Congestion management in a zonal market by a neural network approach

Politecnico di Milano, Milano, Italy
European Transactions on Electrical Power (Impact Factor: 0.89). 05/2009; 19(4):569 - 584. DOI: 10.1002/etep.325


With the introduction of the Power Exchange, one of the most critical issues to be faced by a Transmission System Operator (TSO) is to take into account the transmission constraints in a simplified market model. The zonal approach represents a suitable solution, since its mechanism can be easily understood by all the operators; on the other side, it requires to establish a priori the relevant transmission constraints. However, in a meshed network, this solution results in some problems in the management of the system, mainly because the Transmission Capability (TTC) value is deeply influenced by both demand and generation patterns. In order to face this problem, coupling the clearing process with an on-line TTC evaluation tool would represent the best solution, allowing the full exploitation of the transmission facilities. Since all the methods already proposed in the technical literature are not suitable for on-line applications due to their huge computation time, a new approach is proposed. An Artificial Neural Network (ANN) is used to estimate the TTC in real time: once the proposed model has been trained, it is adopted for a real time update of the TTC between two market areas, with respect to the actual market results, in order to increase the market efficiency and to reduce the associated congestion costs. Copyright © 2009 John Wiley & Sons, Ltd.

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    ABSTRACT: Congestion management is traditionally done using deterministic values of power system parameters. After relieving congestion using conventional methods, the network may be operated with a low voltage or transient stability margin because of hitting security limits. In this paper, a stochastic multi-objective framework considering power system uncertainties is proposed to enhance voltage and transient stabilities after congestion management. The uncertainty sources incorporate contingencies of generating units and branches. The proposed multi-objective framework simultaneously optimizes competing objective functions of congestion management cost, voltage security, and dynamic security under a stochastic framework. The proposed framework not only captures a more uncertainty spectrum of the power system compared with deterministic methods, but also establishes a sufficient level of voltage and dynamic security after congestion management. Results of testing the proposed framework and previous congestion management methods on the New-England power system, discussed in detail, elaborate the efficiency of the proposed framework. Copyright © 2010 John Wiley & Sons, Ltd.
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