Metaphor-less Rao-3 and artificial neural network with parallel computing-based wheeling pricing in competitive power market

Fast and accurate wheeling pricing has emerged as an important issue in the recent competitive power market. Embedded cost-based wheeling pricing is well accepted by power market, because it is based on actual flow of power wheeled by them. It also recovers fully the fixed cost of wheeling facility installation and operation. In this article, metaphor-less Rao-3-based ACOPF, MVA-mile method and Bialek tracing has been employed to compute wheeling prices across various generators and loads. In actual power market due to continuously varying load conditions, the computation of wheeling prices is quite a time taking process. Because for computing wheeling prices, the optimal power flow (OPF) program has to be run each time for every loading condition. In this scenario, the artificial neural network (ANN) approach has been found to be very useful, to estimate wheeling prices instantly and accurately for any unseen loading scenario. Here, a number of ANNs have been developed under parallel computing environment. This article presents a metaphor-less Rao-3-based approach to project wheeling prices in the competitive power market by developing a new radial basis function neural network (RBFNN). The present work of wheeling pricing has been demonstrated and examined on IEEE 30-bus system.