scholarly journals Optimasi Aliran Daya pada Sistem Kelistrikan Opsi Nuklir Berdasarkan Multi-Objective Function: Fuel Cost dan Flat Voltage Profile

2017 ◽  
Vol 18 (2) ◽  
pp. 75
Author(s):  
Rizki Firmansyah Setya Budi ◽  
Sarjiya Sarjiya ◽  
Sasongko Hadi Pramono
Keyword(s):  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Voltage Profile ◽  
Fuel Cost ◽  
Multi Objective ◽  
Optimal Power

Tujuan dari pengoperasian sistem tenaga listrik adalah untuk memasok daya dengan kualitas baik dan biaya pembangkitan seminimal mungkin. Kualitas yang baik membutuhkan biaya yang lebih besar, sehingga untuk mencapai tujuan tersebut diperlukan optimasi dengan fungsi obyektif yang bertujuan untuk memaksimalkan kualitas sekaligus meminimalkan biaya. Penelitian ini bertujuanuntuk mendapatkan kondisi aliran daya optimal atau optimal power flow (OPF) dari segi biaya pembangkitan maupun kualitas tenaga listrik di suatu sistem kelistrikan dengan opsi nuklir pada waktu beban puncak dengan menggabungkan fungsi obyektif fuel cost dan flat voltage profile. Fungsi obyektif fuel cost bertujuan untuk meminimalkan biaya pembangkitan sedangkan fungsi obyektif flat voltage profile bertujuan untuk memaksimalkan kualitas dengan meminimalkan perbedaan/variasi tegangan dalam sebuah sistem. Penelitian dilakukan melalui studi literatur, penentuan fungsi obyektif optimasi, penggabungan fungsi objektif, simulasi menggunakan contoh kasus dan analisis sensitivitas. Contoh kasus menggunakan sistem IEEE 9 Bus yang telah ditambahkan fungsi bahan bakar PLTN, PLTU, dan PLTG. Simulasi menggunakan program bantu ETAP 12.6.0. Analisis sensitivitas dilakukan dengan menggunakan nilai pembobotan dari 0-100% untuk tiap fungsi obyektif. Hasil simulasi menunjukkan bahwa OPF dicapai pada faktor pembebanan 60% untuk fuel cost dan 40% untuk flat voltage profile. Biaya pembangkitan padakondisi optimal tersebut sebesar 7266 US$/jam dengan selisih tegangan maksimum minimumnya sebesar 2,85%. Pada sistem ini PLTU membangkitkan daya sebesar 133,2 MW + 22,1 MVar dan PLTG sebesar 80,7 MW + 13,8 MVar. Sedangkan PLTN membangkitkan daya sebesar 89,9 MW + 12,9 Mvar dan akan ekonomis jika membangkitkan daya kurang dari 90 MW.

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

scholarly journals Optimal Power Flow Menggunakan Metode Interior Point yang Disempurnakan

2018 ◽  
Vol 2 (2) ◽  
pp. 47-50
Author(s):  
Aris Budiman
Keyword(s):  
Objective Function ◽  
Interior Point ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Economic Dispatch ◽  
Multi Objective ◽  
Optimal Power

Permasalahan  OPF  terdiri dari  banyaknya objective function,  an/ara  lain economic dispatch, perencanaan  VAR,   dan juga  minimisasi rugi-rugi.  Untuk perencanaan  VAR perlu  mempertimbangkan biaya operasional rugi-rugi, sehingga membentuk sua/u permasalahan perencanaan dan operasiyang simultan.Di  tulisan  ini  secara  khusus  hanya  membahas  metode  OPF  ImprovedQuadratic Interior Point (IQIP). Metode  !QIP ini memiliki unjuk kerja yang secara umum lebih  baik  dibanding me/ode   generasi   sebelumnya,   yaitu  EQJP (Extended Quadratic Interior Point),  antara lain karena bisa menggunakan titik awal general dan pada  beberapa  pengujian  lebih cepat konvergen dibandingkan EQJP. Hal ini menyebabkan me/ode IQIP mampu menawarkan perbaikan besar di dalam kecepatan,keakuratan,  dan konvergensi  di dalam pemecahan masalah optimisasi yang  multi• objective function  dan multi-constraint.   Kemampuan memecahkan optimisasi global dari sistem terinterkoneksi  dan sis/em  terpartisi  untuk optimisasi /oka/juga lebih baik dari generasi  sebelumnya.Metode ini telah melalui pengujian terhadap sis/em 14 bus, 30 bus, dan 118 busIEEE. Efektivilas dari metode  ini telah dieva/uasi dengan cara dibandingkan denganprogram OPF yang berbasis EQIP (Extended Quadratic Interior Point) dan programMINOSyang cukup dikenal di dunia perencanaan sis/em

scholarly journals Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

2021 ◽  
Vol 13 (16) ◽  
pp. 8703
Author(s):  
Andrés Alfonso Rosales-Muñoz ◽  
Luis Fernando Grisales-Noreña ◽  
Jhon Montano ◽  
Oscar Danilo Montoya ◽  
Alberto-Jesus Perea-Moreno
Keyword(s):  
Objective Function ◽  
Successive Approximation ◽  
Direct Current ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Problem ◽  
Distributed Generators ◽  
Optimal Power ◽  
Power Flow Problem

This paper addresses the optimal power flow problem in direct current (DC) networks employing a master–slave solution methodology that combines an optimization algorithm based on the multiverse theory (master stage) and the numerical method of successive approximation (slave stage). The master stage proposes power levels to be injected by each distributed generator in the DC network, and the slave stage evaluates the impact of each power configuration (proposed by the master stage) on the objective function and the set of constraints that compose the problem. In this study, the objective function is the reduction of electrical power losses associated with energy transmission. In addition, the constraints are the global power balance, nodal voltage limits, current limits, and a maximum level of penetration of distributed generators. In order to validate the robustness and repeatability of the solution, this study used four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: ant lion optimization, particle swarm optimization, continuous genetic algorithm, and black hole optimization algorithm. All of them employed the method based on successive approximation to solve the load flow problem (slave stage). The 21- and 69-node test systems were used for this purpose, enabling the distributed generators to inject 20%, 40%, and 60% of the power provided by the slack node in a scenario without distributed generation. The results revealed that the multiverse optimizer offers the best solution quality and repeatability in networks of different sizes with several penetration levels of distributed power generation.

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