Impact of Loading Conditions on the Emission- Economic Dispatch
Environmental awareness and the recent
environmental policies have forced many electric utilities to
restructure their operational practices to account for their emission
impacts. One way to accomplish this is by reformulating the
traditional economic dispatch problem such that emission effects are
included in the mathematical model. This paper presents a Particle
Swarm Optimization (PSO) algorithm to solve the Economic-
Emission Dispatch problem (EED) which gained recent attention due
to the deregulation of the power industry and strict environmental
regulations. The problem is formulated as a multi-objective one with
two competing functions, namely economic cost and emission
functions, subject to different constraints. The inequality constraints
considered are the generating unit capacity limits while the equality
constraint is generation-demand balance. A novel equality constraint
handling mechanism is proposed in this paper. PSO algorithm is
tested on a 30-bus standard test system. Results obtained show that
PSO algorithm has a great potential in handling multi-objective
optimization problems and is capable of capturing Pareto optimal
solution set under different loading conditions.
[1] J. H. Talaq, F. El Hawary, and M. E. El Hawary, "A summary of
environmental/economic dispatch algorithms," IEEE Transactions on
Power Systems, vol. 9, no. 3, pp. 1508-1516, 1994.
[2] J.Weisman and L.E.Eckart, Modern Power Plant Engineering.
Englewood Cliffs, New Jersey: Prentice-Hall, 1985.
[3] M. A. Abido, "Environmental/economic power dispatch using
multiobjective evolutionary algorithms," IEEE Transactions on Power
Systems, vol. 18, no. 4, pp. 1529-1537, 2003.
[4] R. Yokoyama, S. H. Bae, T. Morita, and H. Sasaki, "Multiobjective
optimal generation dispatch based on probability security criteria," IEEE
Transactions on Power Systems, vol. 3, no. 1, pp. 317-324, 1988.
[5] J. Nanda, D. P. Kothari, and K. S. Lingamurthy, "Economic-emission
load dispatch through goal programming techniques," IEEE Transactions
on Energy Conversion, vol. 3, no. 1, pp. 26-32, 1988.
[6] M. R. AlRashidi and M. E. El-Hawary, "Economic Dispatch with
Environmental Considerations using Particle Swarm Optimization,"
Large Engineering Systems Conference on Power Engineering, pp. 41-
46, 2006.
[7] J. W. Lamont and E. V. Obessis, "Emission dispatch models and
algorithms for the 1990s," IEEE Transactions on Power Systems, vol.
10, no. 2, pp. 941-947, 1995.
[8] J. H. Talaq, "Computational aspects of optimal environmental operation
of electric power systems." Ph. D. thesis, Dalhousie University, 1993.
[9] J. A. Momoh, R. Adapa, and M. E. El-Hawary, "A review of selected
optimal power flow literature to 1993. I. Nonlinear and quadratic
programming approaches," IEEE Transactions on Power Systems, vol.
14, no. 1, pp. 96-104, 1999.
[10] J. A. Momoh, M. E. El-Hawary, and R. Adapa, "A review of selected
optimal power flow literature to 1993. II. Newton, linear programming
and interior point methods," IEEE Transactions on Power Systems, vol.
14, no. 1, pp. 105-111, 1999.
[11] J. Kennedy and R. Eberhart, "Particle swarm optimization," IEEE
International Conference on Neural Networks, vol. 4, pp. 1942-1948,
Perth, Australia, 1995.
[12] R. Eberhart and J. Kennedy, "A new optimizer using particle swarm
theory," Proceedings of the Sixth International Symposium on Micro
Machine and Human Science, pp. 39-43, Nagoya, Japan, 1995.
[13] H. Xiaohui, S. Yuhui, and R. Eberhart, "Recent advances in particle
swarm," Proceedings of 2004 Congress on Evolutionary Computation,
vol. 1, pp. 90-97, 2004.
[14] R. C. Eberhart and Y. Shi, "Guest Editorial Special Issue on Particle
Swarm Optimization," IEEE Transactions on Evolutionary Computation,
vol. 8, no. 3, pp. 201-203, 2004.
[15] Y. Shi and R. Eberhart, "A modified particle swarm optimizer," IEEE
World Congress on Computational Intelligence, pp. 69-73, Alaska,
USA, 1998.
[1] J. H. Talaq, F. El Hawary, and M. E. El Hawary, "A summary of
environmental/economic dispatch algorithms," IEEE Transactions on
Power Systems, vol. 9, no. 3, pp. 1508-1516, 1994.
[2] J.Weisman and L.E.Eckart, Modern Power Plant Engineering.
Englewood Cliffs, New Jersey: Prentice-Hall, 1985.
[3] M. A. Abido, "Environmental/economic power dispatch using
multiobjective evolutionary algorithms," IEEE Transactions on Power
Systems, vol. 18, no. 4, pp. 1529-1537, 2003.
[4] R. Yokoyama, S. H. Bae, T. Morita, and H. Sasaki, "Multiobjective
optimal generation dispatch based on probability security criteria," IEEE
Transactions on Power Systems, vol. 3, no. 1, pp. 317-324, 1988.
[5] J. Nanda, D. P. Kothari, and K. S. Lingamurthy, "Economic-emission
load dispatch through goal programming techniques," IEEE Transactions
on Energy Conversion, vol. 3, no. 1, pp. 26-32, 1988.
[6] M. R. AlRashidi and M. E. El-Hawary, "Economic Dispatch with
Environmental Considerations using Particle Swarm Optimization,"
Large Engineering Systems Conference on Power Engineering, pp. 41-
46, 2006.
[7] J. W. Lamont and E. V. Obessis, "Emission dispatch models and
algorithms for the 1990s," IEEE Transactions on Power Systems, vol.
10, no. 2, pp. 941-947, 1995.
[8] J. H. Talaq, "Computational aspects of optimal environmental operation
of electric power systems." Ph. D. thesis, Dalhousie University, 1993.
[9] J. A. Momoh, R. Adapa, and M. E. El-Hawary, "A review of selected
optimal power flow literature to 1993. I. Nonlinear and quadratic
programming approaches," IEEE Transactions on Power Systems, vol.
14, no. 1, pp. 96-104, 1999.
[10] J. A. Momoh, M. E. El-Hawary, and R. Adapa, "A review of selected
optimal power flow literature to 1993. II. Newton, linear programming
and interior point methods," IEEE Transactions on Power Systems, vol.
14, no. 1, pp. 105-111, 1999.
[11] J. Kennedy and R. Eberhart, "Particle swarm optimization," IEEE
International Conference on Neural Networks, vol. 4, pp. 1942-1948,
Perth, Australia, 1995.
[12] R. Eberhart and J. Kennedy, "A new optimizer using particle swarm
theory," Proceedings of the Sixth International Symposium on Micro
Machine and Human Science, pp. 39-43, Nagoya, Japan, 1995.
[13] H. Xiaohui, S. Yuhui, and R. Eberhart, "Recent advances in particle
swarm," Proceedings of 2004 Congress on Evolutionary Computation,
vol. 1, pp. 90-97, 2004.
[14] R. C. Eberhart and Y. Shi, "Guest Editorial Special Issue on Particle
Swarm Optimization," IEEE Transactions on Evolutionary Computation,
vol. 8, no. 3, pp. 201-203, 2004.
[15] Y. Shi and R. Eberhart, "A modified particle swarm optimizer," IEEE
World Congress on Computational Intelligence, pp. 69-73, Alaska,
USA, 1998.
@article{"International Journal of Electrical, Electronic and Communication Sciences:55057", author = "M. R. Alrashidi and M. E. El-Hawary", title = "Impact of Loading Conditions on the Emission- Economic Dispatch", abstract = "Environmental awareness and the recent
environmental policies have forced many electric utilities to
restructure their operational practices to account for their emission
impacts. One way to accomplish this is by reformulating the
traditional economic dispatch problem such that emission effects are
included in the mathematical model. This paper presents a Particle
Swarm Optimization (PSO) algorithm to solve the Economic-
Emission Dispatch problem (EED) which gained recent attention due
to the deregulation of the power industry and strict environmental
regulations. The problem is formulated as a multi-objective one with
two competing functions, namely economic cost and emission
functions, subject to different constraints. The inequality constraints
considered are the generating unit capacity limits while the equality
constraint is generation-demand balance. A novel equality constraint
handling mechanism is proposed in this paper. PSO algorithm is
tested on a 30-bus standard test system. Results obtained show that
PSO algorithm has a great potential in handling multi-objective
optimization problems and is capable of capturing Pareto optimal
solution set under different loading conditions.", keywords = "Economic emission dispatch, economic cost
dispatch, particle swarm, multi-objective optimization.", volume = "2", number = "3", pages = "422-4", }