Transmission expansion planning by enhanced differential evolution

The restructuring and deregulation has exposed the transmission planner to new objectives and uncertainties. As a result, new criteria and approaches are needed for transmission expansion planning (TEP) in deregulated electricity markets. This paper proposes a new market-based approach for TEP. An enhanced differential evolution (EDE) model is proposed for the solution of this new market-based TEP problem. The modifications of EDE in comparison to the simple differential evolution method are: 1) the scaling factor F is varied randomly within some range, 2) an auxiliary set is employed to enhance the diversity of the population, 3) the newly generated trial vector is compared with the nearest parent, and 4) the simple feasibility rule is used to treat the constraints. Results from the application of the proposed method on the IEEE 30 bus test system demonstrate the feasibility and practicality of the proposed EDE for the solution of TEP problem.

[1]  Pavlos S. Georgilakis,et al.  Genetic algorithm solution to the market-based transmission expansion planning problem , 2008 .

[2]  J. K. Korczynski,et al.  Application of transmission reliability assessment in probabilistic planning of BC Hydro Vancouver South Metro system , 1995 .

[3]  Xin Yao,et al.  Stochastic ranking for constrained evolutionary optimization , 2000, IEEE Trans. Evol. Comput..

[4]  Felix F. Wu,et al.  A kernel-oriented algorithm for transmission expansion planning , 2000 .

[5]  M. Shahidehpour,et al.  Market-based transmission expansion planning , 2004, IEEE Transactions on Power Systems.

[6]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

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

[8]  D. Kirschen,et al.  Fundamentals of power system economics , 1991 .

[9]  Goran Strbac,et al.  Transmission network reinforcement versus FACTS: an economic assessment , 1999, Proceedings of the 21st International Conference on Power Industry Computer Applications. Connecting Utilities. PICA 99. To the Millennium and Beyond (Cat. No.99CH36351).

[10]  Ivan Zelinka,et al.  MIXED INTEGER-DISCRETE-CONTINUOUS OPTIMIZATION BY DIFFERENTIAL EVOLUTION Part 1: the optimization method , 2004 .

[11]  D. J. Slump,et al.  Impact of deregulation on power delivery planning , 1999, 1999 IEEE Transmission and Distribution Conference (Cat. No. 99CH36333).

[12]  Pavlos S. Georgilakis,et al.  Technical challenges associated with the integration of wind power into power systems , 2008 .

[13]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[14]  Goran Strbac,et al.  Fundamentals of Power System Economics: Kirschen/Power System Economics , 2005 .

[15]  Pavlos S. Georgilakis,et al.  Market-based transmission expansion planning by improved differential evolution , 2010 .

[16]  René Thomsen,et al.  Multimodal optimization using crowding-based differential evolution , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[17]  Hongbo Sun,et al.  A multiple-objective optimization model of transmission enhancement planning for independent transmission company (ITC) , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[18]  Mohammad Shahidehpour,et al.  Transmission planning approaches in restructured power systems , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[19]  B. Cory,et al.  Optimal pricing of transmission and distribution services in electricity supply , 1995 .