Extracting the Contingency Curve of Fuel Cost-Pollution the Issue of Environmental Economic Burden Distribution

  • MH Karim KOSHTEH College of Economics, Kharazmi University, Tehran, Iran

Abstract

In recent decades, SO2, CO2 and NOx emissions from fossil fuels have caused significant social costs. Considering the cost of environmental pollution combined with the cost of exploitation in the distribution of loads in many studies shows the important role of social spending in economic estimates. Minimizing the cost of exploitation along with the cost of emissions in order to meet the demand under the applicable constraints of the power systems known as the modeling of the problem of economic- environmental distribution is load EED (Environmental Economic Dispatch). In this research, the EED problem modeling is optimized by Lagrange’s analytical method based on weight coefficients for the six- generation system. The relationship between EER (Emission Reduction Rate) emission reduction index and fuel cost reduction coefficient is determined and, finally, the extraction of the fuel- pollutant- cost indifference curve is described. Sensitivity analysis has been carried out to achieve the final point of operation of the system, the fuel cost reduction coefficient and the cost- dependency curve of fuel- polluting units. The results are based on these principles:



  • The higher the weight loss factor is the greater the cost of fuel. The cost of operating the system is lower and the cost of emissions is higher. In this case, the problem of the economic distribution of burden or ED (Economic Dispatch) and the minimization of the cost of fuel is the object of the problem.

  • The lower the weight loss factor for fuel costs. The cost of operating the system is higher and the cost of pollution is reduced. In this case, minimizing the cost of emissions is the goal of the problem.

  • The combination of modeling results with indifference curves makes the final point of the system operator clear as the optimal answer to the problem. Utilizing the point of the final operation of the system as the answer to the problem, considering the importance of each goal and how cost and pollution changes are compared to each other.

References

[1] Lamont, J.W., and Obessis, E.V. 1995. Emission dispatch models and algorithms for the 1990s. Power Systems, IEEE Transactions on, 10(2): 941-947.
[2] Talaq, J.H., El-Hawary, F., and El-Hawary, M.E. 1994. A summary of environmental/economic dispatch algorithms. Power Systems, IEEE Transactions on, 9(3): 1508-1516.
[3] Brodsky, S.F., and Hahn, R.W. 1986. Assessing the influence of power pools on emission constrained economic dispatch. Power Systems, IEEE Transactions on, 1(1): 57-62.
[4] Granelli, G.P., Montagna, M., Pasini, G.L., and Marannino, P. 1992. Emission constrained dynamic dispatch. Electric Power Systems Research, 24(1): 55-64.
[5] Zahavi, J., and Eisenberg, L. 1975. Economic-Enviromental Power Dispatch.Systems, Man and Cybernetics, IEEE Transactions on, (5): 485-489.
[6] Heslin, J.S., and Hobbs, B.F. 1989. A multiobjective production costing model for analyzing emissions dispatching and fuel switching [of power stations]. Power Systems, IEEE Transactions on, 3(4): 836-.248.
[7] Palanichamy, C., and Babu, N.S. 2008. Analytical solution for combined economic and emissions dispatch. Electric Power Systems Research, 7(87): 1129-.7311.
[8] Dhillon, J., Parti, S.C., and Kothari, D.P. 1993. Stochastic economic emission load dispatch. Electric Power Systems Research, 3(62): 179-.681.
[9] El-Keib, A.A., Ma, H., and Hart, J.L. 1994. Environmentally constrained economic dispatch using the Lagrangian relaxation method. Power Systems, IEEE Transactions on, 9(4): 1723 – 1729.
[10] Chen, S.D., and Chen, J.F. 2003. A direct Newton–Raphson economic emission dispatch. International journal of electrical power & energy systems, 25(5): 411-417.
[11] Yalcinoz, T., and Altun, H. 2002. Environmentally constrained economic dispatch via a genetic algorithm with arithmetic crossover. InAfricon Conference in Africa, 2002. IEEE AFRICON. 6th (Vol. 2, pp. 923928). IEEE.
[12] Lu, S., Sun, C., and Lu, Z. 2010. An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling. Energy Conversion and Management, 3(15): 561-.175.
[13] El Ela, A.A., Abido, M.A., and Spea, S.R. 2010. Differential evolution algorithm for emission constrained economic power dispatch problem. Electric Power Systems Research, 80(10): 1286-.2921.
[14] Meng, K., Wang, H.G., Dong, Z., and Wong, K.P. 2010. Quantum-inspired particle swarm optimization for valve-point economic load dispatch. Power Systems, IEEE Transactions on, 1(52): 215-.222.
[15] Rao, C.K., and Yesuratnam, G. 2013. Emission Constrained optimal power flow by Big-Bang and Big-Crunch optimization. J. Electrical Systems, 2(9): 256-.662.
[16] Kumar, A.I.S., Dhanushkodi, K., Kumar, J.J., and Paul, C.K.C. 2003. Particle swarm optimization solution to emission and economic dispatch problem. In TENCON 2003. Conference on Convergent Technologies for the Asia-Pacific Region (Vol. 1, pp. 435-934). IEEE.
[17] Abido, M.A. 2003. Environmental/economic power dispatch using multiobjective evolutionary algorithms. Power Systems, IEEE Transactions on, 18(4): 1529 - 1537.
[18] Balamurugan, R., and Subramanian, S. 2007. A simplified recursive approach to combined economic emission dispatch. Electric Power Components and Systems, 36(1): 17-27.
[19] Muralidharan, S., Srikrishna, K., and Subramanian, S. 2006. Emission constrained economic dispatch-A new recursive approach. Electric Power Components and Systems, 34(3): 343-353.
Published
2018-09-04
How to Cite
KOSHTEH, MH Karim. Extracting the Contingency Curve of Fuel Cost-Pollution the Issue of Environmental Economic Burden Distribution. Journal of Advanced Research in Law and Economics, [S.l.], v. 8, n. 8, p. 2454-2463, sep. 2018. ISSN 2068-696X. Available at: <https://journals.aserspublishing.eu/jarle/article/view/2208>. Date accessed: 26 apr. 2024. doi: https://doi.org/10.14505//jarle.v8.8(30).16.