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This paper describes a novel approach that combines an abductive reasoning network (ARN) and a technique for order preference by similarity to an ideal solution (TOPSIS) decision approach to achieve real-time economic emission power dispatch and the best compromise solution. The objectives of fuel cost and the environmental impact of emission are simultaneously considered in this paper. The proposed ARN handles complicated relationships between the load demands (input) and the generation power of each unit (output) using a hierarchical network with several layers of function nodes of simple low-order polynomials to make the computed outputs fit the historical data. Once the ARN is constructed, the desired outputs can be produced as soon as the inputs are given. According to the set of noninferior solutions for a specific load level, the TOPSIS approach is used to provide operators with the best compromise solution. The effectiveness of the proposed approach has been demonstrated by the IEEE 30-bus 6-generator and the practical Taipower 388-bus 27-generator test systems. The test results reveal that the proposed ARN outperforms the artificial neural networks (ANNs) method, in both developing the model and estimating the outputs of the generating units according to the input load demands.