Economic dispatch in a power system considering environmental pollution using a multi-objective particle swarm optimization algorithm based on the Pareto criterion and fuzzy logic

Economic dispatch in a power system considering environmental pollution using a multi-objective particle swarm optimization algorithm based on the Pareto criterion and fuzzy logic - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

International Journal of Energy and Environmental Engineering

, Volume 8, Issue 2, pp 99–107

First Online: 12 April 2017Received: 29 September 2016Accepted: 06 April 2017DOI: 10.1007-s40095-017-0233-9

Cite this article as: Taheri, B., Aghajani, G. & Sedaghat, M. Int J Energy Environ Eng 2017 8: 99. doi:10.1007-s40095-017-0233-9

Abstract

In recent years, many studies have studied economic dispatch problem in power systems. However, most of them have not considered the environmental pollution caused by fossil fuels. In this study, the use of an evolutionary search algorithm called multi-objective particle swarm optimization algorithm is proposed to solve the economic dispatch problem in power systems while considering environmental pollution. The proposed method is validated in terms of its accuracy and convergence speed based on comparisons with the results obtained using the classic nonlinear programming method. The proposed strategy is applied to a realistic power system under various conditions. Overall, six generating units are investigated along the corresponding constraints. The results obtained reveal that costs of operation and pollution with-without power loss are reduced significantly by the proposed approach. Obtained results show a good compromise can be established between two contradicting functions of exploitation cost and pollution by optimizing them simultaneously. Values of these function without considering their loss is 46,112.09 $-h and 682.32 kg-h, respectively. And if losses are considered, these values would be 48,381.09$-h and 726.52 kg-h, respectively.

KeywordsEconomic dispatch Multi-objective optimization Multi-objective particle swarm optimization MOPSO algorithm Pareto criterion Power plant environmental pollution List of symbolsai, bi, and ciith generating unit coefficients

FPjith generating unit cost function

NNumber of generating units in operation

Piith generating unit output power

αi, βi, and γiEmissions coefficients for the ith generating unit

PlossSystem power loss

\ P {i}^{ \hbox{min} } \ and \ P {i}^{ \hbox{max} } \The minimum and maximum power levels generated by each generating unit

\ {\text{RDR}} {i} \ and \ {\text{RUR}} {i} \Ramp-down and ramp-up rates for a generating unit

XVector includes the output power of the generating units

\ \mu {i}^{k} \Membership function that shows the ith objective function’s optimality

\ f {i}^{\hbox{max} } \ and \ f {i}^{ \hbox{min} } \Upper and lower boundaries of the ith objective function

nNumber of non-dominated solutions

mNumber of objective functions

Autor: Bahman Taheri - Gholamreza Aghajani - Mahsa Sedaghat