Multilayer feedforward networks are universal approximators
Neural Networks
Approximation capabilities of multilayer feedforward networks
Neural Networks
Nonlinear neural control with power systems applications
Nonlinear neural control with power systems applications
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In the environment of ongoing deregulated power industry, traditional automatic generation control (AGC) has become a set of ancillary services traded in separate markets which are different than the energy market. The performance of AGC is mandated to meet the NERC control performance standards (CPS). The new CPS criteria allow the over-compliant power utilities to loosen control of their generating units. The competition introduced by the deregulation process provides the opportunities for the over-compliant power utilities to sell their excess regulating capabilities. In addition, load following service is often priced lower than regulation service. All these lead generation companies to optimizing the portfolio of their generating assets to achieve better economy. The optimization process involves economic allocation of generation over a consecutive set of time intervals, which requires the load profile to be predicted for the dispatch period of minute level. This paper addresses the importance of very short term load prediction in this context, and proposes a new approach to make load predictions. Procedures involved in this approach are presented. Case studies are presented to demonstrate the effectiveness of the proposed approach.