Static transmission expansion planning for realistic networks in Egypt

Abstract Transmission network planning (TEP) is very important task in electric power systems. It begins with the establishment of power demand growth scenarios, in accordance with forecasts along the time and to obtain the optimal expansion plan, while fulfilling power systems constraints. This paper proposes a two-stage TEP procedure for two realistic transmission Egyptian networks, Western Delta Network (WDN) and 500 kV of Extra High Voltage Network (EHVN). In the first stage, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is employed to obtain the predicted long-term load forecasting (LTLF) up to 2030. In the second one, the Integer Based Particle Swarm Optimization (IBPSO) technique is developed for solving the static TEP problem. The proposed TEP aims at finding the optimal transmission routes, at least capital investment costs, to meet the forecasted load. The TEP problem is formulated as non-linear, large scale, mixed-integer and non-convex optimization problem. The static TEP problem is employed using DC power flow model. The proposed TEP methodology is tested on standard Garver 6-bus test systems. The load forecasting methodology is dependent on the historical (Actual) peak load data for the UEN from 1993 to 2015. The location and capacity of new site of generation station are selected to meet the demand required. Also, the AC load flow method is emerged with the TEP solution employed by IBPSO for Garver and EHVN to assess the voltage, reactive power and security constraints. Numerical results show the capability of the proposed procedure to solve TEP problem at acceptable economical and technical benefits.

[1]  R.A. El-Sehiemy,et al.  Optimized generation costs using a modified particle swarm optimization version , 2008, 2008 12th International Middle-East Power System Conference.

[2]  Haozhong Cheng,et al.  New discrete method for particle swarm optimization and its application in transmission network expansion planning , 2007 .

[3]  Saman Aminbakhsh,et al.  Discrete particle swarm optimization method for the large-scale discrete time-cost trade-off problem , 2016, Expert Syst. Appl..

[4]  A. Monticelli,et al.  Test systems and mathematical models for transmission network expansion planning , 2002 .

[5]  P. Luh,et al.  Forecasting power market clearing price and its discrete PDF using a Bayesian-based classification method , 2001, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[6]  A. A. Abou El-Ela,et al.  An optimization planning technique for Suez Canal Network in Egypt , 2010 .

[7]  Peter Amaize,et al.  Short-Term Load Forecasting Using The Time Series AndArtificial Neural Network Methods , 2016 .

[8]  A.A. Abou Ela,et al.  Different transmission loss allocation schemes in deregulated power markets , 2007, 2007 42nd International Universities Power Engineering Conference.

[9]  A.A. Abido,et al.  Particle swarm optimization for multimachine power system stabilizer design , 2001, 2001 Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.01CH37262).

[10]  N. D. Hatziargyriou,et al.  Static transmission expansion planning under an improved harmony search algorithm , 2012 .

[11]  Sen Guo,et al.  A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm , 2013, Knowl. Based Syst..

[12]  Romeli Barbosa,et al.  Optimal Sizing of a Photovoltaic-Hydrogen Power System for HALE Aircraft by means of Particle Swarm Optimization , 2015 .

[13]  Humberto Verdejo,et al.  Stochastic modeling to represent wind power generation and demand in electric power system based on real data , 2016 .

[14]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[15]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[16]  M. A. Abido.  Optimal des'ign of Power System Stabilizers Using Particle Swarm Opt'imization , 2002, IEEE Power Engineering Review.

[17]  Grzegorz Dudek.  Pattern-based local linear regression models for short-term load forecasting , 2016 .

[18]  Abbas Khosravi,et al.  Uncertainty handling using neural network-based prediction intervals for electrical load forecasting , 2014 .

[19]  Ronnie Belmans,et al.  Usefulness of DC power flow for active power flow analysis , 2005 .

[20]  Ioannis P. Panapakidis,et al.  Day-ahead electricity price forecasting via the application of artificial neural network based models , 2016 .

[21]  L. L. Garver,et al.  Transmission Network Planning Using Linear Programming , 1985, IEEE Power Engineering Review.

[22]  Tao Hong,et al.  Long Term Probabilistic Load Forecasting and Normalization With Hourly Information , 2014, IEEE Transactions on Smart Grid.

[23]  M. A. Abido,et al.  Optimal power flow using particle swarm optimization , 2002 .

[24]  Alper Ekrem Murat,et al.  A discrete particle swarm optimization method for feature selection in binary classification problems , 2010, Eur. J. Oper. Res..

[25]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[26]  Hesham K. Alfares,et al.  Electric load forecasting: Literature survey and classification of methods , 2002, Int. J. Syst. Sci..

[27]  Yoshikazu Fukuyama,et al.  A particle swarm optimization for reactive power and voltage control considering voltage security assessment , 2000 .

[28]  David Zimbra,et al.  Medium term system load forecasting with a dynamic artificial neural network model , 2006 .

[29]  Hossein Seifi,et al.  Electric Power System Planning: Issues, Algorithms and Solutions , 2011 .

[30]  Morteza Saberi,et al.  An Integrated Intelligent Neuro-Fuzzy Algorithm for Long-Term Electricity Consumption: Cases of Selected EU Countries , 2010 .

[31]  Mohsen Kalantar,et al.  Different Methods of Long-Term Electric Load Demand Forecasting a Comprehensive Review , 2011 .

[32]  Bijaya Ketan Panigrahi,et al.  Harmony search algorithm for transmission network expansion planning , 2010 .

[33]  M. A. Abido,et al.  A particle-swarm-based approach of power system stability enhancement with unified power flow controller , 2007 .

[34]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[35]  Weerakorn Ongsakul,et al.  Improved Pseudo-Gradient Search Particle Swarm Optimization for Optimal Power Flow Problem , 2016 .

[36]  Fushuan Wen,et al.  A chance constrained programming approach to transmission system expansion planning , 2005 .

[37]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[38]  Mohamed E. El-Hawary,et al.  A Survey of Particle Swarm Optimization Applications in Electric Power Systems , 2009, IEEE Transactions on Evolutionary Computation.

[39]  Ram Avtar Jaswal,et al.  OPTIMIZATION TECHNIQUES FOR TRANSMISSION EXPANSION PLANNING , 2014 .

[40]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[41]  Arash Ghanbari,et al.  A Cooperative Ant Colony Optimization-Genetic Algorithm approach for construction of energy demand forecasting knowledge-based expert systems , 2013, Knowl. Based Syst..

[42]  E. S. Ali,et al.  A hybrid Particle Swarm Optimization and Bacterial Foraging for optimal Power System Stabilizers design , 2013 .

[43]  M. A. Abido,et al.  Modeling and control of an interconnected combined cycle gas turbine using fuzzy and ANFIS controllers , 2018 .

[44]  S. A. Soliman,et al.  Long-term/mid-term electric load forecasting based on short-term correlation and annual growth , 2005 .

[45]  Bayram Akdemir,et al.  Long-term load forecasting based on adaptive neural fuzzy inference system using real energy data , 2012 .

[46]  Achille Messac,et al.  A multi-objective mixed-discrete particle swarm optimization with multi-domain diversity preservation , 2015, Structural and Multidisciplinary Optimization.

[47]  Celso Tadao Miasaki,et al.  Transmission Network Expansion Planning Considering Phase-Shifter Transformers , 2012, J. Electr. Comput. Eng..