Optimal Power Flow Using Grass Hopper Optimization Algorithm for Generator Fuel Cost and Voltage Profile

2019 ◽  
Vol 7 (3) ◽  
pp. 238-241
Author(s):  
A.I. Modi ◽  
T.V. Rabari
Keyword(s):  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Voltage Profile ◽  
Fuel Cost ◽  
Optimal Power

scholarly journals Comparative Analysis of a Performance of Metaheuristic Algorithms in Solving Optimal Power Flow Problems with UPFC Device in the Transmission System

2021 ◽  
Vol 9 (5) ◽  
pp. 316-326
Author(s):  
K. Padma ◽  
Yeshitela Shiferaw Maru
Keyword(s):  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Transmission System ◽  
Voltage Profile ◽  
Proper Position ◽  
Optimal Position ◽  
Optimal Power

Incremental industrialization and urbanization is the cause of enhanced energy use as it increases the building of new lines and more inductive loads. As a result, the transmission system losses increased, and the magnitudes of voltage profile values deviated from the stated value, resulting in increased cost of active power generation. To mitigate these issues, adequate reactive power compensation in the transmission line and bus systems should be done. Reactive power is regulated by the proper position of the Flexible AC Transmission System (FACTS). Unified Power Flow Controller (UPFC) is a voltage converter system that increases the voltage profile and reduces loss. In this paper, the optimal power flow solution is considered using a FACTS device based on Multi Population Modified Jaya (MPMJ) optimization algorithm. Using the Analytical Hierarchy Process (AHP) system, the optimal position of the UPFC device is determined by considering the most useful objective function provided by priorities and weighting factors. Therefore, on the standard IEEE-57 bus test system, the proposed MPMJ optimization algorithm is implemented with UPFC for optimal fuel cost values of generation, real power loss, voltage deviation and sum of squared voltage stability index. The result obtained by the proposed algorithm is contrasted with the recent literature algorithm

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.

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.

Optimal Power Flow and Optimal Reactive Power Dispatch Using Different Evolutionary Optimization Techniques

2018 ◽  
pp. 117-157
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Cost Minimization ◽  
Voltage Profile ◽  
Fuel Cost ◽  
Loss Minimization ◽  
Optimal Reactive Power Dispatch ◽  
Reactive Power Dispatch ◽  
Fuel Cost Minimization

This chapter describes grey wolf optimization (GWO), teaching-learning-based optimization (TLBO), biogeography-based optimization (BBO), krill herd algorithm (KHA), chemical reaction optimization (CRO), and hybrid CRO (HCRO) algorithms to solve both single and multi-objective optimal power flow (MOOPF) and optimal reactive power dispatch (ORPD) problems while satisfying various operational constraints. The proposed HCRO approach along with GWO, TLBO, BBO, KHA, and CRO algorithms are implemented on IEEE 30-bus system to solve four different single objectives: fuel cost minimization, system power loss minimization, voltage stability index minimization, and voltage deviation minimization; two bi-objectives optimization, namely minimization of fuel cost and transmission loss; minimization of fuel cost and voltage profile; and one tri-objective optimization, namely minimization of fuel cost, minimization of transmission losses, and improvement of voltage profile simultaneously. The simulation results clearly suggest that the proposed is able to provide a better solution than other approaches.

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