Enhanced Merit Order and Augmented Lagrange Hopfield Network for Unit Commitment
نویسندگان
چکیده
This paper presents an enhanced merit order (EMO) and augmented Lagrange Hopfield network (ALHN) for unit commitment (UC). The EMO method is a merit order based heuristic search for unit scheduling and ALHN is a continuous Hopfield network based on augmented Lagrange relaxation for economic dispatch problem. First, generating units are sorted in ascending average production cost and committed to fulfill load demand and spinning reserve requirements neglecting minimum up and down time constraints. Then, a heuristic search based algorithm is applied to satisfy minimum up and down time constraints, and modify start up cost from cold to hot if necessary. Finally, the ALHN is used to solve economic dispatch (ED). The proposed method is tested on systems ranging from 10 to 100 generating units and compared to augmented Hopfield network (AHN), conventional Lagrangian relaxation (LR), genetic algorithm (GA), evolutionary programming (EP), Lagrangian relaxation and genetic algorithm (LRGA), memetic algorithm (MA), Lagrangian relaxation and memetic algorithm (LRMA), genetic algorithm based on unit characteristic classification (GAUC), genetic algorithm based on unit characteristic classification and unit integration technique (GAUCUI), and extended priority list (EPL). The total production costs from the proposed method are less expensive and the computational times are vastly faster than the others, especially for the large number of generating units. For large-scale implementation, it is also tested on systems up to 1000 generating units with time horizon up to 168 hours. Test results indicate that the proposed method is very attractive and favorable due to substantial production costs savings and fast computational times.
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