Thermal generating unit commitment using an extended mean field annealing neural network

An extended mean field annealing neural network approach is used for short-term thermal unit commitment. In power systems, the major goal of the generating unit commitment is to minimise the total fuel cost of the thermal units subject to some practical constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. The annealing neural network combines good solution quality for simulated annealing with rapid convergence for artificial neural network. The extended mean field annealing neural network is used to find short-term thermal unit commitment. By doing so, it can help in finding the optimum solution rapidly and efficiently. The effectiveness of the proposed approach is demonstrated by thermal unit commitment of the Taiwan power system. It is concluded from the results that the proposed approach is very effective in reaching proper unit commitment