Stochastic Hopfield artificial neural network for electric power production costing
- Temple Univ., Philadelphia, PA (United States). Dept. of Electrical Engineering
The paper presents a stochastic Hopfield artificial neural network for unit commitment and economic power dispatch. Because of uncertainties in both the system load demand and unit availability, the unit commitment and economic power dispatch problem is stochastic. In this paper the authors model forced unit outages as independent Markov processes, and load demand as a normal Gaussian random variable. The (0,1) unit commitment-status variables and the hourly unit loading are modeled as sample functions of appropriate random processes. They are solutions of appropriately derived stochastic differential equations which describe the dynamics of a stochastic system for which the operating cost function is a stochastic Lyapunov function. Once the unit commitment and economic power dispatch have been done, the corresponding production costs are computed.
- OSTI ID:
- 163052
- Report Number(s):
- CONF-950103--
- Journal Information:
- IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 3 Vol. 10; ISSN 0885-8950; ISSN ITPSEG
- Country of Publication:
- United States
- Language:
- English
Similar Records
Economic load dispatch for piecewise quadratic cost function using Hopfield neural network
Artificial neural networks for short term electrical load forecasting