Summary
In this paper, a novel method is presented to study the impact of the most congested line on Locational Marginal Price (LMP), and Transmission Congestion Cost (TCC). In most congested condition, the abrupt difference in LMP between two nodes result in enormous TCC and create a huge loss for participants. The method developed in this paper is to find the most congested line on the basis of TCC value, and its sensitivity on the overall network is thoroughly studied. The LMPs computed at each node help in grouping the nodes or buses into two parts, namely, zone 1 or congested zone and zone 2 or non‐congested zone. This method helps market operators to analyze the impact of the most congested line. In addition, the method is handled by obtaining the exact location and sizing of Distributed Generation (DG). Moreover, to formulate the problem accurately, the proposed method is based on AC optimal power flow (ACOPF), which includes network losses in contrast with the lossless DCOPF. The MATLAB interior point method is used to solve the proposed method, and it is tested on IEEE Reliability Test System (IEEE‐RTS) 24 bus and IEEE 30 bus system network in different operating conditions. The results thus obtained shows the betterment of the proposed method.
Most of the conventional spectrum sensing algorithms are based on cognition of transmitting signal features. The general approach for utilizing this cognition property is by bearing some information of the signal being transmitted. At the receiver end the detector obtains final decision explaining the existence of the signal in a certain specific spectrum band it utilizes for transmission. Moreover, in order to ameliorate the detection accuracy for a fixed value of false alarm probability is a dispute to maximum spectrum detection approaches. In this paper, we present a reliable optimal hybrid spectrum sensing scheme (ROHSS) based on energy detection for cognitive radio network. The proposed two stage ROHSS algorithm implements two detectors acting simultaneously corresponding to signals containing high and low signal to noise ratio. In first stage, an enhanced energy detector (EED) is used for the high Signal to noise ratio and an anti eigenvalue-based sensor detecting the signals with low signal to noise ratio. In second stage, student-teacher neural network (STNN) based sensor takes advantage of approximated eigen values of the transmitted signal and obtains a result regarding the existence of the signal. The main objective of the developed ROHSS algorithm is to detect the available frequency slots and allocated them to the cognitive users immediately in order to minimize the delay because of the efficient performance of the decision fusion method. The proposed ROHSS algorithm is analyzed and the performance is compared with the available sensing algorithms.
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