Penjadwalan Pembangkit Tenaga Listrik menggunakan Mixed Integer Linear Programming Dengan Integrasi Peramalan Daya Energi Terbarukan

Wanasida, Nathanael Dylan (2026) Penjadwalan Pembangkit Tenaga Listrik menggunakan Mixed Integer Linear Programming Dengan Integrasi Peramalan Daya Energi Terbarukan. Other thesis, Institut Teknologi Sepuluh Nopember.

Text 5022201080-Undergraduate_Thesis.pdf Download (6MB)

Abstract

Semakin banyak pembangkit yang emnggunakan sumber energi terbarukan, sumber tersebut mempengaruhi produksi daya pembangkit tersebut. Dimana PLTS dipengaruhi oleh solar irradiasi dan PLTB dipengaruhi oleh kecepatan angin. Kedua variabel tersebut juga dipengaruhi oleh faktor luar seperti iklim dan cuaca. Dikarenakan ketidakstabilan jumlah daya yang dibangkitkan, membuat berbagi masalah seperti, di jam tertentu daya yang seharusnya dibutuhkan untuk memenuhi bena titdak dapat digunakan dikarenakan pembnagkit tidak dapat mencapai potensi, atau disaat pembangkit memproduksi daya tetapi bebean sudah terpenuhi dan BESS juga sudah penuh maka pembangkit tidak dapat mencapai potensi sebenarnya. Maka itu digunaakn model LSTM (long-short term memory), teknik deep learning untuk memahami pola agar dapat memprediksi daya dan emmbuat daya yang terjadwal oleh prmbangkit PLTS dan PLTB yang akhirnya dapat membuat rencana untuk hari kedepannya jadwal-jadwal pembangkit untuk memenuhi beban pada jam tertentu. untuk mencapai ini digunakan metode MILP dimana dapat mengahsilkan daya continuous dan integer.
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The increase of power plants using renewable energy sources is affecting power generated by the power plants itself due to the unstable nature of the energy source. Power plants using energies like, wind and solar are generating power uncontrollably. Wind energy is affected by wind speed variable, and solar energy is affected by solar irradiance variable. Both variables are affected by weather, nature and climate. Due to the erratic behavior in power generated, it is causing problem such as, power generated that is supposed to fulfill a load demand cannot fill it because the power plant cannot reach its’ potential due to if using solar panels, the solar panels not getting enough sun. Or another case, being the load demand is fulfilled and the BESS in the system is full the plant is forced to turn off, due to the power being generated has nowhere else to go resulted in wasted power. That is why using Deep Learning method LSTM to understand the pattern of power being generated by the renewable power plants so the model can create forecasted power. And that forecasted power can be used in dispatch scheduling for the day that comes after. To do this the model is using the MILP method to schedule the dispatching of every power plant in the power system.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Energi Terbarukan, MILP (Mixed-Integer Linear Programming), RNN (Recurrent Neural Network), Ramalan Daya, Sistem Energi Manajemen, Renewable Energy, Power Forecasting, Energy Management System
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1007 Electric power systems control
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis
Depositing User:	Nathanael Dylan Wanasida
Date Deposited:	24 Jul 2026 02:57
Last Modified:	24 Jul 2026 04:15
URI:	http://repository.its.ac.id/id/eprint/136909

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