scholarly journals A Robust Optimization Approach for Optimal Power Flow Solutions Using Rao Algorithms

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5449
Author(s):  
Saket Gupta ◽  
Narendra Kumar ◽  
Laxmi Srivastava ◽  
Hasmat Malik ◽  
Amjad Anvari-Moghaddam ◽  
...  
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Cost Minimization ◽  
Optimization Algorithms ◽  
Parameter Tuning ◽  
Electrical Network ◽  
Optimization Approach ◽  
Voltage Profile ◽  
Optimal Power ◽  
Voltage Stability Enhancement

This paper offers three easy-to-use metaphor-less optimization algorithms proposed by Rao to solve the optimal power flow (OPF) problem. Rao algorithms are parameter-less optimization algorithms. As a result, algorithm-specific parameter tuning is not required at all. This quality makes these algorithms simple to use and able to solve various kinds of complex constrained optimization and engineering problems. In this paper, the main aim to solve the OPF problem is to find the optimal values of the control variables in a given electrical network for fuel cost minimization, real power losses minimization, emission cost minimization, voltage profile improvement, and voltage stability enhancement, while all the operating constraints are satisfied. To demonstrate the efficacy of Rao algorithms, these algorithms have been employed in three standard IEEE test systems (30-bus, 57-bus, and 118-bus) to solve the OPF problem. The OPF results of Rao algorithms and the results provided by other swarm intelligence (SI)/evolutionary computing (EC)-based algorithms published in recent literature have been compared. Based on the outcomes, Rao algorithms are found to be robust and superior to their competitors.

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.

scholarly journals Multi-objective optimal power flow in the presence of intermittent renewable energy sources

2018 ◽  
Vol 7 (4) ◽  
pp. 2766 ◽  
Author(s):  
S. Surender Reddy
Keyword(s):  
Wind Power ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Cost Minimization ◽  
Renewable Energy Sources ◽  
Thermal Power ◽  
Wind Farms ◽  
Multi Objective ◽  
Optimal Power ◽  
Generation Cost

This paper solves a multi-objective optimal power flow (MO-OPF) problem in a wind-thermal power system. Here, the power output from the wind energy generator (WEG) is considered as the schedulable, therefore the wind power penetration limits can be determined by the system operator. The stochastic behavior of wind power and wind speed is modeled using the Weibull probability density function. In this paper, three objective functions i.e., total generation cost, transmission losses and voltage stability enhancement index are selected. The total generation cost minimization function includes the cost of power produced by the thermal and WEGs, costs due to over-estimation and the under-estimation of available wind power. Here, the MO-OPF problems are solved using the multi-objective glowworm swarm optimiza-tion (MO-GSO) algorithm. The proposed optimization problem is solved on a modified IEEE 30 bus system with two wind farms located at two different buses in the system.  

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.

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