu simulasi (1–7 Mei 2026) menggunakan data historis operasi PLTMG Duri selama enam bulan. Hasil optimasi menunjukkan bahwa algoritma BPSO mencapai konvergensi pada iterasi ke-270 dengan total biaya operasional sebesar Rp9.447.787.193,61, lebih rendah dibandingkan Unit Commitment eksisting sebesar Rp9.552.661.959,47, sehingga menghasilkan penghematan sebesar Rp104.874.765,86 tanpa mengurangi energi yang disalurkan (9.424,66 MWh). Meskipun frekuensi start-stop meningkat dari 49 menjadi 83 kali, hasil optimasi mampu membentuk prioritas operasi unit yang lebih ekonomis. Temuan ini dapat menjadi dasar bagi PT PLN Nusantara Power dalam menyusun strategi Unit Commitment PLTMG Duri yang lebih efisien, dengan kebaruan berupa integrasi kendala operasional granular berbasis kejadian lapangan ke dalam formulasi UC-BPSO.
Machine Learning-Based Daily Feeder Load Forecasting for Operation and Maintenance Strategy in 20 kV Distribution Systems Fika Ardilla Rista; Herry Nugraha; Widya Nita Suliyanti
Jurnal Teknologi dan Manajemen Industri Terapan Vol. 5 No. 3 (2026): Jurnal Teknologi dan Manajemen Industri Terapan
Publisher : Yayasan Inovasi Kemajuan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55826/jtmit.v5i3.2091

Abstract

Accurate daily feeder load forecasting is essential for supporting operation and maintenance strategies in electrical distribution systems, where nonlinear and dynamic load variations may limit the performance of conventional forecasting methods. This study develops a machine learning-based framework for daily load forecasting of six 20 kV distribution feeders (PLR16–PLR21) supplied by Transformer 1 at Palur Substation, Indonesia. We used daily operational load data from January 2020 to April 2026, incorporating customer-growth information as an additional forecasting feature. We used Random Forest as the primary model and Holt–Winters Exponential Smoothing as a benchmark. We evaluated forecasting performance using MAE, RMSE, MAPE, and sMAPE. Random Forest outperformed Holt–Winters, achieving MAE of 292.45 A, RMSE of 417.85 A, and MAPE of 12.98%, compared with 720.30 A, 853.37 A, and 26.79%, respectively. The selected model was further applied to forecast feeder loading through 2028 and identify priority feeders for operation and maintenance planning. The proposed framework provides a practical, data-driven approach to feeder load forecasting and operational decision support in 20 kV distribution systems.
Co-Authors Fika Ardilla Rista Rendy Abdullah Rudy Setiabudy Widya Nita Suliyanti