A Novel Quasi Opposition Based Passing Vehicle Search Algorithm Approach for Large Scale Unit commitment problem

A Novel Quasi Opposition Based Passing Vehicle Search Algorithm Approach for Large Scale Unit commitment problem

Pradeep Jangir

Contact

Arvind Kumar

Contact

A Novel Quasi Opposition Based Passing Vehicle Search Algorithm Approach for Large Scale Unit commitment problem

Article Fingerprint

ReserarchID

W9MP3

A Novel Quasi Opposition Based Passing Vehicle Search Algorithm Approach for Large Scale Unit commitment problem Banner

AI TAKEAWAY

Connecting with the Eternal Ground

Abstract

This paper presents a novel approach population based metaheuristics algorithm known as Quasi Oppositional Passing Vehicle Search (QOPVS) algorithm for solve the Unit commitment problem (UCP) of thermal units in an electrical power system. Passing vehicle search (PVS) algorithm is a population based algorithm which mechanism is inspired by passing vehicles on two-lane rural highways. As algorithms are population based so enables to provide improved solution with integration of powerful techniques. In this article, such a powerful technique named Opposite based learning techniques (OBLT) is integrated with proposed PVS algorithm. OBLT provides enough strength to proposed PVS algorithm to gain a better approximation for both current and opposite population at the same time, as it provide a solution which is more nearer solution from optimal based from starting by checking both solutions.

References

89 Cites in Article

Reference Format

G Cornuéjols (2008). Valid inequalities for mixed integer linear programs.
M Avriel (2003). Nonlinear programming: analysis and methods.
Ailsa Land,Alison Doig (1958). An Automatic Method for Solving Discrete Programming Problems.
A Simpson,G Dandy,L Murphy (1994). Genetic algorithms compared to other techniques for pipe optimization.
J Walter,Gary Hobbs,Stephen Hermon,Gerald Warner,Sheble (1988). An Enhanced Dynamic Programming Approach for Unit Commitment.
Sasan Mokhtari,J Sing,Bruce Wollenberg (1988). A unit commitment expert system (power system control).
Chung-Ching Su,Yuan-Yih Hsu (1991). Fuzzy Dynamic Programming: An Application to Unit Commitment.
Chem-Lin Chen,Shun-Chung Wang (1993). Branchand-Bound Scheduling for Thermal Generating Units.
B Gerald,George Sheble,Fahd (1994). Unit Commitment Literature Synopsis.
A El-Keib,H Ma,J Hart (1994). Economic dispatch in view of the Clean Air Act of 1990.
A Ma,R E1-Keib,H Smith,Ma (1995). A genetic algorithm based approach to thermal unit commitment of electric power systems.
Hong-Tzer Yang,Pai-Chum Yang,Ching-Lien Huang (1996). Evolutionary Programming Based Economic Dispatch Units with Non-Smooth Fuel Cost Functions.
S O Orero,M R Irving (1997). Large scale unit commitment using a hybrid genetic algorithm.
Seyedrasoul Saneifard,Nadipuram Prasad,Howard Smolleck (1997). A Fuzzy Logic Approach to Unit Commitment.
A Mantawy,Y Abdel-Magid,S Selim (1998). A simulated annealing algorithm for unit commitment.
A Rudolf,R Bayrleithner (1999). A Genetic Algorithm for Solving the Unit Commitment Problem of a Hydro-Thermal Power System.
Haili Ma,S Shahidehpour (1999). Unit commitment with transmission security and voltage constraints.
K Juste,H Kitu,E Tunaka,J Hasegawa (1999). An Evolutionary Programming Solution to the Unit Commitment Problem.
K Swarup,S Yamashiro (2002). Unit Commitment Solution Methodology Using Genetic Algorithm.
Narayana Prasad,Padhy (2004). Unit Commitment-A Bibliographical Survey.
G Ioannis,Anastasios Damousis,Petros Bakirtzis,Dokopoulos (2004). A Solution to the Unit-Commitment Problem Using Integer-Coded Genetic Algorithm.
Miguel Carrión,M José,Arroyo (2006). A Computationally Efficient Mixed-Integer Linear Formulation for the Thermal Unit Commitment Problem.
Bart Ummels,Madeleine Gibescu,Engbert Pelgrum,Wil Kling,Arno Brand (2007). Impacts of Wind Power on Thermal Generation Unit Commitment and Dispatch.
Lei Wu,Mohammad Shahidehpour,Tao Li (2007). Stochastic Security-Constrained Unit Commitment.
A Miguel,Daniel Ortega-Vazquez,Kirschen (2009). Estimating the Spinning Reserve Requirements in Systems with Significant Wind Power Generation Penetration.
A Hawkes,M (2009). Leach Modelling high level system design and unit commitment for a microgrid.
Yun-Won Jeong,Jong-Bae Park,Se-Hwan Jang,Kwang Lee (2010). A New Quantum-Inspired Binary PSO: Application to Unit Commitment Problems for Power Systems.
Yuri Makarov,Pavel Etingov,Jian Ma,Zhenyu Huang,Krishnappa Subbarao (2011). Incorporating Uncertainty of Wind Power Generation Forecast Into Power System Operation, Dispatch, and Unit Commitment Procedures.
Dimitris Bertsimas,Eugene Litvinov,Andy Xu,Jinye Sun,Tongxin Zhao,Zheng (2013). Adaptive Robust Optimization for the Security Constrained Unit © 2017 Global Journals Inc. (US) Commitment Problem.
T Senjyu,H Yamashiro,K Uezato,T Funabashi (2002). A unit commitment problem by using genetic algorithm based on unit characteristic classification.
D Walters,G Sheble (1993). Genetic algorithm solution of economic dispatch with valve point loading.
Zwe-Lee Gaing (2003). Particle swarm optimization to solving the economic dispatch considering the generator constraints.
Ahmed Saber,Ganesh Venayagamoorthy (2008). Economic load dispatch using bacterial foraging technique with particle swarm optimization biased evolution.
N Iba,H Nomana (2008). Differential evolution for economic load dispatch problems.
John Vlachogiannis,Kwang Lee (2009). Economic Load Dispatch-A Comparative Study on Heuristic Optimization Techniques with an Improved Coordinated Aggregation-Based PSO.
R Eberhart,J Kennedy (1995). A new optimizer using particle swarm theory.
R Storn,K Price (1997). Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces.
Lawrence Fogel,Alvin Owens,Michael Walsh (1966). Intelligent decision making through a simulation of evolution.
X Yao,Y Liu,G Lin (1999). Evolutionary programming made faster.
J Holland (1992). Genetic algorithms.
John Holland,Judith Reitman (1977). Cognitive systems based on adaptive algorithms.
A Colorni,M Dorigo,V Maniezzo (1991). Figure 4: Initial conditions between ant colony in a nest and food source (Dorigo, Maniezzo & Colorni, 1996)..
D Wolpert,W Macready (1997). No free lunch theorems for optimization.
Gai-Ge Wang,Suash Deb,Zhihua Cui (2015). Monarch butterfly optimization.
X-S Yang,Deb (2009). Cuckoo search via Lévy flights.
X Yang,Deb (2010). Engineering optimisation by cuckoo search.
B Basturk,D Karaboga (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm.
Mirjalili Seyedali,Seyed Mirjalili,Lewis Mohammad,Andrew (2014). Grey wolf optimizer.
X-S Yang (2010). Firefly algorithm, Levy flights and global optimization.
X-S Yang (2010). Firefly algorithm, stochastic test functions and design optimisation.
Ramin Rajabioun (2011). Cuckoo Optimization Algorithm.
A Kaveh,M Khayatazad (2012). A new meta-heuristic method: Ray Optimization.
A Kaveh,M Khayatazad (2013). Ray optimization for size and shape optimization of truss structures.
A Kaveh,M Ghazaan (2014). Enhanced Colliding Bodies Algorithm for Truss Optimization with Frequency Constraints.
A Kaveh,V Mahdavi (2014). Colliding bodies optimization method for optimum discrete design of truss structures.
E Rashedi,H Nezamabadi-Pour,S Saryazdi (2009). GSA: a gravitational search algorithm.
A Kaveh,N Farhoudi (2013). A new optimization method: Dolphin echolocation.
A Kaveh (2014). Dolphin echolocation optimization.
A Kaveh,S Talatahari (2010). A novel heuristic optimization method: charged system search.
A Kaveh,S Talatahari (2012). Charged system search for optimal design of frame structures.
I Trivedi,J Pradeep,J Narottam,K Arvind,L Dilip (2016). Novel Adaptive Whale Optimization Algorithm for Global Optimization.
Indrajit Trivedi,Pradeep Jangir,Siddharth Parmar (2016). Optimal power flow with enhancement of voltage stability and reduction of power loss using ant-lion optimizer.
I Trivedi,P Jangir,S Parmar (2016). Optimal power flow with voltage stability improvement and loss reduction in power system using Moth-Flame Optimizer.
Pradeep Jangir (2016). A novel hybrid Particle Swarm Optimizer with multi verse optimizer for global numerical optimization and Optimal Reactive Power Dispatch problem Engineering Science and Technology.
Rajnikant Bhesdadiya,Indrajit Trivedi,Pradeep Jangir,Narottam Jangir,Arvind Kumar (2016). An NSGA-III algorithm for solving multi-objective economic/environmental dispatch problem.
R Bhesdadiya,Mahesh Pandya,Indrajit Trivedi,Narottam Jangir,Pradeep Jangir,Arvind Kumar (2016). Price penalty factors based approach for combined economic emission dispatch problem solution using Dragonfly Algorithm.
I Trivedi,P Jangir,M Bhoye (2016). An economic load dispatch and multiple environmental dispatch problem solution with microgrids using interior search algorithm.
G-G Wang,Deb Gandomi,A Alavi,A (2016). Opposition based krill herd algorithm with Cauchy mutation and position clamping.
Gai-Ge Wang,Amir Gandomi,Amir Alavi,Suash Deb (2016). A hybrid method based on krill herd and quantum-behaved particle swarm optimization.
L Guo,G-G Wang,A Gandomi,A Alavi,H Duan (2014). A new improved krill herd algorithm for global numerical optimization.
Gai-Ge Wang,Amir Gandomi,Amir Alavi (2014). Stud krill herd algorithm.
Vikram Kumar,Kamboj A novel hybrid PSO-GWO approach for unit commitment problem.
Vikram Kumar Kamboj,S Bath,J Dhillon Multiobjective multiarea unit commitment using hybrid differential evolution algorithm considering import/export and tie-line constraints.
K Kamboj,S Bath,J Dhillon (2015). A Novel Hybrid DE-Random Search approach for Unit Commitment Problem.
Vikram Kumar Kamboj,S Bath,J Dhillon (2016). Implementation of hybrid harmony search/random search algorithm for single area unit commitment problem.
Ashutosh Vikram Kumar Kamboj,S Bhadoria,Bath (2016). Solution of Non-Convex Economic Load Dispatch Problem for Small Scale Power Systems Using Ant Lion Optimizer.
Poonam Savsani,Vimal Savsani (2016). Passing vehicle search (PVS): A novel metaheuristic algorithm.
Abishai Polus,Moshe Livneh,Benyamin Frischer (1701). Evaluation of the Passing Process on Two-Lane Rural Highways.
Riccardo Rossi,Massimiliano Gastaldi,Federico Pascucci (2014). Flow Rate Effects on Vehicle Speed at Two Way-Two Lane Rural Roads.
D Gerlough,M Huber (1976). Special Report 165: Traffic Flow Theory.
R Luttinen (1996). Statistical analysis of vehicle time headways.
R Luttinen (2001). Traffic flow on two-lane highways -an overview.
P Dey,S Chandra,S Gangopadhaya (2006). Speed distribution curves under mixed traffic conditions.
Yajie Zou,Yunlong Zhang (2011). Use of Skew-Normal and Skew-t Distributions for Mixture Modeling of Freeway Speed Data.
Y Zou,Y Zhang,X Zhu (2012). Constructing a bivariate distribution for freeway speed and headway data.
S Brisset,P Brochet (2005). Analytical model for the optimal design of a brushless DC wheel motor.
S Rahnamayan,H Tizhoosh,Mma Salama (2007). Quasi oppositional differentialevolution.
S Kazarlis,A,V Petridis (1996). A Genetic Algorithm Solution to The Unit Commitment Problem.
Paul Warnshuis (1967). Simulation of two-way traffic on an isolated two-lane road.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Pradeep Jangir. 2017. \u201cA Novel Quasi Opposition Based Passing Vehicle Search Algorithm Approach for Large Scale Unit commitment problem\u201d. Global Journal of Research in Engineering - F: Electrical & Electronic GJRE-F Volume 17 (GJRE Volume 17 Issue F4): .

More Citation Formats

Select Citation Style:

Download Citation

Download Article

GJRE Volume 17 Issue F4
Pg. 49- 84

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

Keywords

Not Found

Classification

GJRE-F Classification: FOR Code: 290903

Submission ReceivedDecember 12, 2016
Peer Review Double Blind
Handling Editor
Accepted January 3, 2017
Published January 15, 2017

Version of record

v1.2

Issue date

September 11, 2017

Language

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Score: 102

Country: India

Subject: Global Journal of Research in Engineering - F: Electrical & Electronic

Authors: Pradeep Jangir, Arvind Kumar (PhD/Dr. count: 0)

View Count (all-time): 234

Total Views (Real + Logic): 3512

Total Downloads (simulated): 1714

Publish Date: 2017 09, Mon

Monthly Totals (Real + Logic):

Month 1: 54 views
Month 2: 39 views
Month 3: 46 views
Month 4: 52 views
Month 5: 29 views
Month 6: 42 views
Month 7: 41 views
Month 8: 48 views
Month 9: 29 views
Month 10: 42 views
Month 11: 33 views
Month 12: 28 views
Month 13: 33 views
Month 14: 34 views
Month 15: 34 views
Month 16: 20 views
Month 17: 15 views
Month 18: 43 views
Month 19: 34 views
Month 20: 29 views
Month 21: 17 views
Month 22: 36 views
Month 23: 26 views
Month 24: 20 views
Month 25: 21 views
Month 26: 43 views
Month 27: 14 views
Month 28: 35 views
Month 29: 43 views
Month 30: 27 views
Month 31: 17 views
Month 32: 16 views
Month 33: 29 views
Month 34: 25 views
Month 35: 26 views
Month 36: 24 views
Month 37: 39 views
Month 38: 28 views
Month 39: 40 views
Month 40: 40 views
Month 41: 19 views
Month 42: 23 views
Month 43: 37 views
Month 44: 23 views
Month 45: 30 views
Month 46: 20 views
Month 47: 35 views
Month 48: 47 views
Month 49: 26 views
Month 50: 31 views
Month 51: 31 views
Month 52: 33 views
Month 53: 24 views
Month 54: 30 views
Month 55: 15 views
Month 56: 26 views
Month 57: 33 views
Month 58: 43 views
Month 59: 23 views
Month 60: 34 views
Month 61: 29 views
Month 62: 47 views
Month 63: 47 views
Month 64: 35 views
Month 65: 14 views
Month 66: 31 views
Month 67: 23 views
Month 68: 25 views
Month 69: 35 views
Month 70: 46 views
Month 71: 24 views
Month 72: 38 views
Month 73: 37 views
Month 74: 49 views
Month 75: 26 views
Month 76: 14 views
Month 77: 34 views
Month 78: 28 views
Month 79: 25 views
Month 80: 35 views
Month 81: 24 views
Month 82: 42 views
Month 83: 36 views
Month 84: 39 views
Month 85: 42 views
Month 86: 29 views
Month 87: 41 views
Month 88: 28 views
Month 89: 45 views
Month 90: 25 views
Month 91: 42 views
Month 92: 31 views
Month 93: 33 views
Month 94: 15 views
Month 95: 43 views
Month 96: 19 views
Month 97: 41 views
Month 98: 26 views
Month 99: 26 views
Month 100: 35 views
Month 101: 39 views
Month 102: 33 views
Month 103: 49 views
Month 104: 18 views

Total Views: 3512

Total Downloads: 1714

2026 Trends

Published Article

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]