Integrated evolutionary neural network approach with distributed computing for congestion management

Electric supply industry is facing deregulation all over the world. Under deregulated power supply scenario, power transmission congestion has become more intensified and recurrent, as compared to conventional regulated power system. Congestion may lead to violation of voltage or transmission capacity limits, thus threatens the power system security and reliability. Also the growing congestion may lead to unanticipated divergent electricity pricing. Owing to these facts congestion management has become a crucial issue in the deregulated power system scenario. Fast and precise prediction of nodal congestion prices in real time deregulated/spot power market may enable market participants and system operators to keep pace with the congestion by taking preventive measures like transaction rescheduling, bids (both for supplying and consuming electricity) modification, regulated dispatch of electric power, etc. This paper proposes an integrated evolutionary neural network (ENN) approach to predict nodal congestion prices (NCPs) for congestion management in spot power market. Distributed computing is employed to tackle the heterogeneity of the data in the prediction of NCP values. Developed ENNs have been trained and tested under distributed computing environment, using a message passing paradigm. Proposed hybrid approach for NCP prediction is demonstrated on a 6-bus test power system with and without distributed computing. The proposed approach not only demonstrated the computing efficiency of the developed ENN model over the conventional optimal power flow method but also shows the time saving aspect of distributed computing.

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