Transmission expansion planning using multi-objective optimization

The electricity industry has always been interested in expanding investment in the transmission sector of the industry. As load demand increases and generation expands to meet the need, transmission expansion becomes important in order to increase social welfare by reducing total system operating cost, and to make the system more reliable. A methodology for contemporary transmission expansion planning using mixed-integer nonlinear multi-objective optimization to reduce total system operating cost (congestion alleviation) and line construction/investment cost is explored here. The mixed-integer nonlinear multi-objective optimization includes network constraints (line thermal limits, voltage limits, and generator limits). Contingency analysis is performed after the optimization. The methodology is applied to an IEEE 30-bus system, an IEEE 118-bus system, and the results are presented

[1]  R. Romero,et al.  A branch-and-bound algorithm for the multi-stage transmission expansion planning , 2005, IEEE Power Engineering Society General Meeting, 2005.

[2]  S.M. Halpin,et al.  Pricing transmission congestion to alleviate system stability constraints in bulk power planning , 2004, Thirty-Sixth Southeastern Symposium on System Theory, 2004. Proceedings of the.

[3]  Seema Singh,et al.  Improved voltage and reactive power distribution factors for outage studies , 1997 .

[4]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[5]  V.A. Levi,et al.  Integrated methodology for transmission and reactive power planning , 1995, Proceedings of Power Industry Computer Applications Conference.

[6]  R. Adapa,et al.  A value-based reliability enhancement scheme for bulk transmission system planning , 1998 .

[7]  C.A. Canizares,et al.  Stability-constrained optimal power flow and its application to pricing power system stabilizers , 2005, Proceedings of the 37th Annual North American Power Symposium, 2005..

[8]  John E. Mitchell,et al.  An improved branch and bound algorithm for mixed integer nonlinear programs , 1994, Comput. Oper. Res..

[9]  B. Gorenstin,et al.  Power system expansion planning under uncertainty , 1993 .

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

[11]  A. Papalexopoulos,et al.  Transmission congestion management in competitive electricity markets , 1998 .

[12]  R. Hackam,et al.  New Transmission Planning Model , 1989, IEEE Power Engineering Review.

[13]  Laura Bahiense,et al.  A Mixed Integer Disjunctive Model for Transmission Network Expansion , 2001 .

[14]  Brian Birge,et al.  PSOt - a particle swarm optimization toolbox for use with Matlab , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[15]  S. Manuspiya,et al.  Network congestion assessment for short-term transmission planning under deregulated environment , 2001, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[16]  Roy Billinton,et al.  A minimum cost assessment method for composite generation and transmission system expansion planning , 1993 .

[17]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[18]  Hsiao-Dong Chiang,et al.  A dynamical trajectory-based methodology for systematically computing multiple optimal solutions of general nonlinear programming problems , 2004, IEEE Transactions on Automatic Control.

[19]  H. Chiang,et al.  A systematic search method for obtaining multiple local optimal solutions of nonlinear programming problems , 1996 .

[20]  S. Salon,et al.  Optimization of Transmission Line Planning Including Security Constraints , 1989, IEEE Power Engineering Review.

[21]  S. M. Shahidehpour,et al.  Transaction analysis in deregulated power systems using game theory , 1997 .