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Day-ahead AC-DC OPF-based nodal price prediction by artificial neural network (ANN) in a restructured electricity market
In the last few years, electricity markets have significantly restructured in both developed and developing countries. Accurate prediction of dayahead 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 decisionmaking 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 technoeconomical advantages. This study aims at 1) motivation and relevance of present study; 2) presenting ACDC OPF nodal pricing and formulating ANNbased peak dayahead nodal price prediction using multilayer feedforward neural network with a backpropagation algorithm; 3) the numerical results of IEEE 30bus 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, ACDC OPF, nodal price prediction, artificial neural networks, ANNs, developing countries, India
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