Title: Day-ahead AC-DC OPF-based nodal price prediction by artificial neural network (ANN) in a restructured electricity market
Authors: S.B. Warkad; M.K. Khedkar; G.M. Dhole
Addresses: Electrical Engineering Department, Visvesvaraya National Institute of Technology, Nagpur-440010, India. ' Electrical Engineering Department, Visvesvaraya National Institute of Technology, Nagpur-440010, India. ' Electrical Engineering Department, Shri Sant Gajanan Maharaj College of Engineering, Shegaon, Maharashtra-444203, India
Abstract: In the last few years, electricity markets have significantly restructured in both developed and developing countries. Accurate prediction of day-ahead electricity nodal price has now become an important activity to address the price volatility in the marketplace. This will facilitate the market participants to estimate the risk and have effective decision-making in formulating bidding strategy. In developing countries, transmission congestion and investment problems have reduced the consumer benefits. Recent trend is to incorporate high voltage direct current (HVDC) transmission in the AC transmission system to gain its techno-economical advantages. This study aims at 1) motivation and relevance of present study; 2) presenting AC-DC OPF nodal pricing and formulating ANN-based peak day-ahead nodal price prediction using multilayer feed-forward neural network with a back-propagation algorithm; 3) the numerical results of IEEE 30-bus system and a real electricity market of India to demonstrate the rationality and feasibility of the proposed methodology.
Keywords: restructured electricity markets; optimal power flow; AC-DC OPF; nodal price prediction; artificial neural networks; ANNs; developing countries; India.
International Journal of Power and Energy Conversion, 2012 Vol.3 No.1/2, pp.54 - 76
Available online: 17 Dec 2011Full-text access for editors Access for subscribers Purchase this article Comment on this article