Efficient use of generator resources in emerging electricity markets

In the emerging electricity market, generators sell ancillary services as well as energy to the system operator. Pricing of ancillary services makes the scheduling of units more difficult due to the resulting unit cost curves. The new climate will encourage generators to exploit the flexibility of their units in terms of operating modes. This paper uses the augmented Hopfield neural network to deal with ancillary service prices, specifically reserve and ramping prices, submitted by generators and to schedule units with a choice of operating modes.