Levenberg Marquardt algorithm neural networks
OPTIMAL PLACEMENT OF DISTRIBUTED GENERATOR IN DEREGULATED POWER SYSTEM By Saurabh Ratra, Power
Transfer of electric energy from the source of generation to the customer via the transmission and distribution networks is accompanied by losses. The majority of these losses occur on the transmission system. The energy industry is in the midst of a transformation that will affect all electricity and natural gas consumers. State legislators and utility regulators are letting consumers choose among a variety of new energy suppliers on the basis of competitive prices and products. This trend is called deregulation. The advancement in new technology like fuel cell, wind turbine, photo voltaic and new innovation in power electronics, customer demands for better power quality and reliability are encouraging the power industry to shift for distributed generations. The research work presents the methodology of social welfare maximization for optimal placement of distributed generation (DG) in an optimal power flow (OPF) based wholesale electricity market. Optimal location of DG can be obtained by making use of Locational Marginal Price (LMP) and Consumer Payment (CP). LMP gives the short run marginal cost (SRMC) of electricity. Consumer payment (CP), evaluated as a product of LMP and load at each load bus. The enhancement of performance indices in deregulated power systems such as social welfare, demand benefits and earnings, supplier’s earnings, reduction of transmission congestion and losses are compared without and with DG. Moreover, the above stated performance indices are also compared under line loading and without line loading scenarios. The proposed analytical expressions are based on an improvement to the method that is limited to DG type, which is capable of delivering real power only. The proposed methodologies are tested in a modified IEEE 14-bus test system. The results show that a significant improvement in social welfare and subsequent reduction in transmission congestion and losses is achieved for both the scenarios in deregulated environment.
Source: Central Library, GNDEC
Recent Advances In Simulated Evolution And Learning (Advances in Natural Computation)
Book (World Scientific Pub Co Inc)
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