Firdaus, Firdaus (2022) Perencanaan Optimal Sumber Energi Terbarukan Intermittent Pada Sistem Microgrid Mempertimbangkan Aspek Ekonomi, Lingkungan Dan Kualitas Daya. Doctoral thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Pemanfaatan sumber energi terbarukan seperti angin dan matahari dimasa yang akan datang menjadi keharusan seiring dengan semakin menipisnya sumber energi fosil. Sumber energi terbarukan telah banyak digunakan pada sistem distribusi atau sistem kelistrikan skala kecil. Sistem distribusi skala kecil yang dapat beroperasi secara mandiri disebut dengan sistem microgrid. Namun, sifat intermittent dan ketidakpastian dari sumber energi ini menjadi tantangan dan mempersulit operasi microgrid. Permasalahan lain yang lebih kompleks dari sistem microgrid dengan sumber energi terbarukan yang bersifat intermittent adalah menyeimbangkan antara permintaan daya dan ketersediaan sumber energi. Sistem penyimpan energi bisa dipasang sebagai energi cadangan untuk memasok permintaan dan mendukung persyaratan cadangan microgrid. Namun, karena sifat intermittent dari sumber energi terbarukan, maka membutuhkan mekanisme untuk memilih dan memanfaatkan sumber energi tersebut secara optimal. Oleh karena itu, dalam penelitian disertasi ini, dikembangkan model perencanaan untuk mengoptimalkan penggunaan sumber energi terbarukan yang bersifat intermittent. Model ini mencakup formulasi untuk memperoleh kapasitas dan lokasi serta biaya dari sumber energi terbarukan dan sistem penyimpan energi. Keputusan perencanaan optimal diperoleh dengan mempertimbangkan faktor ekonomi, lingkungan dan kualitas daya. Metode optimasi yang digunakan adalah Multi Verse Optimizer (MVO) dan metode Genetic Algorithm (GA) sebagai pembanding. Metode MVO dikembangkan dari multi verse dan teori big bang untuk memperoleh nilai optimal perencanaan sumber energi terbarukan. Hasil penelitian menunjukkan bahwa penempatan optimal Distributed Generation (DG) dan kapasitor di microgrid secara signifikan mengurangi rugi daya aktif dan daya reaktif. Penempatan DG yang optimal juga secara signifikan meningkatkan profil tegangan. Penempatan optimal DG intermittent pada sistem IEEE 69 Bus menggunakan metode GA dapat mengurangi rugi-rugi daya aktif sebesar 69,14 persen, sedangkan dengan menggunakan metode Particle Swarm Optimization (PSO) hanya dapat mengurangi rugi-rugi daya aktif sebesar 69,09 persen. Perencanaan optimal sumber energi terbarukan menggunakan metode algoritma MVO secara signifikan dapat meningkatkan profil tegangan, dan mengurangi rugi-rugi daya pada sistem. Optimasi penambahan tiga DG menggunakan MVO mampu mengurangi emisi sebesar 48,20 persen. Sehingga terjadi pengurangan biaya lingkungan sebesar 48,25 persen. Hasil penelitian menunjukkan bahwa Capital Expenditure (CAPEX) dan Operating Expenditure (OPEX) paling rendah pada DG sumber energi terbarukan adalah DG Photovoltaic (DG PV), sedangkan Levelized Cost of Electricity (LCOE) paling rendah adalah DG Wind Turbine (DG WT) dan termahal adalah Battery Energy Storage (BES). Harga rata-rata listrik pada sistem microgrid mengalami penurunan dari LCOE apabila menggunakan DG PV dan mengalami kenaikan apabila menggunakan DG WT dan BES.
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Utilization of renewable energy sources such as wind and solar in the future will become a necessity along with the depletion of fossil energy sources. Renewable energy sources have been widely used in distribution systems or small-scale electricity systems. Small-scale distribution systems that can operate independently are called microgrid systems. However, the intermittent and uncertain nature of this energy source is a challenge and complicates microgrid operations. Another problem that is more complex than microgrid systems with intermittent renewable energy sources is the balance between power demand and the availability of energy sources. Energy storage systems can be installed as backup energy to supply demand and support microgrid backup requirements. However, due to the intermittent nature of renewable energy sources, a mechanism is needed to select and utilize these energy sources optimally. Therefore, in this dissertation research, a planning model was developed to optimize the use of intermittent renewable energy sources. This model includes formulations for obtaining capacity and location and cost of renewable energy sources and energy storage systems. Optimal planning decisions are obtained by considering economic, environmental and power quality factors. The optimization method used is the Multi Verse Optimizer (MVO) and the Genetic Algorithm (GA) method as a comparison. The MVO method was developed from multi verse and the big bang theory to obtain optimal value for planning renewable energy sources. The results show that the optimal placement of Distributed Generation (DG) and capacitors in the microgrid significantly reduces active and reactive power losses. Optimal DG placement also significantly improves the voltage profile. The optimal intermittent DG placement on the IEEE 69 Bus system using the GA method can reduce active power losses by 69.14 percent, while using the Particle Swarm Optimization (PSO) method can only reduce active power losses by 69.09 percent.Optimum planning of renewable energy sources using the MVO algorithm method can significantly increase the voltage profile, and reduce power losses in the system. Optimizing the addition of three DG using MVO can reduce emissions by 48.20 percent. So that there is a reduction in environmental costs of 48.25 percent. The results showed that the lowest Capital Expenditure (CAPEX) and Operating Expenditure (OPEX) for renewable energy source DG was DG Photovoltaic (DG PV), while the lowest Levelized Cost of Electricity (LCOE) was DG Wind Turbine (DG WT) and the most expensive is Battery Energy Storage (BES). The average price of electricity in a microgrid system has decreased from LCOE when using DG PV and has increased when using DG WT and BES.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Uncontrolled Keywords: | Renewable energy intermittent, MVO, emisi, Intermittent renewable energy, multi-verse optimizer, emission, LCOE |
| Subjects: | T Technology > TJ Mechanical engineering and machinery > TJ808 Renewable energy sources. Energy harvesting. T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20001-(S3) PhD Thesis |
| Depositing User: | Firdaus Firdaus |
| Date Deposited: | 03 Feb 2023 18:21 |
| Last Modified: | 03 Feb 2023 18:21 |
| URI: | http://repository.its.ac.id/id/eprint/96210 |
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