Abstract
Due to the environmental concerns that arise from the emissions produced by fossil-fueled electric power plants, the classical economic dispatch, which operates electric power systems so as to minimize only the total fuel cost, can no longer be considered alone. Thus, by environmental dispatch, emissions can be reduced by dispatch of power generation to minimize emissions. The environmental/economic dispatch problem has been most commonly solved using a deterministic approach. However, power generated, system loads, fuel cost and emission coefficients are subjected to inaccuracies and uncertainties in real-world situations. In this paper, the problem is tackled using both deterministic and stochastic approaches of different complexities. The Nondominated Sorting Genetic Algorithm – II (NSGA-II), an elitist multi-objective evolutionary algorithm capable of finding multiple Pareto-optimal solutions with good diversity in one single run is used for solving the environmental/economic dispatch problem. Simulation results are presented for the standard IEEE 30-bus system.
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References
Parti, S.C., Kothari, D.P., Gupta, P.V.: Economic Thermal Power Dispatch. Institution of Engineers (India) Journal-EL 64, 126–132 (1983)
Gent, M.R., Lamont, J.W.: Minimum-Emission Dispatch. IEEE Transactions on Power Apparatus and Systems PAS-90(6), 2650–2660 (1971)
Zahavi, J., Eisenberg, L.: Economic-Environmental Power Dispatch. IEEE Transactions on Systems, Man, and Cybernetics SMC-5(5), 485–489 (1975)
Nanda, J., Kothari, D.P., Lingamurthy, K.S.: Economic-Emission Load Dispatch through Goal Programming Techniques. IEEE Transactions on Energy Conversion 3(1), 26–32 (1988)
Dhillon, J.S., Parti, S.C., Kothari, D.P.: Multiobjective Optimal Thermal Power Dispatch. Electrical Power and Energy Systems 16(6), 383–389 (1994)
Abido, M.A.: A Novel Multiobjective Evolutionary Algorithm for Environmental Economic Power Dispatch. Electric Power Systems Research 65, 71–81 (2003)
Abido, M.A.: A Niched Pareto Genetic Algorithm for Multiobjective Environmental/ Economic Dispatch. Electrical Power and Energy Systems 25(2), 97–105 (2003)
Abido, M.A.: Environmental/Economic Power Dispatch using Multiobjective Evolutionary Algorithms. IEEE Transactions on Power Systems 18(4), 1529–1537 (2003)
Ah King, R.T.F., Rughooputh, H.C.S.: Elitist Multiobjective Evolutionary Algorithm for Environmental/Economic Dispatch. IEEE Congress on Evolutionary Computation 2, 1108–1114 (2003)
Viviani, G.L., Heydt, G.T.: Stochastic Optimal Energy Dispatch. IEEE Transactions on Power Apparatus and Systems PAS-100(7), 3221–3228 (1981)
Bunn, D.W., Paschentis, S.N.: Development of a Stochastic Model for the Economic Dispatch of Electric Power. European Journal of Operational Research 27, 179–191 (1986)
Dhillon, J.S., Parti, S.C., Kothari, D.P.: Stochastic Economic Emission Load Dispatch. Electric Power Systems Research 26, 179–186 (1993)
Dhillon, J.S., Parti, S.C., Kothari, D.P.: Multiobjective Decision Making in Stochastic Economic Dispatch. Electric Machines and Power Systems 23, 289–301 (1995)
Bath, S.K., Dhillon, J.S., Kothari, D.P.: Fuzzy Satisfying Stochastic Multi-Objective Generation Scheduling by Weightage Pattern Search Methods. Electric Power Systems Research 69, 311–320 (2004)
Yokoyama, R., Bae, S.H., Morita, T., Sasaki, H.: Multiobjective Optimal Generation Dispatch based on Probability Security Criteria. IEEE Transactions on Power Systems 3(1), 317–324 (1988)
Deb, K., Pratap, A., Agrawal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Das, D.B., Patvardhan, C.: New Multi-Objective Stochastic Search Technique for Economic Load Dispatch. IEE Proceedings. C, Generation, Transmission, and Distribution 145(6), 747–752 (1998)
Haimes, Y.Y., Lasdon, L.S., Wismer, D.A.: On a Bicriterion Formulation of the Problems of Integrated System Identification and System Optimization. IEEE Transactions on Systems, Man, and Cybernetics 1(3), 296–297 (1971)
Deb, K., Chakroborty, P.: Time Scheduling of Transit Systems With Transfer Considerations Using Genetic Algorithms. Evolutionary Computation 6(1), 1–24 (1998)
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Ah King, R.T.F., Rughooputh, H.C.S., Deb, K. (2005). Evolutionary Multi-objective Environmental/Economic Dispatch: Stochastic Versus Deterministic Approaches. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_47
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DOI: https://doi.org/10.1007/978-3-540-31880-4_47
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