Multiyear transmission expansion planning using discrete evolutionary particle swarm optimization

The objective of Transmission Expansion Planning (TEP) is to obtain a plan to expand or reinforce a transmission network that minimizes construction and operational costs while satisfying the requirement of delivering electricity safely and reliably to load centres along the planning horizon. This definition is quite simple, but the complexity of the problem and the impact on society transforms TEP on a challenging issue. The objective of this paper is the introduction of a new discrete approach to solve dynamic TEP, based on an improved version of the Evolutionary Particle Swarm Optimization (EPSO) meta-heuristic algorithm. The paper includes sections describing the Discrete EPSO (DEPSO), an enhanced approach of EPSO, the mathematical formulation of the problem, including the objective function and constraints, and the application of DEPSO to this problem. Finally, the use of the developed approach is illustrated using Case Studies based on the Garver network and on the IEEE 24 bus / 38 branch test system.

[1]  Haozhong Cheng,et al.  Flexible method for power network planning using the unascertained number , 2004 .

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

[3]  R. C. G. Teive,et al.  A cooperative expert system for transmission expansion planning of electrical power systems , 1998 .

[4]  M. F. Pereira,et al.  A Decomposition Approach To Automated Generation/Transmission Expansion Planning , 1985, IEEE Transactions on Power Apparatus and Systems.

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

[6]  M. El-Sharkawi,et al.  Introduction to Evolutionary Computation , 2008 .

[7]  L. L. Garver,et al.  Transmission Network Estimation Using Linear Programming , 1970 .

[8]  K. Ponnambalam,et al.  Transmission Expansion under Risk using Stochastic Programming , 2006, 2006 International Conference on Probabilistic Methods Applied to Power Systems.

[9]  Ruben Romero,et al.  Transmission system expansion planning by simulated annealing , 1995 .

[10]  S. Binato,et al.  A Greedy Randomized Adaptive Search Procedure for Transmission Expansion Planning , 2001 .

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

[12]  J. Saraiva,et al.  A multiyear dynamic approach for transmission expansion planning and long-term marginal costs computation , 2005, IEEE Transactions on Power Systems.

[13]  Felix F. Wu,et al.  Transmission Expansion Planning From Past to Future , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[14]  Vladimiro Miranda,et al.  EPSO-evolutionary particle swarm optimization, a new algorithm with applications in power systems , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[15]  João Tomé Saraiva,et al.  LONG TERM MARGINAL PRICES - SOLVING THE REVENUE RECONCILIATION PROBLEM OF TRANSMISSION PROVIDERS , 2005 .

[16]  Chilukuri K. Mohan,et al.  Multi-phase Discrete Particle Swarm Optimization , 2002, JCIS.

[17]  Paul R. Drake,et al.  System network planning expansion using mathematical programming, genetic algorithms and tabu search , 2008 .

[18]  R. Romero,et al.  Tabu search algorithm for network synthesis , 2000 .

[19]  G. Latorre,et al.  Classification of publications and models on transmission expansion planning , 2003 .

[20]  S. Dambhare,et al.  An Expert System Approach for Multi-Year Short-Term Transmission System Expansion Planning: An Indian Experience , 2008, IEEE Transactions on Power Systems.

[21]  R.A. Gallego,et al.  Multistage and coordinated planning of the expansion of transmission systems , 2004, IEEE Transactions on Power Systems.