Combined Wavelet-Based Networks and Game-Theoretical Decision Approach for Real-Time Power Dispatch

This paper proposes a novel approach combining wavelet-based networks and the game-theoretical decision approach to reach the terms of real-time power dispatch and the best compromise solution. Both fuel cost and environmental impact of NO emission are considered. The wavelet-based networks, evolved by an evolutionary computing algorithm, are composed of three-layer structures that contain the wavelet weighting and summing nodes. The parameters of translation and dilation in the wavelet nodes and the weighting factors in the weighting nodes are tuned to make the computed outputs fit the historical data. Once the networks are trained properly, the desired outputs can be produced as soon as the inputs are given. Based on the set of noninferior solutions for a certain load level, a game-theoretical approach is relied on to provide operators with the best compromise solution. The effectiveness of the proposed approach has been demonstrated by the IEEE 30-bus six-generator test system. Comparisons of learning performances are made to the existing artificial neural network method.