RBF Neural Network for Estimating Locational Marginal Prices in Deregulated Electricity Market

In the deregulated Power Market scenario, due to liberalized market structure and non-discriminatory open transmission access, the issue of congestion management and hence optimum use of transmission capacity, has become more crucial issue.. The pricing mechanism based on capacity allocation principle, to determine Locational Marginal Prices (LMP) can be proved to be substantial, about efficient utilization of transmission grid and available generation capacity. Regarding Congestion Management the Optimal pricing strategy breaks the Nodal pricing into two components; one is Locational Marginal Price (LMP) and second is Nodal Congestion Price (NCP). Both of these are significant for market participants as system security parameter. In the emerging deregulated environment, the Artificial Intelligent techniques like ANN provide instant and accurate LMPs, which boost up the motive of spot power market. This paper presents Radial Basis Function Neural Network (RBFNN) for estimating LMPs. Since the test results are very accurate and awfully fast, these instant results can be directly floated to OASIS (open access same time information system) web site. The Market Participants willing to make transactions can access this information for any location of the market. The effectiveness of the proposed ANN has been established by comparing the testing results with those obtained with conventional Interior-Point OPF based method for a 6-bus test system having three generating units