Artificial Immune System Applied to the Multi-stage Transmission Expansion Planning

Transmission expansion planning (TEP) is a complex optimization task to ensure that the power system will meet the forecasted demand and the reliability criterion, along the planning horizon, while minimizing investment, operational, and interruption costs. Metaheuristic methods have demonstrated the potential to find good feasible solutions, but not necessarily optimal. These methods can provide high quality solutions, within an acceptable CPU time, even for large-scale problems. This paper presents an optimization tool based on the Artificial Immune System used to solve the TEP problem. The proposed methodology includes the search for the least cost solution, bearing in mind investments and ohmic transmission losses. The multi-stage nature of the TEP will be also taken into consideration. Case studies on a small test system and on a real sub-transmission network are presented and discussed.

[1]  Roy Billinton,et al.  Reliability issues in today's electric power utility environment , 1997 .

[2]  S. Binato,et al.  Power transmission network design by greedy randomized adaptive path relinking , 2005, IEEE Transactions on Power Systems.

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

[4]  John E. Hunt,et al.  Learning using an artificial immune system , 1996 .

[5]  Mohamed A. El-Sharkawi,et al.  Modern heuristic optimization techniques :: theory and applications to power systems , 2008 .

[6]  Jonathan Timmis,et al.  On Diversity and Artificial Immune Systems: Incorporating a Diversity Operator into aiNet , 2005, WIRN/NAIS.

[7]  Mohamed A. El-Sharkawi,et al.  Modern Heuristic Optimization Techniques , 2008 .

[8]  Zhao Yang Dong,et al.  Transmission planning in a deregulated environment , 2006 .

[9]  Kit Po Wong,et al.  A Differential Evolution Based Method for Power System Planning , 2006, 2006 IEEE International Conference on Evolutionary Computation.

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

[11]  Armando M. Leite da Silva,et al.  A Gradient-Based Artificial Immune System Applied to Optimal Power Flow Problems , 2007, ICARIS.

[12]  A.M.L. da Silva,et al.  Evolution Strategies to Transmission Expansion Planning Considering Unreliability Costs , 2006, 2006 International Conference on Probabilistic Methods Applied to Power Systems.

[13]  Ruben Romero,et al.  Parallel simulated annealing applied to long term transmission network expansion planning , 1997 .

[14]  A.M.L. da Silva,et al.  Tabu Search Applied to Transmission Expansion Planning Considering Losses and Interruption Costs , 2008, Proceedings of the 10th International Conference on Probablistic Methods Applied to Power Systems.

[15]  Narayan S. Rau,et al.  Optimization Principles: Practical Applications to the Operation and Markets of the Electric Power Industry , 2003 .

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

[17]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

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

[19]  E. L. da Silva,et al.  A reliable approach for solving the transmission network expansion planning problem using genetic algorithms , 2001 .

[20]  D. Koval,et al.  Value-based system facility planning - a rational response to conflicting customer and regulatory demands , 2004, IEEE Power and Energy Magazine.

[21]  Armando Freitas da Rocha,et al.  Neural Nets , 1992, Lecture Notes in Computer Science.

[22]  S. Binato,et al.  A Greedy Randomized Adaptive Search Procedure for Transmission Expansion Planning , 2001, IEEE Power Engineering Review.