Unit Commitment Mempertimbangkan Energy Storage System Dengan Metode Binary Particle Swarm Optimization Pada Sistem Ieee Microgrid

Wardhana, Muhammad Wisnu (2021) Unit Commitment Mempertimbangkan Energy Storage System Dengan Metode Binary Particle Swarm Optimization Pada Sistem Ieee Microgrid. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

Text 07111740000061-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only Download (1MB) | Request a copy

Abstract

Energi terbarukan mempunyai peran penting dalam mengurangi polusi yang dihasilkan sistem pembangkitan. Perkembangan energi terbarukan seperti turbin angin telah meningkat pesat dalam beberapa tahun terakhir. Namun, penetrasi turbin angin dapat mempengaruhi kestabilan dan keamanan di sistem tenaga listrik karena menghasilkan daya pembangkitan yang tidak pasti. Oleh karena itu perlu untuk dilakukan prediksi kecepatan angin. Pada penelitian ini metode Auto Regressive Integrated Moving Average (ARIMA) akan digunakan untuk memprediksi kecepatan angin sehingga daya yang dihasilkan turbin angin setiap jamnya bisa diketahui. Setelah mendapatkan nilai daya yang dihasilkan oleh turbin angin kemudian dilakukan optimasi penjadwalan dengan pembangkit termal menggunakan Unit Commitment dengan metode Binary Particle Swarm Optimization (BPSO) untuk menentukan pola pembangkitan dengan biaya yang paling ekonomis dengan mempertimbangkan Energy Storage System (ESS) dan tanpa ESS. Pada penelitian ini prediksi kecepatan angin yang dilakukan 5 hari secara acak menghasilkan nilai rata-rata error sebesar 1,07 m/s dan MAPE 10,95%. Proses optimasi dengan menambahkan pembangkit tenaga angin mampu mengurangi biaya pembangkitan sebesar $3006,36. Pada simulasi berikutnya ditambahkan ESS pada sistem dan mampu mengurangi biaya pembangkitan sebesar $4866,28 dibandingkan dengan tidak menggunakan ESS.
====================================================================================================
Renewable energy has an important role in reducing pollution generated by the generation system. The development of renewable energy such as wind turbine has increased rapidly in recent years. However, wind turbine penetration can affect the stability and safety of the electric power system because it produces uncertain generating power. Therefore, it is necessary to predict the wind speed. In this study, Auto Regressive Integrated Moving Average (ARIMA) method will be used to predict wind speed so that the power generated by the wind turbine every hour can be known. After getting the value of the power generated by the wind turbine, scheduling optimization is carried out with thermal generators using Unit Commitment with the Binary Particle Swarm Optimization (BPSO) method to determine the generation pattern with the most economical cost by considering the Energy Storage System (ESS) and without ESS. In this study, the prediction of wind speed for 5 days randomly resulted in an average error of 1.07 m/s and a MAPE of 10.95%. The optimization process by adding wind power was able to reduce the generation cost by $3006,36. In the next simulation, ESS was added to the system and was able to reduce the generation cost by $4866,28 compared to not using ESS.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Turbin Angin, ARIMA, Energy Storage System, Unit Commitment, Binary Particle Swarm Optimization, Wind Turbine, ARIMA, Energy Storage System, Unit Commitment, Binary Particle Swarm Optimization.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1001 Production of electric energy or power
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis
Depositing User:	Muhammad Wisnu Wardhana
Date Deposited:	17 Aug 2021 04:08
Last Modified:	17 Aug 2021 07:36
URI:	http://repository.its.ac.id/id/eprint/87655

Actions (login required)

View Item