An application of ANN in scheduling pumped-storage
- UMIST, Manchester (United Kingdom)
- Electric Power Research Inst., Beijing (China)
An artificial neural network (ANN) based optimization method in scheduling pumped storage is proposed in the paper. Short-term scheduling as well as real-time dispatch of pumped storage station is a constrained optimization problem. It becomes more complicated when coordinated with other generation resources. The computation time is often long and the operation conditions may change unpredictably. A fast and practical way is expected. ANN is used as a signal processing device, which represents mapping functions from input space to output space. Through training process, multi-layered feedforward and neural networks can be used to approximate the continuous functions with a given accuracy and real-time solution can be achieved. In this paper three layer feedforward ANN and improved BP algorithm are adopted to solve the problem of pumped-storage scheduling. A set of ANN training data are obtained by running an OPtimization Software (OPS). The paper described how to select and organize the input data and how to train the ANN. A work example is presented and a comparison with traditional method is made. It shows that a fast and accurate solution for pumped-storage scheduling can be achieved with ANN.
- OSTI ID:
- 433795
- Report Number(s):
- CONF-951136--; ISBN 0-7803-2981-3
- Country of Publication:
- United States
- Language:
- English
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