Multi-Objective Optimal Power Flow Using Efficient Evolutionary Algorithm

2017 ◽  
Vol 18 (2) ◽  
Author(s):  
S. Surender Reddy ◽  
P.R Bijwe
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Stability Index ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Fuel Cost ◽  
Pareto Optimal Front ◽  
Efficient Approach ◽  
Optimal Power

Abstract A novel efficient multi-objective optimization (MOO) technique for solving the optimal power flow (OPF) problem has been proposed in this paper. In this efficient approach uses the concept of incremental power flow model based on sensitivities and some heuristics. The proposed approach is designed to overcome the main drawback of conventional MOO approach, i. e., the excess computational time. In the present paper, three objective functions i. e., generation cost, system losses and voltage stability index are considered. In the proposed efficient MOO approach, the first half of the specified number of Pareto optimal solutions are obtained by optimizing the fuel cost objective while considering other objective (i. e., system loss or voltage stability index) as constraint while the second half is obtained in a vice versa manner. After obtaining the total Pareto optimal solutions, they are sorted in the ascending order of fuel cost objective function value obtained for each solution leads to the Pareto optimal front. The proposed efficient approach is implemented using the differential evolution (DE) algorithm. The proposed efficient MOO approach can effectively handle the complex non-linearities, discrete variables, discontinuities and multiple objectives. The effectiveness of the proposed approach is tested on standard IEEE 30 bus test system. The simulation studies show that the Pareto optimal solutions obtained with proposed efficient MOO approach are diverse and well distributed over the entire Pareto optimal front. The simulation results indicate that the execution speed of proposed efficient MOO approach is approximately 10 times faster than the conventional evolutionary based MOO approaches.

scholarly journals Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

Processes ◽  
2018 ◽  
Vol 6 (12) ◽  
pp. 250 ◽  
Author(s):  
Yahui Li ◽  
Yang Li
Keyword(s):  
Decision Analysis ◽  
Evolutionary Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Decision Makers ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Optimal Power ◽  
Knee Point

To coordinate the economy, security and environment protection in the power system operation, a two-step many-objective optimal power flow (MaOPF) solution method is proposed. In step 1, it is the first time that knee point-driven evolutionary algorithm (KnEA) is introduced to address the MaOPF problem, and thereby the Pareto-optimal solutions can be obtained. In step 2, an integrated decision analysis technique is utilized to provide decision makers with decision supports by combining fuzzy c-means (FCM) clustering and grey relational projection (GRP) method together. In this way, the best compromise solutions (BCSs) that represent decision makers’ different, even conflicting, preferences can be automatically determined from the set of Pareto-optimal solutions. The primary contribution of the proposal is the innovative application of many-objective optimization together with decision analysis for addressing MaOPF problems. Through examining the two-step method via the IEEE 118-bus system and the real-world Hebei provincial power system, it is verified that our approach is suitable for addressing the MaOPF problem of power systems.

Improved Pseudo-Gradient Search Particle Swarm Optimization for Optimal Power Flow Problem

2016 ◽  
pp. 177-207 ◽  
Author(s):  
Jirawadee Polprasert ◽  
Weerakorn Ongsakul ◽  
Vo Ngoc Dieu
Keyword(s):  
Particle Swarm Optimization ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Voltage Stability ◽  
Stability Index ◽  
Gradient Search ◽  
Fuel Cost ◽  
Swarm Optimization ◽  
Optimal Power ◽  
Voltage Deviation

This paper proposes an improved pseudo-gradient search particle swarm optimization (IPG-PSO) for solving optimal power flow (OPF) with non-convex generator fuel cost functions. The objective of OPF problem is to minimize generator fuel cost considering valve point loading, voltage deviation and voltage stability index subject to power balance constraints and generator operating constraints, transformer tap setting constraints, shunt VAR compensator constraints, load bus voltage and line flow constraints. The proposed IPG-PSO method is an improved PSO by chaotic weight factor and guided by pseudo-gradient search for particle's movement in an appropriate direction. Test results on the IEEE 30-bus and 118-bus systems indicate that IPG-PSO method is superior to other methods in terms of lower generator fuel cost, smaller voltage deviation, and lower voltage stability index.

Bees Two-Hive Algorithm for Optimal Power Flow

2013 ◽  
Vol 313-314 ◽  
pp. 870-875
Author(s):  
Nopbhorn Leeprechanon ◽  
Prakornchai Phonrattanasak
Keyword(s):  
Energy Balance ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Test System ◽  
Bees Algorithm ◽  
Fuel Cost ◽  
Physical Constraints ◽  
Optimal Power ◽  
Transmission Limits ◽  
Simulation Results

This paper presents bees two-hive algorithm for solving the optimal power flow (OPF) problem with various constraints. The objective of the proposed technique is to improve the quality solution of the conventional bees algorithm that minimize the total fuel cost subject to operational and physical constraints i.e. energy balance, generation and transmission limits including security constraints. The proposed methodology is tested on the IEEE 30-bus test system. The results obtained using the proposed approach are compared to GA, PSO, BA and other conventional. The comparison of quality solution with other algorithms confirms performance of proposed technique. Simulation results demonstrate that bees two-hive algorithm provides better results than other heuristic techniques.

scholarly journals Pareto-optimal power flow control in heterogeneous battery energy storage systems

2022 ◽  
Vol 48 ◽  
pp. 103803
Author(s):  
Markus Mühlbauer ◽  
Fabian Rang ◽  
Herbert Palm ◽  
Oliver Bohlen ◽  
Michael A. Danzer
Keyword(s):  
Energy Storage ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Storage Systems ◽  
Pareto Optimal ◽  
Battery Energy Storage ◽  
Power Flow Control ◽  
Optimal Power ◽  
Battery Energy Storage Systems ◽  
Battery Energy

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 Optimal operation of thermal system based on optimum power flow

2021 ◽  
Vol 10 (3) ◽  
pp. 1204-1210
Author(s):  
Majli Nema Hawas ◽  
Read K. Ibrahim ◽  
Ahmed Jasim Sultan
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Optimal Operation ◽  
Load Flow ◽  
Thermal System ◽  
Fuel Cost ◽  
Initial Operation ◽  
Optimal Power ◽  
Optimal Load ◽  
Activity Cost

This paper has demonstrated that the Newton-Raphsin (NR) load flow technique can be stretched out to produce optimal load flow (OPF) arrangement that is achievable as for all significant disparity imperatives. These arrangements are frequently desired for arranging and activity. We were examined how the load ought to be shared among different plants, when line misfortunes are represented to limit the absolute activity cost with optimal power flow computation with thought about penalty factors, steady fuel cost, and coefficient factors. The IEEE three-machines and nine- Bus bars system was a tested system. The obtained results were compared by initial operation and equality distribution through the saving cost ($/year). The comparison of results showed saving more than 1.6 million $/year under MATLAB V.18a environment.

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