Three powerful nature-inspired algorithms to optimize power flow in Algeria's Adrar power system

This paper is intended to solve the optimal power flow (OPF) dispatch in the presence of wind power generation (WPG) in the Adrar power system. Towards this aim, the performances of three powerful meta-heuristic algorithms-namely, the cuckoo search algorithm (CSA), firefly algorithm (FFA), and flower pollination algorithm (FPA) are investigated. The proposed algorithms are applied to best capture the active power produced with the minimum value of a multi-objective function. This latter includes: the fuel cost, the NOx emissions, and the imbalance cost of the WPGs. Furthermore, considering the uncertainties governing wind resources, the maximum wind power output is estimated using the wind speed carrying maximum energy. It was found that all algorithms perform well in providing accurate solutions. Interestingly, the convergence is reached in the first 135 iterations. A remarkable outcome of the present work is that CSA outperforms FPA and FFA. CSA has proved itself to be a great tool to optimize Adrar's power flow system in term of iterations and computational time.

[1]  R. Adapa,et al.  A review of selected optimal power flow literature to 1993. I. Nonlinear and quadratic programming approaches , 1999 .

[2]  N. Waser.  Flower Constancy: Definition, Cause, and Measurement , 1986, The American Naturalist.

[3]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[4]  A. Chowdhury,et al.  Cuckoo search algorithm for economic dispatch , 2013 .

[5]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[6]  David C. Yu,et al.  An Economic Dispatch Model Incorporating Wind Power , 2008, IEEE Transactions on Energy Conversion.

[7]  Machteld van den Broek,et al.  Impacts of large-scale Intermittent Renewable Energy Sources on electricity systems, and how these can be modeled , 2014 .

[8]  Kenneth Sörensen,et al.  Metaheuristics - the metaphor exposed , 2015, Int. Trans. Oper. Res..

[9]  Jia-Ching Wang,et al.  An enhanced firefly algorithm to multi-objective optimal active/reactive power dispatch with uncertainties consideration , 2015 .

[10]  M. Tripathy,et al.  Optimal power flow solution of wind integrated power system using modified bacteria foraging algorithm , 2014 .

[11]  Feng Donghan,et al.  A review on reliability assessment for wind power , 2009 .

[12]  Mimoun Younes,et al.  Multi-objective economic emission dispatch solution using hybrid FFA (firefly algorithm) and considering wind power penetration , 2014 .

[13]  D. P. Kothari,et al.  Combined Economic and Emission Dispatch Using Improved Backpropagation Neural Network , 2000 .

[14]  Thang Trung Nguyen,et al.  Cuckoo search algorithm for short-term hydrothermal scheduling , 2014 .

[15]  Andries Petrus Engelbrecht,et al.  A study of particle swarm optimization particle trajectories , 2006, Inf. Sci..

[16]  R. Adapa,et al.  A review of selected optimal power flow literature to 1993. II. Newton, linear programming and interior point methods , 1999 .

[17]  Bikash C. Pal,et al.  Intermittent wind generation in optimal power flow dispatching , 2009 .

[18]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[19]  Bo Xing,et al.  Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms , 2013 .

[20]  Anh Viet Truong,et al.  Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm , 2015 .

[21]  Xin-She Yang,et al.  Multi-Objective Flower Algorithm for Optimization , 2014, ICCS.

[22]  J. A. Carta,et al.  Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions , 2007 .

[23]  Thang Trung Nguyen,et al.  Modified cuckoo search algorithm for short-term hydrothermal scheduling , 2015 .

[24]  M. Kalantar,et al.  Economic Dispatch incorporating wind power plant using Particle Swarm Optimization , 2012, 2012 Second Iranian Conference on Renewable Energy and Distributed Generation.

[25]  Gwo-Ching Liao,et al.  A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reducti , 2011 .

[26]  A. Celik.  A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey , 2004 .

[27]  E. S. Ali,et al.  Combined economic and emission dispatch solution using Flower Pollination Algorithm , 2016 .

[28]  Beverley J. Glover,et al.  Understanding flowers and flowering : an integrated approach , 2007 .

[29]  Chen Wang,et al.  Optimal Power Flow Solution Incorporating Wind Power , 2012, IEEE Systems Journal.

[30]  Narayana Prasad Padhy,et al.  Comparison and application of evolutionary programming techniques to combined economic emission dispatch with line flow constraints , 2003 .

[31]  Jizhong Zhu,et al.  Optimization of Power System Operation , 2009 .

[32]  S. Roy.  Market Constrained Optimal Planning for Wind Energy Conversion Systems over Multiple Installation Sites , 2002, IEEE Power Engineering Review.